Inside the Internet of Things (IoT)
A primer on the technologies building the IoT Inside the Internet of Things (IoT)
About the authors
Jonathan Holdowsky Jonathan Holdowsky is a senior manager with Deloitte Services LP and part of Deloitte’s Eminence Center of Excellence. In this role, he has managed a wide array of thought leadership initiatives on issues of strategic importance to clients within consumer and manufacturing sectors.
Monika Mahto Monika Mahto is a senior analyst with Deloitte Services India Pvt. Ltd and part of Deloitte’s Eminence Center of Excellence. Over the last seven years, she has been involved in various strategic research assignments for clients in the consumer and industrial products industry.
Michael E. Raynor Michael E. Raynor is a director with Deloitte Services LP and the director of the Center for Integrated Research (CIR). In collaboration with a broad cross-section of Deloitte professionals from many different industries, the CIR designs, executes, and supports research into some of the most important issues facing companies today.
Mark Cotteleer Mark Cotteleer is a research director with Deloitte Services LP, affiliated with Deloitte’s Center for Integrated Research. His research focuses on operational and financial performance improvement, in particular, through the application of advanced technology.
Acknowledgements
The authors would like to thank Sadashiva S.R. (Deloitte Services India Pvt. Ltd.), Dhaval Modi (Deloitte Consulting India Pvt. Ltd.), and Joe Mariani (Deloitte Services LP) for their research con- tributions; Gaurav Kamboj and Kritarth Suri (both with Deloitte Consulting LLP) for their con- tributions to the IoT technology architectures; and Athappan Balasubramanian (Deloitte Support Services India Pvt. Ltd.) for his graphics contributions to this report.
Deloitte’s Internet of Things practice enables organizations to identify where the IoT can poten- tially create value in their industry and develop strategies to capture that value, utilizing IoT for operational benefit.
To learn more about Deloitte’s IoT practice, visit http://www2.deloitte.com/us/en/pages/technol- ogy-media-and-telecommunications/topics/the-internet-of-things.html.
Read more of our research and thought leadership on the IoT at http://dupress.com/collection/ internet-of-things. A primer on the technologies building the IoT
Contents
The Information Value Loop | 2
Sensors | 5
Networks | 10
Standards | 16
Augmented intelligence | 21
Augmented behavior | 26
The IoT technology architecture | 33
Closing thoughts | 38
Glossary | 39
Endnotes | 44
1 Inside the Internet of Things (IoT)
The Information Value Loop
F you’ve ever seen the “check engine” light Something about your car’s operation—an Icome on in your car and had the requisite action—triggered a sensor,1 which communi- repairs done in a timely way, you’ve benefited cated the data to a monitoring device. The sig- from an early-stage manifestation of what nificance of these data was determined based today is known as the Internet of Things (IoT). on aggregated information and prior analysis.
Figure 1. The Information Value Loop
Augmented ACT Sensors behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness intelligence Network
COMMUNICATE AGGREGATE
Standards VALUE DRIVERS STAGES TECHNOLOGIES
CREATE: The use of sensors to generate information about a physical event or state. COMMUNICATE: The transmission of information from one place to another. AGGREGATE: The gathering together of information created at different times or from different sources. ANALYZE: The discernment of patterns or relationships among phenomena that leads to descrip- tions, predictions, or prescriptions for action. ACT: Initiating, maintaining, or changing a physical event or state.
Source: Deloitte analysis. Graphic: Deloitte University Press | DUPress.com
2 A primer on the technologies building the IoT
The light came on, which in turn triggered a us—not thanks to any one technological trip to the garage and necessary repairs. advance or even breakthrough but, rather, due In 1991 Mark Weiser, then of Xerox to a confluence of improvements to a suite of PARC, saw beyond these simple applica- technologies that collectively have reached tions. Extrapolating trends in technology, he levels of performance that enable complete described “ubiquitous computing,” a world in systems relevant to a human-sized world. which objects of all kinds could sense, commu- As illustrated in figure 2 below, each stage nicate, analyze, and act or react to people and of the value loop is connected to the subse- other machines autonomously, in a manner quent stage by a specific set of technologies, no more intrusive or noteworthy than how we defined below. currently turn on a light or open a tap. The business implications of the IoT are One way of capturing the process implicit explored in an ongoing series of Deloitte in Weiser’s model is as an Information Value reports. These articles examine the IoT’s Loop with discrete but connected stages. An impact on strategy, customer value, analyt- action in the world allows us to create informa- ics, security, and a wide variety of specific tion about that action, which is then commu- applications. Yet just as a good chef should nicated and aggregated across time and space, have some understanding of how the stove allowing us to analyze those data in the service works, managers hoping to embed IoT- of modifying future acts. enabled capabilities in their strategies are well Although this process is generic, it is served to gain a general understanding of the perhaps increasingly relevant, for the future technologies themselves. Weiser imagined is more and more upon
Figure 2. The technologies enabling the Internet of Things Technology Definition Examples
Sensors A device that generates The cost of an accelerometer has fallen to 40 cents from $2 in 2006.2 an electronic signal from Similar trends have made other types of sensors small, inexpensive, a physical condition or and robust enough to create information from everything from fetal event heartbeats via conductive fabric in the mother’s clothing to jet engines roaring at 35,000 feet.3
Networks A mechanism for Wireless networking technologies can deliver bandwidths of 300 communicating an megabits per second (Mbps) to 1 gigabit per second (Gbps) with near- electronic signal ubiquitous coverage.4
Standards Commonly accepted Technical standards enable processing of data and allow for prohibitions or interoperability of aggregated data sets. In the near future, we could prescriptions for action see mandates from industry consortia and/or standards bodies related to technical and regulatory IoT standards.
Augmented Analytical tools that Petabyte-sized (1015 bytes, or 1,000 terabytes) databases can now be intelligence improve the ability to searched and analyzed, even when populated with unstructured (for describe, predict, and example, text or video) data sets.5 Software that learns might substitute exploit relationships for human analysis and judgment in a few situations. among phenomena
Augmented Technologies and Machine-to-machine interfaces are removing reliably fallible human behavior techniques that improve intervention into otherwise optimized processes. Insights into compliance with human cognitive biases are making prescriptions for action based on prescribed action augmented intelligence more effective and reliable.6
Source: Deloitte analysis.
3 Inside the Internet of Things (IoT)
To that end, this document serves as a technology architecture that guides the devel- technical primer on some of the technologies opment and deployment of Internet of Things that currently drive the IoT. Its structure fol- systems. Our intent, in this primer, is not to lows that of the technologies that connect the describe every conceivable aspect of the IoT or stages of the Information Value Loop: sensors, its enabling technologies but, rather, to provide networks, standards, augmented intelligence, managers an easy reference as they explore and augmented behavior. Each section in the IoT solutions and plan potential implementa- report provides an overview of the respec- tions. Our hope is that this report will help tive technology—including factors that drive demystify the underlying technologies that adoption as well as challenges that the tech- comprise the IoT value chain and explain how nology must overcome to achieve widespread these technologies collectively relate to a larger adoption. We also present an end-to-end IoT strategic framework.
4 A primer on the technologies building the IoT
Augmented ACT Sensors behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness Sensors intelligence Network
COMMUNICATE AGGREGATE
Standards
An overview Figure 3. A temperature sensor OST “things,” from automobiles to MZambonis, the human body included, have long operated “dark,” with their location, position, and functional state unknown or Nonelectrical stimulus even unknowable. The strategic significance of LCD display the IoT is born of the ever-advancing ability to break that constraint, and to create infor- mation, without human observation, in all manner of circumstances that were previously Temperature Electronic sensor circuit invisible. What allows us to create informa- Electrical signal tion from action is the use of sensors, a generic term intended to capture the concept of a Graphic: Deloitte University Press | DUPress.com sensing system comprising sensors, micro- controllers, modem chips, power sources, and or not) into another (electrical or not). For other related devices. example, a loudspeaker is a transducer because A sensor converts a non-electrical input it converts an electrical signal into a magnetic into an electrical signal that can be sent to an field and, subsequently, into acoustic waves. electronic circuit. The Institute of Electrical Different sensors capture different types of and Electronics Engineers (IEEE) provides a information. Accelerometers measure linear formal definition: acceleration, detecting whether an object is moving and in which direction,8 while gyro- An electronic device that produces scopes measure complex motion in multiple electrical, optical, or digital data derived dimensions by tracking an object’s position from a physical condition or event. Data and rotation. By combining multiple sensors, produced from sensors is then electroni- each serving different purposes, it is possible cally transformed, by another device, to build complex value loops that exploit many into information (output) that is useful different types of information. For example: in decision making done by “intelligent” devices or individuals (people).7 • Canary: A home security system that comes with a combination of tempera- The technological complement to a sen- ture, motion, light, and humidity sensors. sor is an actuator, a device that converts an Computer vision algorithms analyze pat- electrical signal into action, often by convert- terns in behaviors of people and pets, while ing the signal to nonelectrical energy, such as machine learning algorithms improve the motion. A simple example of an actuator is an accuracy of security alerts over time.9 electric motor that converts electrical energy into mechanical energy. Sensors and actuators • Thingsee: A do-it-yourself IoT device that belong to the broader category of transducers: individuals can use to combine sensors A sensor converts energy of different forms such as accelerometers, gyroscopes, and into electrical energy; a transducer is a device magnetometers with other sensors that that converts one form of energy (electrical
5 Inside the Internet of Things (IoT)
measure temperature, humidity, pressure, when subjected to the same input under and light in order to collect personally constant environmental conditions. interesting data.10 • Range: The band of input signals within Types of sensors which a sensor can perform accurately. Input signals beyond the range lead to inac- Sensors are often categorized based on curate output signals and potential damage their power sources: active versus passive. to sensors. Active sensors emit energy of their own and then sense the response of the environment • Noise: The fluctuations in the output to that energy. Radio Detection and Ranging signal resulting from the sensor or the (RADAR) is an example of active sensing: A external environment. RADAR unit emits an electromagnetic sig- nal that bounces off a physical object and is • Resolution: The smallest incremental “sensed” by the RADAR system. Passive sen- change in the input signal that the sensor sors simply receive energy (in whatever form) requires to sense and report a change in the that is produced external to the sensing device. output signal. A standard camera is embedded with a passive sensor—it receives signals in the form of light • Selectivity: The sensor’s ability to selectively and captures them on a storage device. sense and report a signal. An example of Passive sensors require less energy, but selectivity is an oxygen sensor’s ability to active sensors can be used in a wider range sense only the O2 component despite the of environmental conditions. For example, presence of other gases. RADAR provides day and night imaging capacity undeterred by clouds and vegetation, Any of these factors can impact the reliabil- while cameras require light provided by an ity of the data received and therefore the value external source.11 of the data itself. Figure 4 provides an illustrative list of 13 types of sensors based on the functions they Factors driving adoption perform; they could be active or passive per within the IoT the description above. Of course, the choice of a specific sensor is There are three primary factors driving the primarily a function of the signal to be mea- deployment of sensor technology: price, capa- sured (for example, position versus motion bility, and size. As sensors get less expensive, sensors). There are, however, several generic “smarter,” and smaller, they can be used in a factors that determine the suitability of a sen- wider range of applications and can generate a 13 sor for a specific application. These include, but wider range of data at a lower cost. 12 are not limited to, the following: • Cheaper sensors: The price of sensors has • Accuracy: A measure of how precisely a consistently fallen over the past several sensor reports the signal. For example, years as shown in figure 5, and these price when the water content is 52 percent, a sen- declines are expected to continue into the 14 sor that reports 52.1 percent is more accu- future. For example, the average cost of an rate than one that reports it as 51.5 percent. accelerometer now stands at 40 cents, com- pared to $2 in 2006.15 Sensors vary widely • Repeatability: A sensor’s performance in in price, but many are now cheap enough to consistently reporting the same response support broad business applications.
6 A primer on the technologies building the IoT
Figure 4. Types of sensors with representative examples Sensor types Sensor description Examples
Position A position sensor measures the position of an object; the position measurement Potentiometer, can be either in absolute terms (absolute position sensor) or in relative terms inclinometer, proximity (displacement sensor). Position sensors can be linear, angular, or multi-axis. sensor
Occupancy Occupancy sensors detect the presence of people and animals in a surveillance Electric eye, RADAR and motion area, while motion sensors detect movement of people and objects. The difference between the two is that occupancy sensors will generate a signal even when a person is stationary, while a motion sensor will not.
Velocity and Velocity (speed of motion) sensors may be linear or angular, indicating how fast Accelerometer, gyroscope acceleration an object moves along a straight line or how fast it rotates. Acceleration sensors measure changes in velocity.
Force Force sensors detect whether a physical force is applied and whether the magnitude Force gauge, viscometer, of force is beyond a threshold. tactile sensor (touch sensor)
Pressure Pressure sensors are related to force sensors and measure the force applied by Barometer, bourdon liquids or gases. Pressure is measured in terms of force per unit area. gauge, piezometer
Flow Flow sensors detect the rate of fluid flow. They measure the volume (mass flow) or Anemometer, mass flow rate (flow velocity) of fluid that has passed through a system in a given period of sensor, water meter time.
Acoustic Acoustic sensors measure sound levels and convert that information into digital or Microphone, geophone, analog data signals. hydrophone
Humidity Humidity sensors detect humidity (amount of water vapor) in the air or a mass. Hygrometer, humistor, soil Humidity levels can be measured in various ways: absolute humidity, relative moisture sensor humidity, mass ratio, and so on.
Light Light sensors detect the presence of light (visible or invisible). Infrared sensor, photodetector, flame detector
Radiation Radiation sensors detect radiations in the environment. Radiation can be sensed by Geiger–Müller counter, scintillating or ionization detection. scintillator, neutron detector
Temperature Temperature sensors measure the amount of heat or cold that is present in a Thermometer, calorimeter, system. They can be broadly of two types: contact and non-contact. Contact temperature gauge temperature sensors need to be in physical contact with the object being sensed. Non-contact sensors do not need physical contact, as they measure temperature through convection and radiation.
Chemical Chemical sensors measure the concentration of chemicals in a system. When Breathalyzer, olfactometer, subjected to a mix of chemicals, chemical sensors are typically selective for a target smoke detector type of chemical (for example, a CO2 sensor senses only carbon dioxide).
Biosensors Biosensors detect various biological elements such as organisms, tissues, cells, Blood glucose biosensor, enzymes, antibodies, and nucleic acids. pulse oximetry, electrocardiograph
Sources: Jacob Fraden, Handbook of Modern Sensors: Physics, Designs, and Applications, fourth edition (Springer: April 2010); Goran Rakocevic, “Overview of sensors for wireless sensor networks,” Internet Journals, 2004.
7 Inside the Internet of Things (IoT)
• Smarter sensors: As discussed earlier, a systems (MEMS) sensors—small devices sensor does not function by itself—it is that combine digital electronics and a part of a larger system that comprises mechanical components—have the poten- microprocessors, modem chips, power tial to drive wider IoT applications.16 The sources, and other related devices. Over the average number of sensors on a smartphone last two decades, microprocessors’ com- has increased from three (accelerometer, putational power has improved, doubling proximity, ambient light) in 2007 to at least every three years (see figure 6). ten (including advanced sensors such as fingerprint- and gesture-based sensors) in • Smaller sensors: There has been a rapid 2014. Similarly, biosensors that can be worn growth in the use of smaller sensors that and even ingested present new opportuni- can be embedded in smartphones and ties for the health care industry. wearables. Micro-electro-mechanical
Figure 5. Sensors prices on the decline over the last 25 years
$25 $22.00 $20
$15
$10
$5 $1.40 $0 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Source: Rob Lineback, IC Insights Inc. “The market for next-generation microsystems: More than MEMS!,” http://itac.ca/up- loads/events/execforum2010/rob_lineback_10-6-10-2.ppt, June 10, 2010, accessed January 28, 2015; Lee Simpson and Robert Lamb, IoT: Looking at sensors, Jeffries Equity Research, February 20, 2014, p. 4.
Note: Prices shown above are average selling prices for image sensors and accelerometers. Graphic: Deloitte University Press | DUPress.com
Figure 6. Computing speed continously increasing 100,000 28751 10,000
1,000
100 (million Hz)
10 Microprocessor clock speed Microprocessor 0 29 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Note: Microprocessor clock speeds are plotted on a logarithmic scale.
Source: E. R. Berndt, E. R. Dulberger, and N. J. Rappaport, “Price and quality of desktop and mobile personal computers: A quarter century of history,” July 17, 2000; ITRS, 2002 Update, On-chip local clock in table 4c: Performance and package chips: Frequency on-chip wiring levels—near-term years, p. 167; Deloitte estimates. Graphic: Deloitte University Press | DUPress.com
8 A primer on the technologies building the IoT
Challenges and given the unreliability associated with 21 potential solutions the supply of alternative power.
Even in cases where sensors are sufficiently • Security of sensors: Executives consider- small, smart, and inexpensive, challenges ing IoT deployments often cite security as a remain. Among them are power consumption, key concern.22 Tackling the problem at the data security, and interoperability. source may be a logical approach. Complex cryptographic algorithms might ensure data • Power consumption: Sensors are pow- integrity, though sensors’ relatively low pro- ered either through in-line connections cessing power, the low memory available to or batteries.17 In-line power sources are them, and concerns about power consump- constant but may be impractical or expen- tion may all limit the ability to provide this sive in many instances. Batteries may security. Companies need to be mindful of represent a convenient alternative, but the constraints involved as they plan their battery life, charging, and replacement, IoT deployments.23 especially in remote areas, may represent significant issues.18 • Interoperability: Most of the sensor sys- There are two dimensions to power: tems currently in operation are proprietary –– Efficiency: Thanks to advanced silicon and are designed for specific applications. technologies, some sensors can now This leads to interoperability issues in het- stay live on batteries for over 10 years, erogeneous sensor systems related to com- thus reducing battery replacement munication, exchange, storage and security cost and efforts.19 However, improved of data, and scalability. Communication efficiency is counterbalanced by the protocols are required to facilitate commu- power needed for increased numbers of nication between heterogeneous sensor sys- sensors. Hence, systems’ overall power tems. Due to various limitations such as low consumption often does not decrease or processing power, memory capacity, and may, in fact, increase; this is an under- power availability at the sensor level, light- lying challenge, as both energy and weight communication protocols are pref- 24 financial resources are finite. erable. Constrained Application Protocol (CoAP) is an open-source protocol that – – Source: While sensors often depend on transfers data packets in a format that is batteries, energy harvesting of alterna- lighter than that of other protocols such as tive energy sources such as solar energy Hypertext Transfer Protocol (HTTP), a pro- may provide some alternatives, at a tocol familiar to many, as it appears in most minimum providing support during web addresses. While CoAP is well suited 20 the battery changing time. However, for energy-constrained sensor systems, it energy harvesters that are currently does not come with in-built security fea- available are expensive, and companies tures, and additional protocols are needed are hesitant to make that investment to secure intercommunications between (installation plus maintenance costs) sensor systems.25
9 Inside the Internet of Things (IoT)
Augmented ACT Sensors behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness Networks intelligence Network
COMMUNICATE AGGREGATE
Standards
An overview customization easier and allowing manufac- turers to differentiate their offerings. Open NFORMATION that sensors create rarely protocols allow interoperability across hetero- attains its maximum value at the time and I geneous devices, thus improving scalability.26 place of creation. The signals from sensors Internet Protocol (IP) is an open proto- often must be communicated to other locations col that provides unique addresses to various for aggregation and analysis. This typically Internet-connected devices; currently, there are involves transmitting data over a network. two versions of IP: IP version 4 (IPv4) and IP Sensors and other devices are connected version 6 (IPv6). IP was used to address com- to networks using various networking devices puters before it began to be used to address such as hubs, gateways, routers, network other devices. About 4 billion IPv4 addresses bridges, and switches, depending on the appli- out of its capacity of 6 billion addresses have cation. For example, laptops, tablets, mobile already been used. IPv6 has superior scalability phones, and other devices are often connected with approximately 3.4x1038 unique addresses to a network, such as Wi-Fi, using a network- compared to the 6 billion addresses supported ing device (in this case, a Wi-Fi router). by IPv4. Since the number of devices con- The first step in the process of transfer- nected to the Internet is estimated to be 26 ring data from one machine to another via billion as of 2015 and projected to grow to 50 a network is to uniquely identify each of the billion or more by 2020, the adoption of IPv6 machines. The IoT requires a unique name has served as a key enabler of the IoT. for each of the “things” on the network. Network protocols are a set of rules that define how computers identify each other. Broadly, Enabling network technologies network protocols can be proprietary or Network technologies are classified broadly open. Proprietary network protocols allow as wired or wireless. With the continuous identification and authorization to machines movement of users and devices, wireless with specific hardware and software, making networks provide convenience through almost continuous connectivity, while wired connec- tions are still useful for relatively more reliable, secured, and high-volume network routes.27 The choice of a network technology depends largely on the geographical range to be covered. When data have to be transferred over short distances (for example, inside a room), devices can use wireless personal area network (PAN) technologies such as Bluetooth and ZigBee as well as wired connections through technologies such as Universal Serial Bus (USB). When data have to be transferred over a relatively bigger area such as an office,
10 A primer on the technologies building the IoT
Figure 7. Broad network classes with representative examples by connection types Personal area network Local area network Wide area network (PAN) (LAN) (WAN)
Wired USB Ethernet Not applicable connections
WiMAX, weightless, cellular Wireless Bluetooth, ZigBee, Near Field Wi-Fi, WiMAX technologies such as 2G, 3G, connections Communication, Wi-Fi 4G (LTE)
Note: A few technologies can work in more than one network type depending on the range of the networking device used. For example, Wi-Fi can provide connection within a house (PAN) as well as within a building (LAN). Source: Wenyuan Xu, Introduction to computer networks, Department of Computer Science and Engineering, University of South Carolina, www.cse.sc.edu/~wyxu/416Fall09/slides/Chapter1_Info.ppt, 2009, accessed March 12, 2015. devices could use local area network (LAN) is a recent addition to the Bluetooth technol- technologies. Examples of wired LAN tech- ogy and consumes about half the power of a nologies include Ethernet and fiber optics. Bluetooth Classic device, the original ver- Wireless LAN networks include technologies sion of Bluetooth.29 The energy efficiency of such as Wi-Fi. When data are to be transferred BLTE is attributable to the shorter scanning over a wider area beyond buildings and cities, time needed for BLTE devices to detect other an internetwork called wide area network devices: 0.6 to 1.2 milliseconds (ms) compared (WAN) is set up by connecting a number to 22.5 ms for Bluetooth Classic. 30 In addi- of local area networks through routers. The tion, the efficient transfer of data during the Internet is an example of a WAN. transmitting and receiving states enables BLTE Data transfer rates and energy requirements to deliver higher energy efficiency compared are two key considerations when selecting a to Bluetooth Classic. Higher energy efficiency network technology for a given application. comes at the cost of lower data rates: BLTE Technologies such as 4G (LTE, LTE-A) and 5G supports 260 kilobits per second (Kbps) while are favorable for IoT applications, given their Bluetooth Classic supports up to 2.1 Mbps.31 high data transfer rates. Technologies such as Existing penetration, coupled with low Bluetooth Low Energy and Low Power Wi-Fi device costs, positions BLTE as a technology are well suited for energy-constrained devices. well suited for IoT applications. However, Below, we discuss select wireless net- interoperability is the persistent bottleneck work technologies that could be used for IoT here as well: BLTE is compatible with only the applications. For each of the following tech- relatively newer dual-mode Bluetooth devices nologies, we discuss bandwidth rates, recent (called dual mode because they support BLTE advances, and limitations. The technologies as well as Bluetooth Classic), not the legacy discussed below are representative, and the Bluetooth Classic devices.32 choice of an appropriate technology depends on the application at hand and the features of Wi-Fi and Low Power Wi-Fi that technology. Although Ethernet has been in use since the 1970s, Wi-Fi is a more recent wireless technol- Bluetooth and Bluetooth ogy that is widely popular and known for its Low Energy high-speed data transfer rates in personal and Introduced in 1999, Bluetooth technology local area networks. is a wireless technology known for its ability to Typically, Wi-Fi devices keep latency, or transfer data over short distances in personal delays in the transmission of data, low by area networks.28 Bluetooth Low Energy (BLTE) remaining active even when no data are being
11 Inside the Internet of Things (IoT)
transmitted. Such Wi-Fi connections are often Weightless set up with a dedicated power line or batter- Weightless is a wireless open-standard ies that need to be charged after a couple of WAN technology introduced in early 2014. hours of use. Higher-cost, lower-power Wi-Fi Weightless uses unused bandwidth originally devices “sleep” when not transmitting data intended for TV broadcast to transfer data; and need just 10 milliseconds to “wake up” based on the technical process of dynamic when called upon.33 Low Power Wi-Fi with spectrum allocation, it can travel longer dis- batteries can be used for remote sensing and tances and penetrate through walls.38 control applications. Weightless can provide data rates between 2.5 Kbps to 16 Mbps in a wireless range of up Worldwide Interoperability to five kilometers, with batteries lasting up for Microwave Access to 10 years.39 Weightless devices remain in (WiMAX) and WiMAX 2 standby mode, waking up every 15 minutes Introduced in 2001, WiMAX was devel- and staying active for 100 milliseconds to sync oped by the European Telecommunications up and act on any messages; this leads to a Standards Institute (ETSI) in cooperation with certain latency.40 Given these characteristics, IEEE. WiMAX 2 is the latest technology in the Weightless connections appear to be better- WiMAX family. WiMAX 2 offers maximum suited for delivering short messages in wide- data speed of 1 Gbps compared to 100 Mbps spread machine-to-machine communications. by WiMAX.34 In addition to higher data speeds, WiMAX Factors driving adoption 2 has better backward compatibility than WiMAX: WiMAX 2 network operators within the IoT can provide seamless service by using 3G Networks are able to transfer data at higher or 2G networks when required. By way of speeds, at lower costs, and with lower energy comparison, Long Term Evolution (LTE) requirements than ever before. Also, with the and LTE-A, described below, also allow introduction of IPv6, the number of connected backward compatibility. devices is rising rapidly. As a result, we are seeing an increasingly diverse composition of Long Term Evolution (LTE) connected devices, from laptops and smart- and LTE-Advanced phones to home appliances, vehicles, traffic Long Term Evolution, a wireless wide-area signals, and wind turbines. Such diversity in network technology, was developed by mem- the nature of connected devices is driving a bers of the 3rd Generation Partnership Project wider-scale adoption of an extensive range of body in 2008. This technology offers data network technologies. speeds of up to 300 Mbps.35 • Data rates: In the last 30 years, data rates LTE-Advanced (LTE-A) is a recent addition have increased from 2 Kbps to 1 Gbps, to the LTE technology that offers still-higher facilitating seamless transfer of heavy data data rates of 1 Gbps compared to 300 Mbps by files (see figure 8 for various cellular-tech- LTE.36 There is debate among industry practi- nology generations). The transition from tioners on whether LTE is truly a 4G technol- the first cellular generation to the second ogy: Many consider LTE a pre-4G technology changed the way communication messages and LTE-A a true 4G technology.37 Given its were sent—from analog signals to digital high bandwidth and low latency, LTE is touted signals. The transition from the second to as the more-promising technology for IoT the third generation marked a leap in capa- applications; however, the underlying network bility, enabling users to share multimedia infrastructure remains under development, as content over high-speed connections. described in the challenges below.
12 A primer on the technologies building the IoT
Figure 8. Data rates 5G key features Possibility of simultaneous access to different wireless networks 1 Gbps 1000000 and higher 4G key features Higher bandwidth that provides multimedia services and global roaming at much lower costs 800000 1G key features Analog signals; only intra-country network, 600000 3G key features no international Improved speed; the technology provides roaming voice, data, and video telephony services
400000 2G key features Digital signals; international 200 Mbps to roaming 1 Gbps 200000
2 kbps 14-64 kbps 2 Mbps 0 1984 1999 2002 2010 2020
Note: Data rates based on various cellular technology generations and the years represent the deployment start.
Source: Roopali Sood and Atul Garg, “Digital society from 1G to 5G: A comparative study,” International Journal of Application or Innovation in Engineering & Management volume 3, issue 2 (2014): p.191. Graphic: Deloitte University Press | DUPress.com
• Internet transit prices: The Internet transit of data; 1 byte = 8 bits), about 50 percent price is the price charged by an Internet lower than that of Bluetooth Classic.43 service provider (ISP) to transfer data from one point in the network to another. Since • IPv6 adoption: Given IPv6’s massive iden- no single ISP can cover the worldwide net- tification space, new devices are typically work, the ISPs rely on each other to trans- IPv6-based, while companies are transi- fer data using network interconnections tioning existing devices from IPv4 to IPv6. through gateways.41 Internet transit prices In 2015, according to Cisco, the number have come down in recent years due to of IPv6-capable websites increased by 33 technology developments such as the global percent over the prior year.44 By 2018, 50 increase in submarine cabling, the rising percent of all fixed and mobile device con- use of wavelength division multiplexing by nections are expected to be IPv6-based, ISPs, the transition to higher-capacity band- compared to 16 percent in 2013.45 width connections, and increased competi- 42 tion between ISPs. In 2003, it cost $120 to Challenges and transfer 1 Mbps in the United States; as of 2015, the cost has come down to 63 cents potential solutions (see figure 9). Even though network technologies have improved in terms of higher data rates and • Power efficiency: Availability of power- lower costs, there are challenges associated efficient networks is critical given the with interconnections, penetration, security, increase in the number of connected and power consumption. devices. Bluetooth Low Energy has a power consumption of 0.153 μW/bit (0.153 • Interconnections: Metcalfe’s Law states microwatts consumed in transferring 1 bit that “the value of a network is proportional
13 Inside the Internet of Things (IoT)
Figure 9. Internet transit prices in the United States 1,000
120 100
US$/Mbps 10
1
0.63
0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Note: Transit prices are plotted on a logarithmic scale.
Source: DrPeering.net, http://drpeering.net/white-papers/Internet-Transit-Pricing-Historical-And-Projected.php, accessed January 21, 2015. Graphic: Deloitte University Press | DUPress.com
Figure 10. Number of connected devices globally
60 50.1 50 42.1
40 34.8 28.4 30
Billions 22.9 20 18.2 14.4 11.2 10 8.7 0.001 0.5 0
1993 2003 2012 2013 2014 2015 2016 2017 2018 2019 2020
Source: National Cable & Telecommunications Association, “Broadband by the numbers,” https://www.ncta.com/broad- band-by-the-numbers, accessed April 22, 2015. Graphic: Deloitte University Press | DUPress.com
to the square of the number of compatibly • Network penetration: There is limited pen- communicating devices.” There is limited etration of high-bandwidth technologies value in connecting the devices to the such as LTE and LTE-A, while 5G tech- Internet; companies can create enhanced nology has yet to arrive.46 Currently, LTE value by connecting devices to the network accounts for only 5 percent of the world’s and to each other. Different network tech- total mobile connections. LTE penetration nologies require gateways to connect with as a percentage of connections is 69 percent each other. This adds cost and complexity, in South Korea, 46 percent in Japan, and 40 which can often make security management percent in the United States, but its penetra- more difficult. tion in the developing world stands at just 2 percent.47 In emerging markets, network operators are treading the slow-and-steady
14 A primer on the technologies building the IoT
path to LTE infrastructure, given the integrity while remaining energy efficient accompanying high costs and their focus on stands as an enduring challenge. fully reaping the returns on the investments in 3G technology that they made in the last • Power: Devices connected to a network three to five years. consume power, and providing a continu- ous power source is a pressing concern • Security: With a growing number of sensor for the IoT. Depending on the applica- systems being connected to the network, tion, a combination of techniques such as there is an increasing need for effective power-aware routing and sleep-scheduling authentication and access control. The protocols can help improve power manage- Internet Protocol Security (IPSec) suite ment in networks. Power-aware routing provides a certain level of secured IP con- protocols determine the routing decision nection between devices; however, there based on the most energy-efficient route for are outstanding risks associated with the transmitting data packets; sleep-scheduling security of one or more devices being com- protocols define how devices can “sleep” promised and the impact of such breaches and remain inactive for better energy effi- on connected devices.48 Maintaining data ciency without impacting the output.
15 Inside the Internet of Things (IoT)
Augmented ACT Sensors behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness Standards intelligence Network
COMMUNICATE AGGREGATE
Standards
An overview • Choice and notice: The principle of choice and notice states that entities that col- The third stage in the Information Value lect data should give users the option to Loop—aggregate—refers to a variety of activi- choose what they reveal and notify users ties including data handling, processing, and when their personal information is being storage. Data collected by sensors in different recorded. This may not be required for IoT locations are aggregated so that meaning- applications that aggregate information, de- ful conclusions can be drawn. Aggregation linked to any specific individual. increases the value of data by increasing, for example, the scale, scope, and frequency of • Purpose specification and use limitation: data available for analysis. Aggregation is This principle states that entities collect- achieved through the use of various standards ing data must clearly state the purpose to depending on the IoT application at hand. the authority that permits the collection of According to the International Organization those data. The use of data must be limited for Standardization (ISO), “a standard is a to the purpose specified, although this document that provides requirements, speci- might hinder creative uses of collected data fications, guidelines or characteristics that can sets in various IoT applications. be used consistently to ensure that materials, products, processes and services are fit for their • Data minimization: The principle of data purpose.”49 minimization suggests that a company can Two broad types of standards relevant collect only the data required for a spe- for the aggregation process are technology cific purpose and delete that data after the standards (including network protocols, com- intended use. This necessarily restricts the munication protocols, and data-aggregation scope of analysis that can result from slicing standards) and regulatory standards (related and dicing the IoT data. to security and privacy of data, among other issues). • Security and accountability: This principle We discuss technology standards in the states that entities that collect and store data “Enabling technology standards” discus- are accountable and must deploy security sion later in this section. The second type of systems to avoid any unauthorized access, standards relates to regulatory standards that modification, deletion, or use of the data. will play an important role in shaping the IoT landscape. There is a need for clear regulations related to the collection, handling, ownership, It is unclear, as of now, who will design, use, and sale of the data. Within the context of develop, and implement any regulatory stan- expanding IoT applications, it is worthwhile to dards specifically tailored to IoT applications. consider the US Federal Trade Commission’s There is discussion about the appropriateness privacy and security recommendations dubbed of existing guidelines and whether they are the Fair Information Practice Principles adequate for evolving IoT applications. For (FIPPs) and described below.50 example, the US Health Insurance Portability
16 A primer on the technologies building the IoT
and Accountability Act (HIPAA) governs the such as Wi-Fi, Ethernet, and possibly protection of medical information collected Bluetooth and ZigBee.53 by doctors, hospitals, and insurance com- panies.51 However, the act does not extend • Communication protocols: Once devices to information collected through personal are connected to a network and they iden- wearable devices.52 tify each other, communication protocols (a set of rules) provide a common language Enabling technology standards for devices to communicate. Various com- munication protocols are used for device- We just discussed the issues related to to-device communication; broadly, they regulatory standards. Technology standards, vary in the format in which data packets the second type, comprises three elements: net- are transferred. There are ongoing efforts work protocols, communication protocols, and to identify protocols better suited to IoT data-aggregation standards. Network protocols applications. Toward that end, we earlier define how machines identify each other, while discussed the advantages and limitations communication protocols provide a set of rules of the Constrained Application Protocol, a or a common language for devices to commu- communication protocol lighter than other nicate. Once the devices are “talking” to each popular protocols such as HTTP. other and sharing data, aggregation standards help to aggregate and process the data so that • Data aggregation standards: Data col- those data become usable. lected from multiple devices come in • Network protocols: Network protocols different formats and at different sampling refer to a set of rules by which machines rates—that is, the frequency at which data identify and authorize each other. are collected. One set of data-aggregation Interoperability issues result from multiple tools—Extraction, Transformation, network protocols in existence. In recent Loading (ETL) tools—aggregate, process, years, companies in the IoT value chain and store data in a format that can be have begun working together to help align used for analytics applications (see fig- 54 multiple network protocols. One example ure 11). Extraction refers to acquiring is the AllJoyn standard established by data from multiple sources and multiple Qualcomm in late 2013 that allows devices formats and then validating to ensure to discover, connect, and communicate that only data that meet a criterion are directly with other AllJoyn-enabled prod- included. Transformation includes activi- ucts connected to different technologies ties such as splitting, merging, sorting, and transforming the data into a desired
Figure 11. Typical data aggregation process
Unprocessed Analytics data Extraction Transformation Loading tools Analysis and visualizations
Data set 1 ID Name 101 Tom 102 Dave 103 Alice
Queried data SQL queries Data set 2 ID Name State ID State 101 Tom AZ 101 AZ 103 Alice CA 102 TX 103 CA
Graphic: Deloitte University Press | DUPress.com
17 Inside the Internet of Things (IoT)
format—for example, names can be split aggregation and storage.56 Hadoop comprises into first and last names, while addresses two major components: MapReduce and can be merged into city and state format. Hadoop Distributed File System (HDFS). Loading refers to the process of loading the While MapReduce enables aggregation and data into a database that can be used for parallel processing of large data sets, HDFS is analytics applications. a file-based storage system and a type of “Not Traditional ETL tools aggregate and store only SQL (noSQL)” database. Compared to the data in relational databases, in which data relational databases, NoSQL databases rep- are organized by establishing relationships resent a wider variety of databases that can based on a unique identifier. It is easy to enter, store unstructured data. Data processed and store, and query structured data in relational stored on Hadoop systems can be queried databases using structured query language through Hadoop application program inter- (SQL). The American National Standards faces (APIs) that offer an easy user inter- Institute standardized SQL as the querying face to query the data stored on HDFS for language for relational databases in 1986.55 analytics applications. SQL provides users a medium to communicate Depending on the type of data and pro- with databases and perform tasks such as data cessing, different tools could be used. While modification and retrieval. As the standard, MapReduce works on parallel processing, SQL aids aggregation not just in centralized Spark, another big-data tool, works on both databases (all data stored in a single location) parallel processing and in-memory process- 57 but also in distributed databases (data stored ing. Considering storage databases, HDFS is a on several computers with concurrent data file-based database that stores batch data such modifications). as quarterly and yearly company financial data, With recent advances in easy and cost- while Hbase and Cassandra are event-based effective availability of large volumes of data, storage databases that are useful for storing there is a question about the adequacy of tradi- streaming (or real-time) data such as stock- 58 tional ETL tools that can typically handle data performance data. We discussed select big- in terabytes (1 terabyte = 1012 bytes). Big-data data tools above; other tools exist with a range ETL tools developed in recent years can handle of benefits and limitations, and the choice of a a much higher volume of data, such as in tool depends on the application at hand. petabytes (1 petabyte = 1000 terabytes or 1015 bytes). In addition to handling large volume, Factors driving adoption big-data tools are also considered to be better within the IoT suited to handle the variety of incoming data, At present, the IoT landscape is in a nascent structured as well as unstructured. Structured stage, and existing technology standards serve data are typically stored in spreadsheets, while specific solutions and stakeholder require- unstructured data are collected in the form of ments. There are many efforts under way images, videos, web pages, emails, blog entries, to develop standards that can be adopted documents, etc. more widely. Primarily, we find two types of Apache Hadoop is a big-data tool useful developments: vendors (across the IoT value especially for unstructured data. Based on the chain) coming together to an agreement, and Java programming language, Hadoop, devel- standards bodies (for example, IEEE or ETSI) oped by the Apache Software Foundation, working to develop a standard that vendors is an open-source tool useful for process- follow. Time will tell which one of these two ing large data sets. Hadoop enables parallel options will prevail. Ultimately, it might be processing of large data across clusters of difficult to have one universal standard or “one computers wherein each computer offers local ring to rule them all” either at the network or
18 A primer on the technologies building the IoT
communication protocol level or at the data- standards to handle unstructured data and aggregation level. legal and regulatory standards to maintain In terms of network and communication data integrity. There are gaps in people skills to protocols, a few large players have at hand a leverage the newer big-data tools, while secu- meaningful opportunity to drive the standards rity remains a major concern, given the fact that IoT players will follow for years to come. that all the data are aggregated and processed As an example, Qualcomm—with other com- at this stage of the Information Value Loop. panies such as Sony, Bosch, and Cisco—has developed the AllSeen Alliance that provides • Standard for handling unstructured the AllJoyn platform, as described earlier.59 On data: Structured data are stored in rela- similar lines, through the Open Interconnect tional databases and queried through SQL. Consortium, Intel launched the open-source Unstructured data are stored in differ- IoTivity platform that facilitates device-to- ent types of noSQL databases without device connectivity.60 IoTivity offers its mem- a standard querying approach. Hence, bers a free license of the code, while AllSeen new databases created from unstruc- does not. However, AllSeen-compliant devices tured data cannot be handled and used by are already available, while devices compliant legacy database-management systems that with IoTivity are expected to be available by companies typically use, thus restricting 65 the second half of 2015.61 Both platforms are their adoption. comparable but not interoperable, just as iOS and Android are.62 • Security and privacy issues: There is a Concurrently, various standards bodies need for clear guidelines on the reten- are also working to develop standards (for tion, use, and security of the data as well network and communication protocols) that as metadata, the data that describe other apply to their geographical boundaries and data. As discussed earlier, there is a trade- could extend well beyond to facilitate world- off between the level of security and the wide IoT communications. As an example, the memory and bandwidth requirements. ETSI, which primarily has a focus on Europe, is working to develop an end-to-end architec- • Regulatory standards for data markets: ture called the oneM2M platform that could Data brokers are companies that sell data be used worldwide.63 IEEE, another standards collected from various sources. Even body, is making progress with the IEEE P2413 though data appear to be the currency of working group and is coordinating with stan- the IoT, there is lack of transparency about dards bodies such as ETSI and ISO to develop who gets access to data and how those data a global standard by 2016.64 are used to develop products or services In terms of data aggregation, relational and sold to advertisers and third parties. databases and SQL are considered to be the A very small fraction of the data col- standards for storing and querying structured lected online is sold online, while a larger data. However, we do not yet have a widely share is sold through offline transactions 66 used standard for handling unstructured data, between providers and users. As high- even though various big-data tools are avail- lighted by the Federal Trade Commission, able. We discuss this challenge below. there is an increased need for regulation of data brokers.67 Challenges and • Technical skills to leverage newer aggre- potential solutions gation tools: Companies that are keen on For effective aggregation and use of the leveraging big-data tools often face a short- data for analysis, there is a need for technical age of talent to plan, execute, and maintain
19 Inside the Internet of Things (IoT)
systems.68 There is an uptrend in the num- this is far fewer than the number of engi- ber of engineers being trained to use newer neers trained in traditional languages such tools such as Spark and MapReduce, but as SQL.69
20 A primer on the technologies building the IoT
Augmented ACT Sensors behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness Augmented intelligence intelligence Network
COMMUNICATE AGGREGATE
Standards
An overview “Analytics typically involves sifting through mountains of what are often confusing and XTRACTING insight from data requires conflicting data—in search of nuggets of analysis, the fourth stage in the E insight that may inform better decisions.”71 As Information Value Loop. Analysis is driven by figure 12 illustrates, there are three different cognitive technologies and the accompany- ways in which analytics can inform action.72 ing models that facilitate the use of cognitive At the lowest level, descriptive analytics technologies.70 We refer to these enablers tools augment our intelligence by allowing us collectively as “augmented intelligence” to to work effectively with much larger or more capture the idea that systems can automate complex data sets than we could otherwise intelligence—a concept that for us includes easily handle. Various data visualization tools notions of volition and purpose—in a way that such as Tableau and SAS Visual Analytics excludes human agency but nevertheless can make large data sets more amenable to human be supplemented and enhanced. comprehension and enable users to identify insights that would otherwise be lost in the Enabling augmented- huge heap of data. intelligence technologies Predictive analytics is the beginning of In the context of the value loop, analysis is keener insight into what might be happen- useful only to the extent that it informs action. ing or could happen, given historical trends.
Figure 12: Types of analytics77
Higher
Prescriptive analytics Answers: What should one do for a desired outcome?
Predictive analytics Answers: What could happen? applications Descriptive analytics
Complexity of analytics Answers: What has happened?
Lower Higher Degree of business impact
Degree of business impact represents the shift from post-mortem analysis to informed future planning based on past experienc- es. The shift in the basis of decision making from hindsight to insight and foresight could help companies move closer to a business objective. Complexity of analytics applications refers to the algorithmic sophistication of analytics tools used and characteristics (for example, scale, scope, and frequency) of data sets used. The shift from descriptive to predictive and prescriptive analytics requires increasingly complex analytics applications (data scientists, large and clean data sets, big data tools); however, the higher degree of business impact should prompt companies to ascend the analytics stack and leverage the copious amount of data to aid decision making and action.
Graphic: Deloitte University Press | DUPress.com
21 Inside the Internet of Things (IoT)
on the best course of action, the element of human participation becomes more impor- tant; the focus shifts from a purely analytics exercise to behavior change management. We discuss this more in the “Augmented behavior” section. With advances in cognitive technologies’ ability to process varied forms of information, vision and voice have also become usable. Below, we discuss select cognitive technolo- gies that are experiencing increasing adop- tion and can be deployed for predictive and prescriptive analytics.78
• Computer vision refers to computers’ abil- ity to identify objects, scenes, and activities Predictive analytics exploits the large quantity in images. Computer vision technology uses and increasing variety of data to build useful sequences of imaging-processing operations models that can correlate seemingly unrelated and other techniques to decompose the task variables.73 Predictive models are expected of analyzing images into manageable pieces. to produce more accurate results through Certain techniques, for example, allow for machine learning, a process that refers to detecting the edges and textures of objects computer systems’ ability to improve their per- in an image. Classification models may be formance by exposure to data without the need used to determine whether the features to follow explicitly programmed instructions. identified in an image are likely to repre- For instance, when presented with an informa- sent a kind of object already known to the tion database about credit-card transactions, system.79 Computer vision applications are a machine-learning system discerns patterns often used in medical imaging to improve that are predictive of fraud. The more transac- diagnosis, prediction, and treatment tion data that the system processes, the better of diseases.80 its predictions should become.74 Unfortunately, in many practical applications, even seem- • Natural-language processing refers to ingly strong correlations are unreliable guides computers’ ability to work with text the to effective action. Consequently, predictive way humans do, extracting meaning from analytics in itself still relies on human beings text or even generating text that is readable. to determine what sorts of interventions are Natural-language processing, like computer likeliest to work.75 vision, comprises multiple techniques that Finally, prescriptive analytics takes on may be used together to achieve its goals. the challenge of creating more nearly causal Language models, a natural-language models.76 Prescriptive analytics includes processing technique, are used to predict optimization techniques that are based on the probability distribution of language large data sets, business rules (information expressions—the likelihood that a given on constraints), and mathematical models. string of characters or words is a valid part Prescriptive algorithms can continuously of a language, for instance. Feature selection include new data and improve prescriptive may be used to identify the elements of a accuracy in decision optimizations. Since piece of text that may distinguish one kind prescriptive models provide recommendations of text from another—for example, a spam email versus a legitimate one.81
22 A primer on the technologies building the IoT
• Speech recognition focuses on accurately tools. As an example, the number of R transcribing human speech. The technology packages (a package includes R functions, must handle various inherent challenges data sets, and underlying code in a usable such as diverse accents, background noise, format) has increased 40-fold since 2001 homophones (for example, “principle” (see figure 15).84 versus “principal”), and speed of speaking. Speech-recognition systems use some of • Real-time data processing and analysis: the same techniques as natural-language Analytics tools such as complex event pro- processing systems, as well as others such as cessing (CEP) enable processing and analy- acoustic models that describe sounds and sis of data on a real-time or a near-real-time the probability of their occurring in a given basis, driving timely decision making and sequence in a given language.82 Applications action.87 An event is any activity—such of speech-recognition technologies include as stock trades, sales orders, social media medical dictation, hands-free writing, voice posts, and website clicks—that leads to the control of computer systems, and telephone creation and potential use of data. CEP customer-service applications. Domino’s tools monitor, process, and analyze streams Pizza, for instance, recently introduced a of data coming from multiple sources mobile app that allows customers to use to identify movements in data that help natural speech to place orders.83 identify abnormal events or patterns so that any required action can be taken as quickly Factors driving adoption as possible. CEP tools can be proprietary or open-source; Apache Spark, discussed within the IoT earlier, is an example of a big-data tool that Availability of big data—coupled with offers CEP functionality. growth in advanced analytics tools, proprietary CEP is relevant for the IoT in its ability to as well as open-source—is driving augmented recognize patterns in massive data sets at intelligence. Typical intelligence applications low latency rates. A CEP tool identifies pat- are based on batch processing of data; however, terns by using a variety of techniques such the need for timely insights and prompt action as filtering, aggregation, and correlation to is driving a growing adoption of real-time data trigger automated action or flag the need analysis tools. for human intervention.86 CEP tools exhibit low latency rates—sometimes less than a • Availability of big data: Artificial intel- second—by using techniques such as con- ligence models can be improved with tinuous querying, in-memory processing, large data sets that are more readily avail- and parallel processing.87 Continuous que- able than ever before, thanks to the lower rying works on the principle of incremental storage costs. Figures 13 and 14 show the processing. For example, once the system recent decline in storage costs alongside has calculated the average of a million the growth in enterprise data over the observations, the moment the next data last decade. point comes in, the system simply updates the value and doesn’t scan the entire data • Growth in crowdsourcing and open- set to calculate the average; this saves com- source analytics software: Cloud-based putational time and resources. In-memory crowdsourcing services are leading to new processing—the process of storing data in algorithms and improvements in exist- random access memory (RAM) in lieu of ing ones at an unprecedented rate. Data hard disks—enables faster data processing. scientists across the globe are working to Lastly, parallel processing, the process- improve the breadth and depth of analytics ing of data across clusters of computers,
23 Inside the Internet of Things (IoT)
is achieved by partitioning data either by a customer’s transactions with the bank source or type. through various channels. The tool can In the banking industry, CEP plays a key be used to monitor any large withdraw- role in cross-selling services. A CEP tool als through the ATM or check payments. can continuously monitor and analyze Further, the tool could correlate the
Figure 13. Storage cost (US$ per gigabyte)
$12 $11.00
$9
$6
$3
$0.03 $0 2000 2001 2002 2003 2004 2005 2006 2007 2009 2008 2010 2011 20122013 2014
Source: “Average cost of hard drive storage,” Statistic Brain, http://www.statisticbrain.com/average-cost-of-hard-drive-storage/, accessed February 10, 2015. Graphic: Deloitte University Press | DUPress.com
Figure 14. Enterprise data generated globally
1400 1222 1200 1000 800
Exabytes 600 400 200 5 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Note: 1 Exabyte = 1018 bytes.
Source: Axel Ngonga, “Data acquisition,” University of Leipzig and AKSW Research Group, presented at the Big Data webinar, November 21, 2013. Graphic: Deloitte University Press | DUPress.com
Figure 15. Growth of open-source analytics
6000 5051
4000
available 2000 129 Number of R packages 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 20122013 2014
Source: r4stats.com, “R continues its rapid growth,” http://r4stats.com/2014/04/07/r-continues-its-rapid-growth/, April 7, 2014, accessed April 23, 2015. Graphic: Deloitte University Press | DUPress.com
24 A primer on the technologies building the IoT
transactions with any accompanying bank- programmed to recognize and exclude branch transactions such as an address negative events, thus the faux pas.89 change—potentially indicating a house purchase—and present a cross-selling • Legacy systems’ ability to analyze unstruc- opportunity, say home insurance, before a tured data: Legacy systems are well suited competitor draws the customer away.88 Such to handle structured data; unfortunately, cross-selling recommendations can then be most IoT/business interactions generate automatically pushed onto web-based bank- unstructured data.90 Unstructured data are ing systems and concurrently used by bank- growing at twice the rate of structured data ers, tellers, and call-center representatives. and already account for 90 percent of all enterprise data.91 While traditional rela- Challenges and tional-database systems will continue to be relevant for structured-data management potential solutions and analysis, a steady influx of IoT-driven Limitations of augmented intelligence result applications will require analytics systems from the quality of data, human inability to that can handle unstructured data without develop a foolproof model, and legacy systems’ compromising the scope of data. limited ability to handle unstructured and real-time data. Even if both the data and model • Legacy systems’ ability to manage real- are shipshape, there could be challenges in time data: Traditional analytics software human implementation of the recommended generally works on batch-oriented process- action; in the next section, on augmented ing, wherein all the data are loaded in a behavior, we discuss the challenges related to batch and then analyzed.92 This approach human behavior. does not deliver the low latency required for near-real-time analysis applications. • Inaccurate analysis due to flaws in the Predictive applications could be designed data and/or model: A lack of data or to use a combination of batch processing presence of outliers may lead to false and real-time processing to draw meaning- positives or false negatives, thus exposing ful conclusions.93 Timeliness is a challenge various algorithmic limitations. Also, if in real-time analytics—that is, what data all the decision rules are not correctly laid can be considered truly real?94 Ideally, data out, the algorithm could throw incorrect are valid the second they are generated; conclusions. For example, a social network- however, because of practical issues related ing site recently featured the demise of a to latency, the meaning of “real time” varies subscriber’s daughter on an automatically from application to application. generated dashboard. The model was not
25 Inside the Internet of Things (IoT)
Augmented ACT Sensors behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness Augmented behavior intelligence Network
COMMUNICATE AGGREGATE
Standards
An overview • Machine-to-machine (M2M) interfaces: M2M interfaces refer to the set of technolo- In its simplest sense, the concept of “aug- gies that enable machines to communi- mented behavior” is the “doing” of some action cate with each other and drive action. In that is the result of all the preceding stages of common vernacular, M2M is often used the value loop—from sensing to analysis of interchangeably with the IoT.95 For our data. Augmented behavior, the last phase in the purposes, though, the IoT is a broader loop, restarts the loop because action leads to concept that includes machine-to-machine creation of data, when configured to do so. and machine-to-human (M2H) interfaces, There is a thin line between augmented as well as support systems that facilitate the intelligence and augmented behavior. For management of information in a way that our purpose, augmented intelligence drives creates value.96 informed action, while augmented behavior is an observable action in the real world. • Machine-to-human interfaces: We discuss As a practical matter, augmented behavior M2H interfaces in the context of individual finds expression in at least three ways: users; business users of M2H interfaces are
Figure 16. Representative examples of M2M and M2H applications M2M interfaces Industrial automation Smart grid
M2H interfaces Home automation Wearables
Graphic: Deloitte University Press | DUPress.com
26 A primer on the technologies building the IoT
discussed in the next element, organiza- 1940–1970 tional entities. Based on the data collected In the late 1940s, a few non-programmable and algorithmic calculations, machines robots were developed; these robots could have the potential to convey suggestive not be reprogrammed to adjust to chang- actions to individuals who then exercise ing situations and, as such, merely served as their discretion to take or not to take the mechanical arms for heavy, repetitive tasks in recommended action. With human interac- manufacturing industries.99 In 1954, George tion, the IoT discussion shifts into a slightly Devol developed one of the first programmable different direction, toward behavioral robots,100 and in the early 1960s, an increasing sciences, which is distinct from the data number of companies started using pro- science that encapsulates the preceding four grammable robots for industrial automation stages focused on creating, communicat- applications such as warehouse management ing, aggregating, and analyzing the data to and machining.101 derive meaningful insights.97 1970–2000 • Organizational entities: Organizations This period witnessed key developments include individuals and machines and thus related to the evolution of adaptive robots.102 involve the benefits as well as the chal- As the name suggests, adaptive robots embed- lenges of both M2M and M2H interfaces. ded with sensors and sophisticated actuation Managing augmented behavior in organiza- systems can adapt to a changing environment tional entities requires changes in people’s and can perform tasks with higher precision behaviors and organizational processes. and complexity compared to earlier robots.103 Business managers could focus on process During this period, robotic machines that redesign based on how information creates could adapt to varying situations were used value in different ways.98 to identify objects and autonomously take action in applications such as space vehicles, Enabling augmented unmanned aerial vehicles, and submarines.104 behavior technologies 2000–2010 The enabling technologies for both M2M The development of an open-source robot and M2H interfaces prompt a consideration of operating system (developed by the Open the evolution in the role of machines—from Source Robotics Foundation) in 2006 was simple automation that involves repetitive an important driver enabling the develop- tasks requiring strength and speed in struc- ment and testing of various robotic technolo- tured environments to sophisticated applica- gies.105 As robots’ intelligence and precision of tions that require situational awareness and execution improved, they increasingly started complex decision making in unstructured working with human beings on critical tasks environments. The shift toward sophisticated such as medical surgeries. Following the US automation requires machines to evolve in two Defense Advanced Research Project Agency’s ways: improvements in the machine’s cogni- competition for developing autonomous mili- tive abilities (for example, decision making tary vehicles in 2004, many automakers made a and judgment) discussed in the previous headway into military and civilian autonomous section and the machine’s execution or actua- vehicles.106 Even though the underlying tech- tion abilities (for example, higher precision nology is available, legal and social challenges along with strength and speed). With respect related to the use of autonomous vehicles are to robots specifically, we present below an yet to be resolved. overview of how machines have ascended this evolutionary path:
27 Inside the Internet of Things (IoT)
2010 onward Factors driving adoption With the availability of big data, cloud- within the IoT based memory and computing, new cognitive technologies, and machine learning, robots in Improved functionality at lower prices is general seem to be getting better at decision driving higher penetration of industrial robots making and are gradually approaching auton- and increasing the adoption of surgical robots, omy in many actions. We are witnessing the personal-service robots, and so on. For situ- development of machines that have anthropo- ations where a user needs to take the action, morphic features and possess human-like skills machines are increasingly being developed such as visual perception and speech recogni- with basic behavioral-science principles in tion.107 Machines are automating intelligence mind. This allows machines to influence work such as writing news articles and doing human behaviors in effective ways. legal research—tasks that could be done only • Lower machine prices: The decreasing 108 by humans earlier. prices of underlying technologies—such as
Figure 17. Average selling price of industrial robots
$62000 $60,748
$57600
$53200
$48800 $45,200 $44400
$40000 2000 2001 2002 2003 2004 2007 2010 2012 2013
Note: The prices shown above do not include the cost of peripheral devices required and installation and maintenance charges. Industrial robots available at different price points can perform a wide range of activities. Robots available at the above price points can perform welding, loading, and painting, among other activities.
Source: Louis-Vincent Gave, Too Different for Comfort (Hong Kong: GaveKal Research, 2013), p. 24; Frank Tobe, “Competing sales reports for industrial robotics,” http://www.therobotreport.com/news/competing-sales-reports-for-industrial-robotics, January 28, 2014, accessed April 23, 2015. Graphic: Deloitte University Press | DUPress.com
Figure 18. Unit sales of industrial robots globally
180000 178,993
160000
140000
120000
100000 98,943
80000
60000 2000 2001 2002 2003 2004 2007 2010 2012 2013
Source: World robotics: Industrial robotics, IFR Statistical Department, 2014, http://www.worldrobotics.org/uploads/media/Exec- utive_Summary_WR_2014_02.pdf, accessed April 23, 2015; Trends in the market for the robot industry in 2012, Industrial Machinery Division, Japanese Ministry of Economy, Trade and Industry, July 2013, http://www.meti.go.jp/english/press/2013/pd- f/0718_02.pdf, accessed April 23, 2015. Graphic: Deloitte University Press | DUPress.com
28 A primer on the technologies building the IoT
sensors, network connections, data process- ing and computing tools, and cloud-based storage—are leading to lower prices of robots. Figures 17 and 18 show a decline in the average selling price of industrial robots alongside increasing unit sales.
• Improved machine functionality: As dis- cussed earlier, there is a thin line between augmented intelligence and augmented behavior. An underlying driver for the shift in the use of robots from mundane to sophisticated tasks is the development of elaborate algorithms that focus on the qual- ity of fine decision making in live environ- ments, and not simply a binary “yes/no” decision. Typically, robots made force-fitted decisions based on programmed algorithms, irre- Source: "High-five with a humanoid robot" by Steve Jurvetsonis, licensed spective of the situation and information under CC BY 2.0, http://bit.ly/1dPYo7x, accessed June 4, 2015. availability.109 However, recent advances in robotic control architecture prompt behaviors—involve the design of choices the machine to ask for more information that prompt them to move from “intention” if there is an information insufficiency to “action.”112 For example, placing a fruit at before taking a decision.110 For example, eye level is a nudge technique, while ban- a robot offers a pill to the patient and the ning junk food is not, according to Richard patient refuses; instead of repeating the Thaler and Cass Sunstein.113 Choice designs same instruction, the robot could ana- could be built by consciously choosing lyze the patient’s behavior patterns based the options that should be presented to an on his personal data stored on the cloud individual and the manner in which the and try to deduce the reason for refusal. options are presented. For example, menus The robot may also deduce from the sometimes start with expensive items fol- environment that the patient has a fever lowed by relatively less expensive ones. This and inform the doctor. One of the many makes the customer feel that she is mak- techniques under development enables ing a judicious choice by ordering any of users to train robots by “rewarding” them the latter items, since her reference price in cases where they have made the right is much higher. Furthermore, a health- decision by telling them so and asking conscious restaurateur could place the them to continue to do the same—a kind of relatively healthful food items at relatively positive reinforcement.111 lower prices and, in so doing, “nudge” the customer to order them. • Machines “influencing” human actions through behavioral-science rationale: In an analogous fashion, IoT devices can Literature suggests that creating a new “nudge” human behaviors by establishing a human behavior is challenging. Creating feedback loop. A school in California was a new human behavior that endures is trying to find a solution to speeding drivers even more challenging. Nudge tech- who were undeterred even by police ticket- niques—attempts to influence people’s ing. The school authorities experimented
29 Inside the Internet of Things (IoT)
with a creative signboard that compared choice will likely only evolve and become more two data points: “your speed” (speed of prominent in the years ahead. a passing car measured by a radar sen- sor) against the “speed limit” of 25 miles/ Challenges and hour. Even though the radar signboard potential solutions offered drivers no new information—as the dashboard display readily provides There are challenges related to machines’ driver speed—the signboards “nudged” judgment in unstructured situations and the them into reducing their speed by an aver- security of the information informing such age of 10 percent, bringing their speed judgments. Interoperability is an additional within the permissible limit or sometimes issue when heterogeneous machines must even lower.115 In a similar way, David Rose work in tandem in an M2M setup. Beyond the helped develop an IoT-connected pill bottle issues related to machine behavior, managing equipped with “GlowCaps” that “nudge” the human behaviors in the cases of M2H inter- patient with a flash of light at the predeter- faces and organizational entities present their mined time to take a pill.116 own challenges. In addition to influencing the choices of • Machines’ actions in unpredictable situ- individuals on a stand-alone basis, IoT ations: Machines are typically considered devices can also drive adoption by “using” to be more reliable than human beings in social or peer pressure to achieve a desired structured environments that can be simu- result. For example, when a fitness device lated in programming models; however, in suggests to an individual that it’s time for the real world, most situations we encoun- a workout, the recommendation may go ter are unstructured.119 In such cases, unheard. However, if the device shows how machines cannot possibly be relied upon the user is doing vis-à-vis his peers (lagging completely; as such, the control should fall or outperforming), the user is potentially back to human beings. more likely to act.117 The manufacturer of an electronic soap dispenser fitted its • Information security and privacy: There product with a computer chip that records is a looming risk of compromise to the the frequency with which health care pro- machine’s security. An example of a privacy fessionals in different hospital wards wash concern relates to an appliance manufac- their hands; it then compares these results turer that offers televisions with voice- with World Health Organization standards recognition systems. The voice-recognition and conveys the comparisons back to the feature collects information related to not wards at a group level. Such a process of only voice commands but also any other aggregation and comparison effectively audio information it can sense.120 This raises makes personal hygiene a team sport: Each concerns about the manner in which the worker, in turn, is effectively “nudged” into audio information collected by the software a greater awareness of his hygienic habits, as will be used—both in benign and person- 118 he knows that he is a part of a team effort. ally invasive ways. A benign use could Other examples abound showing how the include, for example, the personalization of IoT can influence human behavior to achieve television advertising. A personally inva- normative outcomes. The larger point, though, sive use might be the unauthorized sale of is that the IoT may augment human behavior such information. as much as it augments mechanical behavior. And the interplay between the IoT and human • Machine interoperability: Performance of M2M interfaces is impacted by
30 A primer on the technologies building the IoT
Figure 19. Machine feature121
Glanceability Ability of devices to convey information at a glance-easy reading and comprehension
Gesturability Ability of machines to understand human gestures and take action
Affordability Ability to improve functionality and increase customization due to better affordability
Wearability Ease of wearing and even ingesting devices
Indestructibility Flexibility to use the device in any way since it is strong and sturdy
Usability Ability of the machine to embed one or more usable features
Ability of the device to look loveable and behave in amicable ways through the use of physical Loveability features
Figure 20. Machine enchantment hierarchy
Storyification: The machine narrates a personalized story that appeals to the user.
nt Gamification: The machine involves the users through games; participation is
tme encouraged through reward points.
Socialization: The machine enables users’ social connections and shares information with a chosen group of people.
Level of enchan Personalization: The machine develops an understanding of the users’ preferences over a period of time and delivers customized experience and service.
Connection: The machine provides the users a ubiquitous connectivity and access to resources on the cloud.
Source: David Rose, Enchanted Objects: Design, Human Desire, and the Internet of Things (New York: Simon & Schuster, 2014). Graphic: Deloitte University Press | DUPress.com
interoperability challenges resulting from course of action and, eventually, the devices heterogeneous brands, hardware, soft- end up serving as “shelfware.”122 ware, and network connections. There is David Rose states that machines cater to a need for a convergence of standards, as human drives; he cites six such drives: discussed earlier. omniscience (the need to know all), That machines perform as desired in a telepathy (human-to-human connec- particular context is a matter of getting the tions), safekeeping (protection from harm), technology right, which is currently in an immortality (longer life), teleportation evolving stage. Perfecting human behavior (hassle-free travel), and expression (the is another matter entirely. desire to create and express). Machines serve human drives through one or more of • Mean-reverting human behaviors: One of their features (see figure 19). These features the main challenges in M2H interfaces is also determine the machine’s position in that, although users have the smart devices, the enchantment hierarchy (see figure they minimally “follow” the suggested 20). The enduring association between the machine and its user is a function of the
31 Inside the Internet of Things (IoT)
Figure 21. An illustration of the relationship-building process between a machine and its user
Related books: What is loT? - IoT DIY approach - Executive's guide to IoT
IoT is the...
To acquire knowledge on a topic The free and “glanceable” ebook reader on the phone The phone’s browser stores the (need for omniscience), a user allows the user to get up-to-date with the topic. user’s preferences over a period browses the Web on his of time and recommends related smartphone and finds an ebook. books, and, thus offers personalized service to the user.
Graphic: Deloitte University Press | DUPress.com
machine’s position in the enchantment they do not understand how the outcome hierarchy, starting from the most basic level has been churned.125 of “connection” and going all the way up to To manage these augmented behavior 123 “storyification.” changes in organizations, decision makers could give the new technologies a fair chance • Inertia to technology-driven deci- to contribute in their decision-making process sion making in organizational entities: by setting aside their biases. At the same time, Decision makers have decades of experi- data scientists and developers could focus on ence running successful businesses wherein two objectives: continuously improving the they have primarily relied on professional statistical tools and the algorithms to bring the judgment. They typically encounter resis- machine’s decision-making ability closer to tance to newer developments such as reality, and making it easier for business users 124 predictive and prescriptive analytics. to comprehend the results through means Executives are skeptical of the accuracy and such as easy-to-use visualization tools. In the efficacy of conclusions drawn out of statisti- current state of affairs, augmented behavior cal analyses, since they view augmented has the potential to grow, with an increasing intelligence tools as “black boxes” in which number of successful use cases over time.
32 A primer on the technologies building the IoT
The IoT technology architecture
HE Information Value Loop can serve Our architecture for guiding the develop- Tas the cornerstone of an organization’s ment and deployment of IoT systems consists approach to IoT solution development for of the following views: potential use cases. To transform ideas and 1. Business. This view defines the vision concepts discussed earlier in the report into for an IoT system and covers aspects the concrete building blocks of a solution, we such as return on investment, value posit an end-to-end IoT technology architec- proposition, customer satisfaction, and ture to guide IoT solution development. This maintenance costs. architecture links strategy decisions to imple- mentation activities. It can serve as a playbook 2. Functional. The linchpin of the refer- for establishing the vision for an IoT solution ence architecture, this view spans modules and for converting that vision into tangible that cater to high-level information flow reality. The Information Value Loop informs through the system. It contains the func- and is present in each phase of this develop- tional layers for data creation, processing, ment, whereby ideas are made progressively and presentation. more specific, and tactical decisions remain consistent with the overall strategic goals. The 3. Usage. This view shows how the reference process of turning ideas into IoT solutions is model realizes key capabilities desired shown in figure 22. in a usage scenario. It may include the
Figure 22. Translating ideas into action
Augmented ACT Sensors IDEAS: Information Value Loop behavior
MAGNITUDE Scope Scale Frequency ANALYZE CREATE The Information Value Loop in the firm’s proprietary insight on the IoT. The RISK Security Reliability Accuracy
TIME Augmented Latency Timeliness framework helps clients understand the dynamics of the IoT-enabled world, intelligence Network
COMMUNICATE and identify “where to play” and “how to win.” AGGREGATE
Standards
BLUEPRINTS: Reference architecture/playbooks Like a blueprint, the reference architecture maps the SENSORS NETWORK INTEGRATION AUGMENTED AUGMENTED INTELLIGENCE BEHAVIOR
Create Communicate Aggregate Analyze Act
concepts of the Value Loop onto recognizable tech Data Data Data preparation/ Cold data acquisition transmission storage calculation Analytics Visualization areas, to begin to see the implications of strategic choices.
DATA AT REST PATH
DATA OS/ BATCH ANALYTICS & BUILDING BLOCKS: IoT “stack” IN-MEM DB DISCOVERY
Cluster Parallelized Analytics mgmt. data store engine Finally, based on those strategic choices, a specific IoT “stack” or platform
Paralellel Analytics solution can be determined to meet organizational goals. processing libraries
General engine
Source: Deloitte’s IoT Reference Architecture. Graphic: Deloitte University Press | DUPress.com
33 Inside the Internet of Things (IoT)
detailed use case description, user journey, and translates these blueprints into the and requirements. individual components needed to design, build, and implement the components and 4. Implementation. A technical representa- interconnections shown in the functional tion of usage scenario deployment, this and implementation views. view incorporates the technologies and Each of the views is detailed below. system components required to implement the functions prescribed by the usage and In the business view, the Information Value functional viewpoints. Loop stages are utilized to examine the flow of information which guides strategic deci- 5. Specifications. Finally, this view captures sions for the use case at hand. These decisions the complete IoT stack to be deployed. further help define the overall IoT strategy. An It includes detailed technical specifica- example of how value can be realized using IoT tions for the build-out of the solution, in health monitoring is shown in figure 23.
Figure 23. The IoT business view
Augmented ACT Sensors behavior B A
Meet Isabel
ANALYZE CREATE
Augmented intelligence Network
COMMUNICATE AGGREGATE
Integration
Source: Deloitte’s IoT Reference Architecture. Graphic: Deloitte University Press | DUPress.com
34 A primer on the technologies building the IoT
Prior to the IoT, the patient could wear a The functional view categorizes the com- heart monitor, but the monitor’s data would ponents of an IoT system across the five value usually be communicated to the external world loop stages and five functional layers—sensors, using written records that had to be carried network, integration, augmented intelligence, each time. This represented a blockage at the and augmented behavior. It serves as a guide to “communicate” step (see arrow “A”). the functional considerations and technology With the introduction of the IoT, data can choices of an IoT solution (see figure 24). now be communicated between a patient and As discussed earlier in the report, sensors the physician using network connections. create the data that are sent downstream to However, there is still a bottleneck associ- subsequent layers of the architecture. Network ated with the ability of the smart systems to is the connectivity layer that communicates interface with existing electronic health record data from the sensors and connects them to (EHR) systems in order to aggregate data. the Internet. The integration layer manages the Alleviating this bottleneck is key to IoT appli- sensor and network elements, and aggregates cations in the health care industry. data from various sources for analysis. The In the “Meet Isabel’ scenario of figure 23, augmented intelligence layer processes data the bottleneck associated with data aggregation into actionable insights. Finally, augmented and use can be addressed by the “integration” behavior encapsulates the actions or changes layer wherein standards for sensor manage- in human or machine behavior resulting ment, data transfer, storage, and aggregation from these insights. The augmented behavior come together in an integral fashion. In the layer includes an edge computing sub-layer earlier part of this report, we discuss “stan- defined by local analysis (near the source of dards” as they relate to specific technologies data) and action without the need for human that fall under the integration layer described intervention. Aligned with these layers and further in the functional view. the value loop stages are standards for sensor
Figure 24. The IoT functional view
CREATE COMMUNICATE AGGREGATE ANALYZE ACT
Augmented Augmented Sensors Network Integration intelligence behavior
Physical devices Connectivity Management Processing Applications • Sensor devices • Processing • IoT middleware • Processing • User applications elements Accumulation engines/ • Reporting • Connectivity • Data ingestion frameworks Edge computing elements • Data storage • Data messaging • Intelligent • Connectivity • Data abstraction Analytics gateways frameworks Existing data • Stream analytics • Fog computing (data in motion) • Unstructured data platforms • Batch analytics, • Structured data machine learning (data at rest)
Standards
Security
Source: Deloitte’s IoT Reference Architecture.
35 Inside the Internet of Things (IoT)
Figure 25. The IoT usage view
KNOW ME INSPIRE ME GUIDE ME CONNECT ME INFORM ME EXPEDITE ME Beacons enable Customers Once a product The app allows Customers can At the completion customers to be experience a is selected, a customers to access product of the shopping recognized customized guided store review social information and experience, entering and home screen map will allow media trends and reviews to help customers have navigating the and targeted customers to update their own make their the capability to store and mapped special offers. easily find their feeds. decision. pay for the product to their CRM way to the using the in-app profile and selected payment service. browsing history. product.
Source: Deloitte’s IoT Reference Architecture. Graphic: Deloitte University Press | DUPress.com
management and data management and use, as be used to identify the best mix of product well as security considerations including end- solutions for an IoT implementation across point protection, network security, intrusion different layers. Figure 26 shows a representa- prevention, and privacy and data protection. tive implementation view for the retail use case The usage view sets up the technical solu- example described earlier. tion by describing the user’s journey through The specifications view captures the final all the steps of the use case being implemented. translation of the various viewpoints described This view would include the key actors, that above as part of the IoT Reference Architecture may be users and/or machines, and the activi- into ground-level deployment. It crystalizes the ties involved. The usage view also describes the functional requirements and specific technol- use case from the point of view of user needs ogy choices identified earlier into detailed and system capabilities. Figure 25 illustrates a specification definitions that describe how all typical IoT use in a brick-and-mortar store. the selected components must be linked to The implementation view delves deeper work together. A sample specifications view is into specific technology choices and the shown in figure 27. vendor solutions that are used to deploy those Together, all the myriad viewpoints choices. It leverages the high-level component that comprise the Deloitte IoT Reference view from the functional architecture to frame Architecture form an end-to-end blueprint the specific system implementation. for realizing an IoT system from strategy Our IoT reference architecture describes through implementation. parameters and benchmark criteria that can
36 A primer on the technologies building the IoT
Figure 26. The IoT implementation view
SENSORS NETWORK INTEGRATION AUGMENTED AUGMENTED INTELLIGENCE BEHAVIOR
Create Communicate Aggregate Analyze Act
Data Data Cold data Data preparation/ acquisition transmission storage calculation Analytics Visualization SENSORS CONNECTIVITY COMPLEX EVENT PROCESSING MOBILE MQTT Store Mobile REPORTING floor apps/ Mobile beacon devices Data processing engine Message broker apps/ devices POS Beacons DATA IN MOTION PATH Push beacon notification Real-time Bluetooth messaging LE DATA AT REST PATH
BATCH HADOOP DATA OS/ BATCH ANALYTICS & AGGREGATION VISUALIZATION INGEST CLUSTERS IN-MEM DB DISCOVERY Business intelligence & analytics Batch DFS Cluster Parallelized Analytics General ingest API cluster mgmt. data store engine engine Customized platform dashboards Store POS Distributed Parallel Analytics system file system processing libraries
General engine
INTERNAL EXTERNAL
Cloud On-premise Social data Demographic Browsing data data
Source: Deloitte’s IoT Reference Architecture. Graphic: Deloitte University Press | DUPress.com Figure 27. The IoT specifications view
1. Extract multiple streams from Cloud messaging Wearable wearable devices push notification devices Real-time Unified Sink Custom MQTT push notification 2. Mash up Mobile alerts aggregators platform API streams to HTTP sink (MQTT publisher) member data Real-time 3. Detect alerts, Rest APIs add indicators to stream Integration platform—restful Real-time feed web services Look-up Updates Unified Unified Rest APIs platform In-memory platform DB (TBD) Cloud Batch processing Cloud & transformations Updates Persistent with PSQL scripts
ODBC connection Distributed file system Analysis & visualization Daily refresh Daily refresh SFTP (frequently) tool
Health plan member data Source: Deloitte’s IoT Reference Architecture. Graphic: Deloitte University Press | DUPress.com
37 Inside the Internet of Things (IoT)
Closing thoughts
HE Internet of Things is an ecosystem At this relatively nascent stage, the IoT Tof ever-increasing complexity, and the ecosystem is fragmented and disorganized. vocabulary of its language is dynamic. As we Over time, the IoT ecosystem should undergo stated at the outset, our intent in presenting a streamlining and organizing process and a this primer is not to answer every question “knitting together” of its individual pieces. that a reader may have about the IoT. No single Because the IoT will play an increasingly resource could ever hope to achieve that end important role in how we live and run our about anything as elaborate as the IoT. Rather, businesses, Deloitte is undertaking an IoT- in developing this report, our objective was to focused eminence campaign. This primer provide a useful top-down reference to assist will serve as a foundational resource for the readers as they explore IoT-driven solutions. campaign that will include thoughtware exam- In using this primer, the reader should come ining the IoT both from industry- and issue- away with a better understanding of what the specific perspectives. IoT is as well as the elements that comprise its constituent parts within a strategic framework.
38 A primer on the technologies building the IoT
Glossary
Actuator: a device that complements a sensor Big data: a term popularly used to describe in a sensing system. An actuator converts an large data sets that cannot be handled effi- electrical signal into action, often by convert- ciently by traditional data management ing the signal to non-electrical energy, such systems. In addition to the large volume, the as motion. A simple example of an actuator is concept of big data also refers to the variety of an electric motor that converts electric energy data sets—i.e., structured and unstructured as into mechanical energy. well as the velocity or the rate at which the data are incoming. Analytics: the systematic analysis of often-con- fusing and conflicting data in search of insight Cloud computing: an infrastructure of shared that may inform better decisions. resources (such as servers, networks, and software applications and services) that allow Application program interfaces (API): a set users to scale up their data management and of software commands, functions, and pro- processing abilities while keeping the costs low. tocols that programmers can use to develop A cloud vendor invests in and maintains the software that can run on a certain operating cloud infrastructure; a user pays for only the system or website. On the one hand, APIs resources and applications he wishes to use. make it easier for programmers to develop software; on the other, they ensure that users Cognitive technologies: a set of technologies experience the same user interface when using able to perform tasks that only humans used to software built on the same API. be able to do. Examples of cognitive technolo- gies include computer vision, natural-language Artificial intelligence: the theory and devel- processing, and speech recognition. opment of computer systems able to perform tasks that normally require human intelligence. Communication protocol: a set of rules that The field of artificial intelligence has produced provide a common language for devices to a number of cognitive technologies such as communicate. Different communication proto- computer vision, natural-language processing, cols are used for device-to-device communica- speech recognition, etc. tion; broadly, they vary in the format in which data packets are transferred. One example Batch processing: the execution of a series is the familiar Hypertext Transfer Protocol of computer programs without the need for (HTTP). human intervention. Traditional analytics soft- ware generally works on batch-oriented pro- Complex event processing (CEP): an analyt- cessing wherein data are aggregated in batches ics tool that enables processing and analysis and then processed. This approach, however, of data on a real-time or a near-real-time does not deliver the low latency required for basis, driving timely decision making and near-real-time analysis applications. action. CEP is relevant for the IoT in its ability to recognize patterns in massive data sets at
39 Inside the Internet of Things (IoT)
low latency rates. A CEP tool identifies pat- and last names, addresses can be merged into terns by using a variety of techniques such city and state format, etc. Loading refers to the as filtering, aggregation, and correlation to process of loading the data into a database that trigger automated action or flag the need for can be used for analytics applications. human intervention. Gateway: a combination of hardware and soft- Computer vision: a type of cognitive tech- ware components that connects one network nology that refers to the ability of computers to another. to identify objects, scenes, and activities in images. Computer-vision technology uses Hadoop: an open-source tool that is useful for sequences of imaging-processing operations processing large data sets. Hadoop is a part of and other techniques to decompose the task the Apache Software Foundation and is based of analyzing images into manageable pieces. on the Java programming language. Hadoop Certain techniques, for example, allow for enables parallel processing of large data across detecting the edges and textures of objects in clusters of computers in which each computer an image. Classification models may be used to offers local aggregation and storage. determine if the features identified in an image are likely to represent a kind of object already In-memory processing: the process of stor- known to the system. ing data in random access memory instead of hard disks; this enables quicker data querying, Data rates: the speed at which data are trans- retrieval, and visualizations. ferred by a network. Sometimes termed “band- width,” data rates are typically measured in bits Internet Protocol (IP): an open network pro- transferred per second. Network technologies tocol that provides unique addresses to vari- that are currently available can deliver data ous devices connected to the Internet. There rates of up to 1 gigabit per second. are two versions of IP: IP version 4 (IPv4) and IPv6. Descriptive analytics: a type of analytics that provides insights into past business events and Internet transit prices: the price charged by an performance. In a fundamental sense, descrip- Internet service provider (ISP) to transfer data tive analytics helps answer the question “What on a network. Since no single ISP can cover the has happened?” Descriptive analytics tools worldwide network, the ISPs rely on each other augment human intelligence by allowing us to transfer data using network interconnec- to work effectively with much larger or more tions through gateways. complex data sets than we would ordinarily be able to without such tools. IP version 4 (IPv4): an older version of the Internet Protocol (IP); IPv6 is a most recent Extraction, Transformation, Loading (ETL) version. IPv4 offers an addressing space of tools: a set of data aggregation tools that about 6 billion addresses, out of which 4 bil- aggregate, process, and store data in a format lion addresses have been used already. IPv4 that can be used for analytics applications. allows a group of co-located sensors to be Extraction refers to acquiring data from mul- identified geographically but not individually, tiple sources and formats and then validating thus restricting the value that can be gener- to ensure that only data that meet a specific ated through the scope of data collected from criterion are included. Transformation includes individual devices that are co-located. activities such as splitting, merging, sorting, and transforming the data into a desired for- IP version 6 (IPv6): a recent version of the mat; for example, names can be split into first Internet Protocol (IP) that succeeds IPv4.
40 A primer on the technologies building the IoT
IPv6 has superior scalability and identifiability Machine-to-machine (M2M) interfaces: a features compared to IPv4: the IPv6 address set of technologies that enable machines to space supports approximately 3.4x1038 unique communicate with other machines and drive addresses compared to 6 billion addresses action. In common vernacular, M2M is often under IPv4. used interchangeably with the IoT. For our purposes, though, the IoT is a broader concept Latency: the time delay in transfer of data that includes machine-to-machine (M2M) from one point in a network to another. Low- and machine-to-human (M2H) interfaces, latency networks allow for near-real-time as well as support systems that facilitate the data communications. management of information in a way that creates value. Local area network (LAN): a network that extends to a geographic range of at least 100 Metadata: the data that describe other data. meters, such as within a house, office, etc. For example, metadata for a document would Devices could connect to wired or wire- typically include the author’s name, size of the less LAN technologies. Examples of wired document, last created or modified date, etc. LAN technologies include Ethernet, and fiber optics. Wi-Fi is an example of a wireless Natural-language processing: a type of cogni- LAN technology. tive technology that refers to computers’ ability to work with text the way humans do, extract- Machine learning: the ability of computer ing meaning from text or even generating text systems to improve their performance by that is readable. Natural-language processing exposure to data, without the need to fol- comprises multiple techniques that may be low explicitly programmed instructions. At used together to achieve its goals. Language its core, machine learning is the process of models, a natural-language processing tech- automatically discovering patterns in data. nique, are used to predict the probability Once discovered, the pattern can be used to distribution of language expressions—the make predictions. For instance, presented with likelihood that a given string of characters or a database of information about credit-card words is a valid part of a language, for instance. transactions—such as date, time, merchant, Feature selection may be used to identify the merchant location, price, and whether the elements of a piece of text that may distinguish transaction was legitimate or fraudulent—a one kind of text from another—for instance, a machine-learning system recognizes patterns spam email versus a legitimate one. that are predictive of fraud. The more transac- tion data it processes, the better its predictions Network protocol: a set of rules that define are expected to become. how computers identify each other on a network. One example of a network proto- Machine-to-human (M2H) interfaces: a set of col is the Internet Protocol (IP) that offers technologies that enable machines to interact unique addresses to machines connected to with human beings. Some common examples the Internet. of M2H interfaces include wearables, home automation devices, and autonomous vehicles. Network: an infrastructure of hardware com- Based on the data collected and algorithmic ponents and software protocols that allows calculations, machines have the potential to devices to share data with each other. Networks convey suggestive actions to individuals who can be wired (e.g., Ethernet) or wireless (e.g., then exercise their discretion to take or not to Wi-Fi). take the recommended action.
41 Inside the Internet of Things (IoT)
Parallel processing: the concurrent processing Relational databases: a type of database that of data on clusters of computers in which each organizes data by establishing relationships computer offers local aggregation and storage. based on a unique identifier. Structured data stored in relational databases can be queried Personal area network (PAN): a network that using structured query language (SQL). extends to a small geographic range of at least 10 meters, such as inside a room. Devices can Sensor: a device that is used to “sense” a physi- connect to wireless PAN technologies such as cal condition or event. A sensor works by con- Bluetooth and ZigBee as well as wired PAN verting a non-electrical input into an electrical technologies such as Universal Serial Bus signal that can be sent to an electronic circuit. (USB). A sensor does not function by itself—it is a part of a larger system that comprises micro- Predictive analytics: the computational tools processors, modem chips, power sources, and that aim to answer questions related to “what other related devices. might be happening or could happen, given historical trends?” Predictive analytics exploits Speech recognition: a type of cognitive tech- the large quantity and the increasing variety nology that focuses on accurately transcribing of data to build useful models that correlate human speech. The technology has to handle sometimes seemingly unrelated variables. challenges such as diverse accents, background Predictive models are expected to produce noise, homophones (e.g., “principle” and more accurate results through machine learn- “principal”), speed of speaking, etc. Speech- ing, a process that refers to computer systems’ recognition systems use some of the same tech- ability to improve their performance by expo- niques as natural-language processing systems, sure to data without the need to follow explic- as well as others such as acoustic models that itly programmed instructions. describe sounds and their probability of occur- ring in a certain sequence in a given language. Prescriptive analytics: the computational tools that endeavor to answer questions Structured data: the data stored in predefined related to “What should one do to achieve a formats, such as rows and columns in spread- desired outcome?” based on data related to sheets. Structured data are generally stored in what has happened and what could happen. relational databases and can be queried using Prescriptive analytics includes optimization structured query language (SQL). techniques that are based on large data sets, business rules (information on constraints), Structured query language (SQL): a program- and mathematical models. Prescriptive ming language standardized by the American algorithms can continuously include new National Standards Institute as the querying data and improve prescriptive accuracy in language for relational databases in 1986. SQL decision optimizations. provides users a medium to communicate with databases and perform tasks such as data Real-time processing: the processing of data modification and retrieval. SQL aids aggrega- instantaneously upon receiving the data and/ tion, not only in centralized databases (all data or instruction. There is often the question of, stored in a single location) but also in distrib- “What data can be considered truly real?” uted databases (data stored on several comput- Ideally, data are valid the second they are ers with concurrent data modifications). generated; however, because of practical issues related to latency, the meaning of “real time” Transducer: a device that converts one form of varies from application to application. energy (electrical or not) into another (elec- trical or not). For example, a loudspeaker is
42 A primer on the technologies building the IoT
a transducer because it converts an electrical videos, webpages, emails, blog entries, and signal into a magnetic field and, subsequently, Word documents. into acoustic waves. Transducers refer to a broad category of devices that includes sensors Wide area network (WAN): a network that and actuators. spreads to a large area, say, beyond buildings and cities. WAN is an internetwork that is Unstructured data: the data that do not fit set up by connecting a number of local area into predefined formats. Common sources networks (LAN) through routers. The Internet of unstructured data include images, is an example of a WAN.
43 Inside the Internet of Things (IoT)
Endnotes
1. See glossary for a description of select IoT 10. Thingsee, The world’s first smart devel- terms. The terms described in the glossary oper device, Thingsee One, http://www. are italicized and in bold the first time they thingsee.com/Thingsee-One-Fact-sheet. appear in any given section of this report. pdf, accessed February 2, 2015. 2. Lee Simpson and Robert Lamb, IoT: 11. Wayne Walker, “Introduction to RADAR Looking at sensors, Jefferies Equity remote sensing for vegetation mapping Research, February 20, 2014, p. 4. and monitoring,” Woods Hole Research 3. “Smart clothes track health in pregnancy,” Center, http://www.whrc.org/education/ New Scientist, January 24, 2015, p. 22. Con- rwanda/pdf/Walker_SAR_Veg_Map- ductive silver fibers woven into maternity ping.pdf, accessed February 2, 2015. clothes can track both fetal and maternal 12. Jacob Fraden, Handbook of Modern Sensors: vital signs. “GE opens its big data platform,” Physics, Designs, and Applications, Fourth New York Times, October 9, 2014. Edition (Springer, April 2010), p. 13; Kalantar- 4. “3GPP approves LTE specifications,” zadeh, Kourosh, Fry, Benjamin, “Sensor ETSI, http://www.etsi.org/news-events/ characteristics and physical effects,” Nanotech- news/210-news-release-9th-january-2008?hi nology-Enabled Sensors (Springer, 2008), p. 13. ghlight=YToyOntpOjA7czozOiJsdGUi 13. Raynor and Cotteleer, “The O2k6MTtpOjIwMDg7fQ==, January 17, more things change.” 2008, accessed January 22, 2015; “LTE,” 14. John Greenough, “The Internet of every- GSMA, http://www.gsma.com/aboutus/gsm- thing: 2015 [slide deck]” Business Insider, technology/lte, accessed January 20, 2015. February 2, 2015, http://www.businessin- 5. For a detailed report on artificial intelligence sider.com/internet-of-everything-2015-bi- and cognitive technologies, refer to the 2014-12?op=1, accessed February 23, 2015. report “Demystifying artificial intelligence: 15. Simpson and Lamb, IoT: Looking at sensors, p. 4 What business leaders need to know about cognitive technologies,” Deloitte 16. Ibid. University Press, November 4, 2014, http:// 17. Shio Kumar Singh, M.P. Singh, and D. dupress.com/articles/what-is-cognitive- K. Singh, “Routing protocols in wireless technology/, accessed February 9, 2015. sensor networks: A survey,” International 6. David Rose, Enchanted Objects: Design, Journal of Computer Science & Engineering Human Desire, and the Internet of Things Survey (IJCSES) 1, no. 2 (2010), http://www. (New York: Simon & Schuster, 2014). airccse.org/journal/ijcses/papers/1110ijcses06. pdf, accessed February 23, 2015. 7. IEEE, “IEEE P1451.6 terms and definitions,” http://grouper.ieee.org/ 18. Powercast Corporation, “Lifetime power groups/1451/6/TermsDefinitions. wireless sensor system,” http://www.pow- htm#Sensor, accessed February 19, 2015. ercastco.com/PDF/wireless-sensor-system. pdf, 2012, accessed January 29, 2015. 8. Michael E. Raynor and Mark J. Cot- teleer, “The more things change: Value 19. Ibid. creation, value capture, and the Internet of 20. Gongbo Zhou et al., “Harvesting ambi- Things,” Deloitte Review 17, July 2015. ent environmental energy for wireless 9. “A closer look: Technical specifica- sensor networks: A survey,” Journal of tions,” Canary, http://canary.is/ Sensors 2014, article ID 815467 (2014), specs/, accessed February 2, 2015. http://dx.doi.org/10.1155/2014/815467, accessed February 23, 2015. 21. Ibid.
44 A primer on the technologies building the IoT
22. Nick Jones, Stephen Kleynhans, and controlled embedded applications,” Inter- Leif-Olof Wallin, Survey analysis: The national Journal of Electronics and Electrical Internet of Things is a revolution waiting Engineering 3, no. 4, August 2015. to happen, Gartner, January 19, 2015. 31. Robin Heydon and Nick Hunn, “Blue- 23. Hui Suo and Caifeng Zou, “Security in the tooth Low Energy,” CSR and WiFore. Internet of Things: A review,” Proceedings 32. Tauchmann and Sikora, “Experi- of the 2012 International Conference on ences and measurements with Bluetooth Computer Science and Electronic Engineer- Low Energy (BLE) enabled and smartphone ing (ICCSEE 2012) 3 (2012), pp. 648–651, controlled embedded applications.” doi: 10.1109/ICCSEE.2012.373. 33. Daniel M. Dobkin and Bernard Abous- 24. Muhammad Omer Farooq and Thomas souan, “Low power Wi-Fi (IEEE 802.11) for Kunz, “Operating systems for wireless sen- IP smart objects,” GainSpan Corporation, sor networks: A survey,” Department of 2009, http://www.gainspan.com/docs2/ Systems and Computer Engineering, Carleton Low_Power_Wi-Fi_for_Smart_IP_Ob- University Ottawa, Canada, May 31, 2011, jects_WP_cmp.pdf, accessed January 20, 2015. http://www.mdpi.com/1424-8220/11/6/5900/ pdf, accessed February 2, 2015. 34. “LTE,” GSMA. 25. Alessandro Ludovici, Pol Moreno, and Anna 35. “3GPP approves LTE specifications,” Calveras, “TinyCoAP: A novel Constrained ETSI, http://www.etsi.org/news-events/ Application Protocol (CoAP) implementa- news/210-news-release-9th-january-2008?hi tion for embedding RESTful Web services in ghlight=YToyOntpOjA7czozOiJsdGU wireless sensor networks based on TinyOS,” iO2k6MTtpOjIwMDg7fQ==, January Journal of Sensor and Actuator Networks 2, 17, 2008, accessed January 22, 2015. issue 2 (2013), DOI:10.3390/jsan2020288; 36. “LTE,” GSMA. Mahya Ilaghi, Tapio Leva, and Miika Komu, 37. We present both LTE and LTE-A as 4G “Techno-economic feasibility: Analysis of technologies as the idea here is to under- Constrained Application Protocol,” Proceedings stand how network technologies have of the IEEE World Forum on Internet of Things improved in terms of bandwidth rates and (WF-IoT), March 2014, http://www.leva.fi/wp- not get hung up with the dividing lines. content/uploads/CoAP-feasibility_wf-iot_ac- cepted_manuscript.pdf, accessed April 2, 2015. 38. James Temperton, “TV white space will connect the internet of things,” Wired, http:// 26. Logical Network Architecture Guidelines, www.wired.co.uk/news/archive/2015-02/13/ Commonwealth of Massachusetts Com- white-space-spectrum, February 13, mittee on Information Technology, July 2015, accessed February 14, 2015. 1998, http://www.mass.gov/anf/docs/itd/ policies-standards/logicalnetworkarchitec- 39. “What is Weightless,” Weightless, http:// tureguidelines.pdf, accessed April 1, 2015. www.weightless.org/about/what-is- weightless, accessed January 20, 2015. 27. Navpreet Kaur and Sangeeta Monga, “Com- parisons of wired and wireless networks: A 40. Ibid. review,” International Journal of Advanced 41. A gateway connects one network to Engineering Technology V, issue II, (2014), another through a combination of http://www.technicaljournalsonline.com/ hardware and software components. ijeat/VOL%20V/IJAET%20VOL%20V%20 ISSUE%20II%20APRIL%20JUNE%20 42. “Wavelength division multiplexing,” RP 2014/Article%2009%20V%20II%20 Photonics, http://www.rp-photonics. 2014.pdf, accessed February 2, 2015. com/wavelength_division_multiplexing. html, accessed February 5, 2015. 28. “History of the Bluetooth special interest group,” Bluetooth, http://www.bluetooth. 43. Por Phil Smith, “Comparing low-power com/Pages/History-of-Bluetooth. wireless technologies,” Digi-Key Electron- aspx, accessed January 19, 2015. ics, August 8, 2011, http://www.digikey. com/es/articles/techzone/2011/aug/ 29. “A look at the basics of Bluetooth technology,” comparing-low-power-wireless-technologies, Bluetooth, http://www.bluetooth.com/Pages/ accessed March 30, 2015; John Donovan, Basics.aspx, accessed January 20, 2015. “Bluetooth goes ultra-low-power,” Digi-Key 30. David Tauchmann and Axel Sikora, “Experi- Electronics, http://www.digikey.com/en/ ences and measurements with Bluetooth articles/techzone/2011/dec/bluetooth-goes- Low Energy (BLE) enabled and smartphone ultra-low-power, accessed April 1, 2015.
45 Inside the Internet of Things (IoT)
44. Cisco, “Cisco Global Cloud Index: Forecast 60. Stephen Lawson, “Intel-backed OIC ad- and methodology, 2013–2018,” 2014. vances in fast-moving IoT standards race,” 45. Ibid. PCWorld, January 14, 2015, http://www. pcworld.com/article/2870872/intelbacked- 46. “Understanding 5G: Perspectives on future oic-advances-in-fastmoving-iot-standards- technological advancements in mobile,” GSMA race.html, accessed March 9, 2015. Intelligence, December 2014, https://gsmaintel- ligence.com/files/analysis/?file=141208-5g. 61. Caroline Gabriel, “As we feared, OIC pdf, accessed January 22, 2015. IoTivity is going head-to-head with AllSeen,” ReTHINK Research, January 47. “Understanding 5G: Perspectives on future 15, 2015, http://www.rethinkresearch.biz/ technological advancements in mobile,” GSMA articles/feared-oic-iotivity-going-head- Intelligence, December 2014, https://gsmaintel- head-allseen/, accessed March 25, 2015. ligence.com/files/analysis/?file=141208-5g. pdf, accessed January 22, 2015. 62. Please note that these are representative exam- ples of alliances for development of IoT stan- 48. FreeS/WAN, “The IPSEC proto- dards. There are many other bodies working to cols,” http://www.freeswan.org/ develop standards for IoT implementations. freeswan_trees/freeswan-1.91/doc/ ipsec.html, accessed March 11, 2015. 63. ETSI, “Connecting things,” http://www. etsi.org/technologies-clusters/clusters/ 49. ISO, “Standards,” http://www.iso.org/iso/home/ connecting-things, accessed March 12, 2015. standards.htm, accessed March 4, 2015. 64. Stephen Lawson, PCWorld, “IEEE 50. Internet of things: Privacy & secu- standards group wants to bring order rity in a connected world, Federal to IoT,” September 19, 2014, http:// Trade Commission, January 2015. www.pcworld.com/article/2686692/ 51. Ibid. ieee-standards-group-wants-to-bring-order- to-iot.html, accessed March 25, 2015. 52. Ibid. 65. Ameya Nayak, Anil Poriya, and Dikshay 53. AllSeen Alliance, “Allseen FAQs,” Poojary, “Type of NoSQL databases and its https://allseenalliance.org/about/ comparison with relational databases,” Interna- faqs, accessed March 9, 2015. tional Journal of Applied Information Systems 54. Extract, transform, and load big data 5, no. 4 (2013), DOI: 10.5120/ijais12-450888. with Apache Hadoop, Intel, 2013, https:// 66. Magdalena Balazinska, Bill Howe, and Dan software.intel.com/sites/default/files/ Suciu, “Data markets in the cloud: An opportu- article/402274/etl-big-data-with-hadoop. nity for the database community,” Proceedings pdf, accessed March 13, 2015. of the VLDB Endowment 4, no. 12 (2011). 55. “Database language SQL,” http://www. 67. Edith Ramirez et al., Data brokers: A itl.nist.gov/div897/ctg/dm/sql_info. call for transparency and accountability, html, accessed March 29, 2015. Federal Trade Commission, May 2014. 56. Hadoop, “Welcome to Apache Hadoop,” 68. Ben Woo, “Combating the big data skills https://hadoop.apache.org/, accessed shortage,” Forbes, January 18, 2013, http:// March 25, 2015. Parallel processing is www.forbes.com/sites/bwoo/2013/01/18/ the process of dividing and executing combating-the-big-data-skills- instructions on multiple processors si- shortage/, accessed March 30, 2015. multaneously to reduce execution time. 69. Ibid. 57. Ibid. In-memory processing refers to the process of storing data in random access 70. David Schatsky, Craig Muraskin, and Ragu memory (RAM) instead of hard disks; this Gurumurthy, Demystifying artificial intel- enables quicker querying process, data ligence: What business leaders need to know retrieval, and faster data visualizations. about cognitive technologies, Deloitte University Press, November 4, 2014, http://dupress. 58. Ibid. com/articles/what-is-cognitive-technology/, 59. “Why AllSeen,” AllSeen Alliance, https:// accessed February 9, 2015. Note that the allseenalliance.org/about/why-allseen, authors draw an interesting relationship accessed February 24, 2015. between artificial intelligence and cognitive technologies. They define artificial intel- ligence (AI) as the theory and development of computer systems able to perform tasks
46 A primer on the technologies building the IoT
that normally require human intelligence. The 79. Stuart Russell and Peter Norvig, field of artificial intelligence has produced a Artificial Intelligence, third edition number of cognitive technologies. Cognitive (Saddle River: Prentice Hall, 2010). technologies are technologies that are able to 80. C. H. Chen, Computer Vision in Medi- perform tasks that only humans used to be cal Imaging (Singapore: World Scientific able to do, such as computer vision, natural- Publishing Company, 2014). language processing, speech recognition, etc. 81. Russell and Norvig, Artificial Intel- 71. James Guszcza and John Lucker, Beyond the ligence, third edition. numbers: Analytics as a strategic capabil- ity, Deloitte University Press, January 1, 82. Ibid. 2011, http://dupress.com/articles/beyond- 83. Bruce Horovitz, “Domino’s app lets you the-numbers-analytics-as-a-strategic- voice-order pizza,” USA Today, June 17, capability/, accessed March 23, 2015. 2014, http://www.usatoday.com/story/ 72. Thomas H. Davenport and Jeanne G. Harris, money/business/2014/06/16/dominos-voice- Competing on Analytics: The New Science ordering-app-nuance-fast-food-restau- of Winning (Boston: Harvard Business rants/10626419/, accessed October 3, 2014. School, 2007) p. 7; Thomas H. Davenport, 84. R is an open-source software environment “The three ‘..tives’ of business analytics; for statistical analysis. R packages include R predictive, prescriptive and descriptive,” functions, data sets, and underlying codes November 16, 2012, 5:04, http://www. that are available in a usable format. “Pack- enterprisecioforum.com/en/video/three-tives- ages,” http://www.statmethods.net/interface/ business-analytics-predictiv?q4851921=1, packages.html, accessed March 23, 2015. accessed February 16, 2015. 85. Thomas H. Davenport and John Lucker, 73. Viktor Mayer-Schonberger and Kenneth “Running on data: Activity trackers and Cukier, Big Data: A Revolution That Will the Internet of Things,” Deloitte Review 16, Transform How We Live, Work, and Think January 2015, http://dupress.com/articles/ (London: John Murray, 2014); Chris internet-of-things-wearable-technology/. Anderson, “The end of theory: The data 86. Mohd. Saboor and Rajesh Rengasamy, deluge makes the scientific method obsolete,” Designing and developing complex event Wired, June 23, 2008, http://archive.wired. processing applications, Sapient com/science/discoveries/magazine/16-07/ Global Markets, August 2013. pb_theory, accessed February 16, 2015. 87. Julian Clark, “Complex event processing 74. Schatsky, Muraskin, and Gurumurthy, and the future of business decisions,” http:// Demystifying artificial intelligence. www.complexevents.com/2012/07/12/ 75. Viktor Mayer, “What’s to be done about big complex-event-processing-and-the- data?,” Forbes, November 3, 2013, http:// future-of-business-decisions/, July www.forbes.com/sites/gilpress/2013/03/11/ 12, 2012, accessed April 28, 2015. whats-to-be-done-about-big-data/, 88. Ibid. accessed February 16, 2015. 89. Alexis Kleinman, “Facebook apologizes to 76. Gary Marcus, “Steamrolled by big data,” bereaved father for ‘year in review’,” Huff- http://www.newyorker.com/tech/elements/ ington Post, December 12, 2014, http:// steamrolled-by-big-data, The New Yorker, www.huffingtonpost.com/2014/12/28/ March 29, 2013, accessed February 16, 2015. facebook-year-in-review_n_6387098. 77. Davenport and Harris, Competing on html?ir=India, accessed February 10, 2015. Analytics, p. 7; Davenport, “The three ‘..tives’ 90. Ameya Nayak, Anil Poriya, and Dikshay of business analytics; predictive, prescrip- Poojary, “Type of NoSQL databases and its tive and descriptive,” 5:04, http://www. comparison with relational databases,” Interna- enterprisecioforum.com/en/video/three-tives- tional Journal of Applied Information Systems business-analytics-predictiv?q4851921=1, 5, no. 4 (2013), DOI: 10.5120/ijais12-450888. accessed February 16, 2015. 91. John Gantz and David Reinsel, The 78. For a detailed report on artificial intel- Digital digital universe decade: Are you ligence and cognitive technologies, refer ready?, IDC, May 2010, http://www. to Schatsky, Muraskin, and Gurumurthy, geospatialworldforum.org/2012/gwf_PDF/ Demystifying artificial intelligence. steven.pdf, accessed February 16, 2015.
47 Inside the Internet of Things (IoT)
92. Adopting Hadoop in the enterprise: Typical 108. Timothy Aeppel, “What clever robots enterprise use cases, Indus Corp., http:// mean for jobs,” Wall Street Journal, www.induscorp.com/sites/indus/files/ February 24, 2015, http://www.wsj.com/ uploads/adoptinghadoopintheenterprise. articles/what-clever-robots-mean-for- pdf, accessed March 30, 2015. jobs-1424835002, accessed March 3, 2015. 93. Wayne Eckerson, Big data analytics: Profiling 109. Computers and robots: Decision-makers in the use of analytical platforms in user organiza- an automated world, Stanford University, tions, Beye Network, 2011, http://www.cin. http://cs.stanford.edu/people/eroberts/cs181/ ufpe.br/~tcmn/DW/Semin%E1rio/pentaho- projects/2010-11/ComputersMakingDeci- document.pdf, accessed March 30, 2015. sions/index.html, accessed March 3, 2015. 94. Raynor and Cotteleer, “The more things 110. Ibid. change.” 111. Georgia Tech Research Institute, Case study: 95. Peter Kelly-Detwiler, “Machine to machine Artificial intelligence: Creating machines connections - the Internet of Things - and that can make complex decisions, http:// energy,” Forbes, August 6, 2013, accessed www.gtri.gatech.edu/casestudy/artificial- February 23, 2015. intelligence, accessed March 3, 2015. 96. Raynor and Cotteleer, “The more things 112. Richard H. Thaler and Cass R. Sunstein, change.” Nudge: Improving Decisions about 97. Ibid. Health, Wealth, and Happiness, (New Haven: Yale University Press, 2008). 98. Ibid. 113. Ibid. 99. “Robotics has a long history in Australia,” PACE, November 5, 2013, http://www. 114. For a more extended discussion, see the pacetoday.com.au/features/evolution-of- “In 3D: Data meets digital meets design” robotics-in-australia, accessed March 4, 2015. section of “The last mile problem:How data science and behavioral science can work 100. RobotWorx, “History of industrial ro- together” by James Guszcza, Deloitte Review, bots,” http://www.robots.com/education/ January 2015, http://dupress.com/articles/ industrial-history, accessed March 4, 2015. behavioral-economics-predictive-analytics/. 101. Ibid. 115. Thomas Goetz, “Harnessing the power 102. “Robotics has a long history in Australia,” of feedback loops,” Wired, June 19, 2011, PACE, November 5, 2013, http://www. http://www.wired.com/2011/06/ff_feed- pacetoday.com.au/features/evolution-of- backloop/, accessed April 29, 2015. robotics-in-australia, accessed March 4, 2015. 116. Rose, Enchanted Objects. 103. A.Rethinavelsubramanian, “Design and analy- 117. Mitesh S. Patel, David A. Asch, and Kevin sis of Autonomous Robots,” International Jour- G.Volpp, “Wearable devices as facilitators, not nal of Engineering Research and General Science drivers, of health behavior change,” Journal of volume 3, issue 1 (2015), ISSN 2091-2730. Medical Association vol. 313, issue 5 (2015): 104. Dan O.Popa, Introduction to robotics, pp. 459–460, DOI:10.1001/jama.2014.14781. http://www.docstoc.com/docs/121247889/ 118. “First, wash your hands,” Economist, July Behavior-Based-Roboticsppt, University 17, 2013, http://www.economist.com/news/ of Texas, 2006, accessed March 4, 2015. technology-quarterly/21584441-biomedicine- 105. Fidelity Worldwide Investment, 21st century smart-antiseptic-dispensers-promise-save- themes: Automation and robotics, July 2014. lives-subtly-encouraging, accessed May 1, 2015. 106. Marc Weber, “Where to? A history of autono- mous vehicles,” http://www.computerhistory. 119. Ibid. org/atchm/where-to-a-history-of-autonomous- 120. Natasha Lomas, “Today in creepy privacy vehicles/, accessed March 30, 2015. policies, Samsung’s eavesdropping TV,” Tech 107. David Schatsky and Jeff Schwartz, Machines Crunch, February 8, 2015, http://techcrunch. as talent: Collaboration, not competi- com/2015/02/08/telescreen/, accessed March tion, Deloitte University Press, February 12, 2015. 27, 2015, http://dupress.com/articles/ 121. David Rose, Enchanted Objects. cognitive-technology-in-hr-human-capital- trends-2015/, accessed March 3, 2015.
48 A primer on the technologies building the IoT
122. Melanie Swan, “Sensor Mania! The Internet 124. “Why some CEOs are so skeptical of analytics,” of Things, Wearable Computing, Objective Wall Street Journal, http://deloitte.wsj.com/ Metrics, and the Quantified Self 2.0,” Journal cio/2012/06/05/403/, accessed April 24, 2015. of Sensor and Actuator Networks, Volume 1, 125. Ibid. (2012): p. 217-253; DOI:10.3390/jsan1030217. 123. Rose, Enchanted Objects.
49 Inside the Internet of Things (IoT)
Contacts
Michael Raynor Mark Cotteleer Director Director Deloitte Services LP Deloitte Services LP [email protected] [email protected]
50
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