Chapter 14 Wireless Sensor Nodes

Chapter 14 Wireless Sensor Nodes Serge Chaumette and Damien Sauveron Abstract This chapter addresses the key points of wireless sensor nodes: applications, constraints, architecture, operating systems, and security concerns. It does not pretend to be exhaustive but to provide the major references on these topics. 14.1 Introduction Huge advances in Microelectromechanical systems (MEMS) and Wireless commu- nications in the last decade of the twentieth century gave birth to new paradigms, where cheap, small size communicating sensors have been developed and integrated in many devices and large hardware/software environments. In this chapter, we target standalone wireless sensing devices, so-called wireless sensor nodes, which means that we focus on the device itself and not on the way it can be integrated within a global wireless sensor network. These small hardware pieces are becoming a key component of the digitization of the real world, thanks to their ease of deployment and the benefits that they can bring to human life in general, like infrastructure management (such as power grids) and environmental protection for instance. They are quite different from expensive isolated sensors (i.e., not intended to be a part of a whole swarm) and achieve complex measurements (related to a given phenomenon), and subsequent computation operations. Indeed, the strength of small sensor nodes is their ability to self-organize as a large network which enables measurement very close to a possibly dangerous phenomenon. They can cover a wide area and so are able to observe the evolution and spreading of complex events. In Sect.14.2 we S. Chaumette (B) LaBRI,University of Bordeaux, Bordeaux, France e-mail: [email protected] D. Sauveron University of Limoges, Limoges, France e-mail: [email protected] K. Markantonakis and K. Mayes (eds.), Secure Smart Embedded Devices, 335 Platforms and Applications, DOI: 10.1007/978-1-4614-7915-4_14, © Springer Science+Business Media New York 2014 336 S. Chaumette and D. Sauveron will show that wireless sensor nodes are used in both military and civil applications, health care, and power grid monitoring, among many other domains. In Sect.14.3 we address constraints, like cost, energy-consumption, and network management (even though, as explained above, this last point is not a central topic of this chapter). In Sect. 14.4 the generic architecture of a wireless sensor node and the major features of the associated operating systems are described. We eventually present the security concerns in Sect.14.5. 14.2 Applications It is widely acknowledged that the first uses of advanced sensing technologies were military. In 1949, the US Navy announced its intention to exploit passive sonars for anti-submarine warfare (ASW) purposes using hydrophonic sensors deployed on the seafloor. In 1961, the sound surveillance system (SOSUS) [4] provided deep- water long-range detection capabilities, which were successfully used during the Cold War. It is now used by national oceanographic and atmospheric administration (NOAA) to observe a number of natural phenomena like submarine earthquakes [51] or the activities of some animals [22]. However, the concept of Distributed Sensor Networks only appeared in the early 1980’s, driven by defense advanced research projects agency (DARPA) through a first program which was followed in 2001, due to evolution in the domain of MEMS, by the sensor information technology (SensIT) program [43]. The primary goals of SensIT were the creation of a new class of software for distributed microsensors, the development of novel methods for ad hoc networking of deployable microsensors and the extraction of right and timely information from a sensor field. Typical military applications of sensors are related to the collection of information that can be used to enforce situation management: • monitoring friendly forces, equipment, and ammunition; • battlefield surveillance; • reconnaissance of opposing forces and terrain; • targeting (e.g., for missiles); • battle damage assessment; • nuclear, biological, and chemical attack detection and reconnaissance. Since early 2000, the number of civil applications has increased. Sensors now permit the creation of low-cost monitoring systems. Many domains, for instance biology, climatology, geology, etc., benefit from integrated sensors and sensor network technology. These sensors can for instance be used: • to alert the civilian population when a volcanic eruption is going to occur [67] and to collect data without any risk to compromise the physical integrity of the persons in charge of taking the samples [65]; • to detect forest fire [37] or floodings [15]; • to forecast environmental pollution; 14 Wireless Sensor Nodes 337 • to observe animals in their living environment without human presence [42, 49]; • to help farmers collect accurate measures of phenomena, which have a potential impact on their agricultural production [16]. As mentioned in the introduction, sensors can also be used in: • health care, like telemonitoring of human physiological data [41], tracking and monitoring doctors and patients inside a hospital, environmental control in office buildings [54], etc.; • infrastructure protection by monitoring bridges, tunnels, pipelines, power grids, etc.; • home automation and smart environments [32], interactive museums [54], vehicle tracking and detection [60], etc. Beyond these well-known applications, some other are much more unusual. For example, Simon et al. [61] propose to use a large number of cheap sensors communicating through an ad hoc wireless network, to detect and accurately locate shooters in urban environments. Their system supports multiple sensors failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. 14.3 Constraints As illustrated in the above section, Wireless Sensor Nodes can be used for an extremely wide range of applications. However, they suffer from several constraints that need to be known and understood in order to properly target their possible domains of use. 14.3.1 Costs: Production Versus Performance When a phenomenon needs to be observed, the decision to use wireless sensor nodes or classical sensors is mostly based on technical reasons. For instance, when consider- ing the ability of the system to observe the spreading and evolution of a phenomenon to measure, it appears that a wireless sensor network is probably a good candidate because it allows wider and more accurate geographic coverage. Nevertheless, the financial cost of building the sensing system is also a prominent parameter. A wireless sensor network can be composed of a few to several thousands1 devices and the overall price of the sensing system may thus be huge. To be viable, their production 1 In practice very few applications use thousands of nodes. However theoretically, for instance, in military applications like battlefield surveillance, a huge number of nodes may be required. The biggest wireless sensor networks publicly known that have ever been built are: a system based on MyriaNed and composed of more than 1000 nodes [14, 38]; an 800 nodes network called the “Largest Tiny Network Yet” deployed at Berkeley [5] in 2001; WISEBED [21] which is made of 338 S. Chaumette and D. Sauveron cost should remain very low (for example less than 1$/node). Indeed, even if not all networks are composed of thousands of nodes, there can be several networks operating in parallel for different purposes and thus the total number of nodes can still be huge. At the same time, they should offer good performance to sense phenomena and if required enough computing power to handle data locally, so as to overcome the network-related constraints and usage consequences such as energy-consumption (see below). Thus, their design is always the result of a trade-off between performance and cost. Obviously there is no hidden cost due to the deployment of wires (and associated devices) as can be found in classical networks, since by definition wireless sensor nodes do not require any infrastructure, however, in some cases there may be a cost for a wireless sensors spectrum licence. 14.3.2 Energy To enable the sensors to operate during their whole mission time, energy saving is a major concern of both the manufacturers and the users, even though it is widely acknowledged that designing energy efficient communication components is a chal- lenging task. Furthermore, it still remains that even with a power-efficient radio fre- quency (RF) technology, the energy consumption due to the communication between two nodes follows at best an inverse-square law of their distances. For this reason, to save power, it is needed to ‘split’ long distances between two communicating nodes by using multi-hop communication and routing [24]. Software is thus also largely involved in the energy-saving process. However, this way to communicate implies more cooperation between the nodes of the network in order to relay messages using power-efficient routing algorithms. It is thus a challenge to design low duty cycle radio circuits to relay messages between neighbors without losing any message. Strategies based on “wakeup on demand” (using two radios) [59] or “adaptive duty cycles” [68] are being studied to circumvent this problem. Another solution is to setup a network backbone with a subset of nodes that remain active. In addition, it is possible to minimize [8] power consumption in a multi-hop sensor network, by processing sensed data locally to minimize transmission. The economy comes from the fact that the cost to transmit a large quantity of raw data coming from the sensor is more important than the cost of locally processing them, extracting the useful information, and eventually sending these information over the wireless network. Even if it is true that communication consumes a large quantity of energy in a sensor architecture, it still remains that the rest of the hardware and especially the processing unit must also be designed to be efficient. At software level, the operating system, protocols, algorithms, etc., must also be power-efficient and thus power-aware.

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