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Wireless Sensor Networks and Real-World Applications Nirupama Bulusu Portland State University

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1 Wireless Sensor Networks and Real-World Applications Nirupama Bulusu Portland State University

2 On Sensor Networks “One of the 10 technologies that will change the world”, MIT Technology Review, 2003 “More than half a billion sensor nodes will ship for wireless sensor applications in 2010 for an end-user market worth at least $7 billion” “Demand growing at 300% between 2004 and 2005”. ON World, a wireless research firm.

3 Burgeoning Research and Commercial Activity NSF Research Centers Center for Embedded Networked Sensing More than 100 Companies (many started after 2003) Crossbow, Sensoria, Millennial Net, Ember Corporation, Dust Networks, Chipcon, Arched Rock Corporation, Moteiv

4 Push: Technology Trends Moore’s law Energy capacity miniaturization Micro-electro Mechanical Systems (MEMS) System-on-chip Integration

5 Wireless Sensor Networks Sensing Computing Communication Micro-sensors, on-board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close” Enables spatially and temporally dense environmental monitoring

6 State-of-the-Art Telos Mote (Source: David Culler, Berkeley)

7 Pull: Real-world Applications Most applications fall into of one of three categories* –Monitoring Space –Monitoring Objects –Monitoring Interactions of Objects and Space * Classification due to Culler, Estrin, Srivastava

8 Monitoring Space Environmental and Habitat Monitoring Precision Agriculture Indoor Climate Control Military Surveillance Treaty Verification Intelligent Alarms

9 Example: Precision Agriculture The “Wireless Vineyard” –Sensors monitor temperature, moisture –Roger the dog collects the data Source: Richard Beckwith, Intel Corporation

10 Monitoring Objects Structural Monitoring Eco-physiology Condition-based Maintenance Medical Diagnostics Urban terrain mapping

11 Example: Condition-based Maintenance Intel fabrication plants –Sensors collect vibration data, monitor wear and tear; report data in real- time –Reduces need for a team of engineers; cutting costs by several orders of magnitude

12 Monitoring Interactions between Objects and Space Wildlife Habitats Disaster Management Emergency Response Ubiquitous Computing Asset Tracking Health Care Manufacturing Process Flows

13 Example: Habitat Monitoring The ZebraNet Project: Collar-mounted sensors monitor zebra movement in Kenya Source: Margaret Martonosi, Princeton University

14 Data Base station (car or plane) Data Store-and-forward communications Data Tracking node with CPU, FLASH, radio and GPS Sensor Network AttributesZebraNetOther Sensor Networks Node mobilityHighly mobileStatic or moderate mobile Communication rangeMilesMeters Sensing frequencyConstant sensingSporadic sensing Sensing device powerHundreds of mWTens of mW

15 The Computing Challenge Build Robust, Long-lived systems that can be un- tethered (wireless) and unattended Communication will be the persistent primary consumer of scarce energy resources (MICA Mote: 720nJ/bit xmit, 4nJ/op) Autonomy requires robust, adaptive, self-configuring systems Leverage data processing inside the network Exploit computation near data to reduce communication, achieve scalability Collaborative signal processing Achieve desired global behavior with localized algorithms (distributed control)

16 Some Problems Calibration = correcting systematic errors in sensor data Causes: manufacturing, environment, age, crud Localization = establish spatial coordinates for sensors and target objects Power-aware Networking low-power media access; power-aware routing of data packets Macro-programming= high-level program for a sensor network; not low-level programs for individual sensors

17 In-depth: Localization

18 Mathematically Given: x i, c ij for some i, j € {1, …N} Estimate: x s for any s 1 (0,0,0) 2 3 4(100,0,0) C 23 = 5 C 5.11 = 5

19 Localization System Components Coordinate System Synthesis Parameter Estimation Filtering Parameter Estimation Filtering Parameter Estimation Filtering Parameters might include: Range between nodes Angle between nodes Psuedo-range to target (TDOA) Bearing to target (TDOA) Absolute orientation of node Absolute location of node (GPS) Coordinate System Synthesis Parameter Estimation Filtering Parameter Estimation Filtering Parameter Estimation Filtering Stitching and Refinement This step applies to distributed construction of large-scale coordinate systems This step estimates target coordinates (and often other parameters simultaneously)

20 Example of a Localization System* SHM system, developed at Sensoria Corp. 12 cm Microphone Speaker Each node has 4 speaker/ microphone pairs, arranged along the circumference of the enclosure. The node also has a radio system and an absolute orientation sensor that senses magnetic north. Source: Lewis Girod, UCLA

21 System Architecture Ranging between nodes based on detection of coded acoustic signals, with radio synchronization to measure time of flight Angle of arrival is determined through TDOA and is used to estimate bearing, referenced from the absolute orientation sensor An onboard temperature sensor is used to compensate for the effect of environmental conditions on the speed of sound

22 System Architecture Nodes periodically emit acoustic pulses. Other nodes detect these pulses and compute a range and angle of arrival. Range data, angle data, and absolute orientation are broadcast N hops away. Based on this table of ranges, angles, and orientations, each node applies a multi-lateration algorithm with iterative outlier rejection to compute a consistent coordinate system. Range, Angular Data Multilat Engine

23 In-depth: Cane-toad Monitoring Joint work with colleagues at University of New South Wales, Australia

24 Figures of Cane ToadCane Toads’ Distribution in Australia (2003)

25 Objective In-expensive real-time monitoring system (set up and maintenance cost) to detect Cane toads and their impact (Presence and Area)

26 Detecting Frogs by Their Calls Acoustic features can be used to distinguish the vocalizations of different amphibians. (call rate, call duration, amplitude-time envelope, waveform periodicity, pulse- repetition rate, frequency modulation, frequency and spectral patterns.) Frog 1 Frog 2 Frog 3 (Cane toad) Waveform Figures of Three Different Frogs’ Calls

27 How Our System Works Quinlan’s machine learning system, C4.5 used to build classifiers. Our system examines each slice of the spectrogram (1 millisecond) and tries to estimate frequency local peaks. Input acoustic signal is converted into a spectrogram of time- frequency pixels by a Fast Fourier Transform (FFT) algorithm. Frog 1 Frog 2 Frog 3 (Cane toad) Spectrogram Figures of Three Different Frog’s Calls Frog species are identified based on the comparisons of these frequency local peaks with some classifiers.

28 Application Challenges on Device Resources Very High Frequency Sampling (> 10 KHZ, the rule of double the highest frequency) Acoustic Signal Processing Machine Learning

29 Hybrid Architecture Motivation: Increased sensing coverage at comparable cost

30 Design Features In-network Reasoning Achieve (Very) High Sampling Rate in Mica motes through sampling scheduling Compression and noise- reduction. Acoustic Signal of a frog’s call collected from the field (Top). The same signal after compression and decompression (Bottom).

31 The Future: Participatory Sensor Networks* Sensor networks for urban applications will form the “next tier of the Internet” + -Leverage Cell phone installed base of acoustic and image sensors -Using internet search, blog, and personal feeds, along with automated location tags, to achieve context, and in network processing for privacy and personal control * Source: Deborah Estrin, UCLA + Source: David Culler Berkeley

32 For more information “Wireless Sensor Networks: A Systems Perspective”, Nirupama Bulusu and Sanjay Jha (editors), Artech House, Norwood, MA, August 2005.


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