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ORBIT: a Smartphone-Based Platform for Data-Intensive Embedded Sensing Applications

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ORBIT: a Smartphone-Based Platform for Data-Intensive Embedded Sensing Applications ORBIT: A Smartphone-Based Platform for Data-Intensive Embedded Sensing Applications Mohammad-Mahdi Moazzami∗ Dennis E. Phillips∗ Rui Tan† Guoliang Xing∗ Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA Advanced Digital Sciences Center, Illinois at Singapore ABSTRACT 1. INTRODUCTION Owing to the rich processing, multi-modal sensing, and versatile The ubiquity of smartphones and their multi-modal sensing ca- networking capabilities, smartphones are increasingly used to build pabilities have enabled a wide spectrum of mobile sensing appli- data-intensive embedded sensing applications. However, various cations. These applications are usually human-centric in that the challenges must be systematically addressed before smartphones smartphone utilizes on-board sensors to sense people and character- can be used as a generic embedded sensing platform, including istics of their contexts. Different from these human-centric sensing high power consumption, lack of real-time functionality and user- applications, this paper considers an emerging class of smartphone- friendly embedded programming support. This paper presents OR- based data-intensive embedded sensing applications. In contrast to BIT, a smartphone-based platform for data-intensive embedded se- the people-centric nature of participatory sensing, smartphones in nsing applications. ORBIT features a tiered architecture, in which these applications are embedded into environments to sense and a smartphone can interface to an energy-efficient peripheral board interact with the physical world autonomously over long periods and/or a cloud service. ORBIT as a platform addresses the short- of time. For instance, in the Floating Sensor Network project [9], comings of current smartphones while utilizing their strengths. OR- smartphone-equipped drifters are rapidly deployed to collect real- BIT provides a profile-based task partitioning allowing it to intel- time data about the flow of water through a river. The smartphone’s ligently dispatch the processing tasks among the tiers to minimize GPS allows the drifter to measure volume and direction of wa- the system power consumption. ORBIT also provides a data pro- ter flow based on its real-time location and transmit the data back cessing library that includes two mechanisms namely adaptive de- to the server through cellular networks. Smartphones have also lay/quality trade-off and data partitioning via multi-threading to op- been employed for monitoring earthquakes [7], volcanoes [13], and timize resource usage. Moreover, ORBIT supplies an annotation even operating miniature satellites [22]. Another important class of based programming API for developers that significantly simplifies smartphone-based embedded systems is cloud robots [11] [4]. By the application development and provides programming flexibility. integrating smartphones, these robots can leverage a plethora of Extensive microbenchmark evaluation and two case studies includ- phone sensors to realize complex sensing and navigation capabil- ing seismic sensing and multi-camera 3D reconstruction, validate ities and offload compute-intensive cognitive tasks like image and the generic design of ORBIT. voice recognition to the cloud. Compared with the traditional mote-class sensing platforms, sm- artphones have several salient advantages that make them promis- Categories and Subject Descriptors ing system platforms for the aforementioned embedded applica- C.3 [Special-Purpose and Application-Based Systems]: Real- tions. These features include high-speed multi-core processors that time and embedded systems, signal processing systems are capable of executing advanced data processing algorithms, mul- tiple network interfaces, various integrated sensors, friendly user Keywords interfaces and advanced programming languages. Moreover, the price of smartphones has been dropping significantly in the last Smartphone, embedded sensing, data processing, data-intensive ap- decade. Many Android phones with reasonable configurations (up plications to 800 MHz CPU and 2 GB memory) cost less than US$50 [18]. However, several challenges must be addressed before smart- phones can be used as a system platform for embedded sensing applications. First, the smartphones, which are designed to provide several days of battery life, are ill-suited for many embedded sens- ing applications that must operate unattended for long periods of Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies time. Many of today’s embedded applications are inherently data- are not made or distributed for profit or commercial advantage and that intensive in that sensors must sample at high rates (e.g., 100 Hz copies bear this notice and the full citation on the first page. Copyrights in seismic sensing [29]). The continuous sensor sampling can pre- for components of this work owned by others than ACM must be honored. vent the smartphone from entering sleep state, leading to battery Abstracting with credit is permitted. To copy otherwise, or republish, to depletion in a few hours. Moreover, the current major smartphone post on servers or to redistribute to lists, requires prior specific permission operating systems (OSes) do not provide real-time functionalities, and/or a fee. Request permissions from [email protected]. such as constant sampling rate, precise timestamping, and program- IPSN’15, April 14-16, 2015, Seattle, WA, USA ming interfaces for expressing timing requirements, which are cru- Copyright 2015 ACM 978-1-4503-3475-4/15/04$15.00 cial to many embedded sensing applications. For instance, our http://dx.doi.org/10.1145/2737095.2737098. measurements show that the USB hardware interrupt of Android end-to-end sensing and processing platform for smartphones-based phones suffers an unpredictable delay of up to 5 ms, which makes data-intensive embedded applications 1. Lastly, we demonstrate it impossible to achieve a high constant sampling rate. Lastly, the the generality and flexibility of ORBIT as a platform by presenting smartphone programming environment, although simplifying many our experience in prototyping two applications upon ORBIT: seis- programming tasks in the life cycle of embedded systems such as mic sensing and multi-camera 3D reconstruction. The flexible task debugging, remote data logging, visualization, and software main- partitioning and dispatching framework allows ORBIT to adapt to tenance, lacks important embedded programming support such as different task structures, application deadlines, and communication resource-efficient signal processing libraries and communication/- delays. The experiments show ORBIT reduces energy consump- control primitives for peripheral sensors. tion by up to 50% compared to baseline approaches. In this paper, we take the first step toward addressing these chal- lenges collectively. We present ORBIT, a smartphone-based plat- 2. RELATED WORK form for embedded sensing systems. In particular, ORBIT lever- ages off-the-shelf smartphones to meet the energy-efficiency and Mobile sensing based on smartphones has recently received sig- timeliness requirements of data-intensive embedded sensing appli- nificant interests. Most studies focus on the issues related to human- cations. ORBIT is based on a tiered architecture that comprises up centric context, including coordination among multiple concurrent to three tiers: the cloud, the smartphone, and one or more energy- sensing applications [16, 17, 15] and sensing algorithms such as efficient peripheral boards (referred to as extBoard) that are inter- context classifiers [3]. Recently, smartphones have been used in faced with the smartphone. A number of extBoard platforms are a number of embedded sensing applications. In [7], smartphones currently available, such as Arduino [1] and IOIO [14]. There- are used to build an earthquake early warning system using an on- fore, if the built-in sensors on the smartphones are not suitable board accelerometer. In the Floating Sensor Network project [9], for sensing applications, these boards can readily integrate vari- smartphone-equipped drifters are deployed to monitor waterways ous accessories, such as external sensors, to an Android phone via and collect real-time volume and direction of water flow based on USB or bluetooth interface. We conduct a measurement study on the phone’s GPS. The NASA PhoneSat project [22] has launched the latency and power consumption of Android smartphones and low-cost satellites equipped with Android smartphones. Controlled extBoard platforms. Our results show that the two platforms have by a smartphone, such small satellites could perform various tasks highly heterogeneous but complementary power/latency profiles: such as earth observation and space debris tracking. Several re- smartphone features higher energy efficiency due to its faster pro- cent efforts focus on building cloud robots [11] that integrate smart- cessing capability while yielding poor timing accuracy due to the phones with robots. The phone’s built-in sensors are used for sens- overhead of OS. These results have important implication for ef- ing and navigation, while compute-intensive tasks like image and ficient task partitioning. In particular, while the smartphone and voice recognition are offloaded to the cloud. cloud should handle long-running compute-intensive tasks, time- Various task offloading schemes for smartphones have been de- critical functions such as high-rate sensor
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