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Wireless sensor networks Overview & applications

Murat Demirbas. Wireless sensor networks Overview & applications. Wireless sensor networks. A sensor node (mote) 8K RAM, 4Mhz processor magnetism, heat, sound, vibration, infrared wireless (radio broadcast) communication up to 100 feet costs ~$10 (right now costs $200) ‏. Outline.

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Wireless sensor networks Overview & applications

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  1. Murat Demirbas Wireless sensor networks Overview & applications

  2. Wireless sensor networks A sensor node (mote) 8K RAM, 4Mhz processor magnetism, heat, sound, vibration, infrared wireless (radio broadcast) communication up to 100 feet costs ~$10 (right now costs $200)‏

  3. Outline Vision Ubiquitous [pervasive | proactive] computing Design space Challenges Applications Ecology monitoring Precision agriculture Asset management Military surveillance

  4. Ubiquitous computing Mark Weiser, PARC, 1991 The most profound technologies are those that disappear: E.g., Writing: does not require active attention, but the information to be conveyed is ready for use at a glance (Periphery / calm technology)‏ We should not be required to live in computer’s world (OS, virtual reality), computers should become invisible and ubiquitous (disappear in background) in our physical world Already computers in light switches, thermostats, stereos and ovens help to activate the world For such a technology, localization & scalability are critical Location-aware devices Wireless communication Micro-kernel OS Distributed computing

  5. Ubiquitous computing… Ubiquitous PC: Tab : post-it sized; e.g., badge, shrink/store window on a tab Pad : A4/letter sized; e.g., scrap computer, edit each window on a pad Board : yard sized; e.g., long-distance meetings, bulletin boards Ubiquitous computers to overcome information overload “There is more information available at our fingertips during a walk in the woods than in any computer system, yet people find a walk among trees relaxing and computers frustrating. Machines that fit the human environment, instead of forcing humans to enter theirs, will make using a computer as refreshing as taking a walk in the woods.”

  6. iComp Ubiquitous Computing Lab @ Furnas 210

  7. Proactive computing David Tennenhouse, Intel VP, 2000 Moving from human-centered to human-supervised computing 150 million PCs versus 8 billion embedded computers Only 2% of computers are PCs Getting physical embedded computers Getting real real-time, fast responses from computers need to be arbitrated Getting out human above the loop (hidden Markov models)‏ Reinventing computer science

  8. Next century challenges: Scalable coordination in sensor networks Distributed local algorithms are needed for scalability! Embedded Networked Exploitcollaborative Sensing, action Control system w/ Small form factor Untethered nodes Sensing Tightly coupled to physical world

  9. New Class of Computing Number Crunching Data Storage Mainframe Minicomputer productivity interactive Workstation PC Laptop PDA log (people per computer)‏ streaming information to/from physical world year

  10. Technology Push CMOS miniaturization Micro-sensors (MEMS, Materials, Circuits)‏ acceleration, vibration, gyroscope, tilt, magnetic, heat, motion, pressure, temp, light, moisture, humidity, barometric chemical (CO, CO2, radon), biological, microradar, ... actuators too (mirrors, motors, smart surfaces, micro-robots)‏ Communication short range, low bit-rate, CMOS radios Power batteries remain primary storage, fuel cells 10x solar, vibration, flow

  11. Design space Deployment (random vs manual)‏ Mobility (static vs mobile; occasional vs continuous; active vs passive)‏ Cost, Size, Resources (brick vs matchbox vs grain)‏ Heterogeneity (homogenous vs heterogeneous)‏ Communication modality (radio vs light vs inductive)‏ Infrastructure (ad hoc vs infrastructure)‏

  12. Design space … Network topology (single-hop vs multihop)‏ Coverage (sparse vs dense)‏ Connectivity (connected vs intermittent vs sporadic)‏ Network size (10 vs 100 vs 1000 vs 10,000 vs 100,000)‏ Lifetime (day vs month vs year vs decade)‏ QOS requirements (none vs real-time)‏

  13. Challenges in sensor networks Energy constraint Unreliable communication Unreliable sensors Ad hoc deployment Large scale networks Limited computation power Distributed execution Nodes are battery powered Radio broadcast, limited bandwidth, bursty traffic False positives Pre-configuration inapplicable Algorithms should scale well Centralized algorithms inapplicable Difficult to debug & get it right

  14. Assignment 1 Present in class one WSN or smartphone application Outline the overall function of the WSN or smartphone in this application. What is the improvement it offers? Specify the design parameters and challenges for the proposed system Enumerate the system requirements and challenges Time for your presentation should be around 7 minutes

  15. References for assignment Great Duck island Agricultural applications Analysis of a habitat monitoring application NASA SensorWeb Meteorology and Hydrology in Yosemite Monitoring redwoods ZebraNet Virtual fences Active visitor guidance system UVA flock control

  16. References for assignment Counter-sniper system Self-healing land mines Damage detection in civil structures Smart-tag based data dissemination Continuous medical monitoring Elder care Aware home Smart kindergarten Media production Factory floor monitoring

  17. Assignment 2 Summarize one of the following Some computer science issues in ubiquitous computing (Weiser)‏ Proactive computing (Tennenhouse)‏ Next century challenges (Estrin.)‏

  18. Outline Vision Ubiquitous [pervasive | proactive] computing Design space Challenges Applications Ecology monitoring Precision agriculture Asset management Military surveillance

  19. WSN applications a new "scope" to a scientific endeavor a new approach to an engineering problem a new capability to a computing environment a new form of entertainment a new product opportunity

  20. Monitoring nesting behavior of birds Great Ducks experiment Detecting forest fires Detecting chemical or biological attacks Monitoring Redwood trees Ecology monitoring

  21. Dense Self-Organized Multihop Network

  22. Bottom Top 10m 20m 34m 30m 36m 2003, unpublished

  23. Precision agriculture Wireless sensor networks can be placed on farm lands to monitor temperature, humidity, fertilizer and pesticide levels Pesticide and fertilizer can only be applied when and where required Pesticide and fertilizer per one acre costs $20 Considering 100,000 acres savings of $2 million possible Vineyards BC

  24. Equipment Health Monitoring in Semiconductor Fab Equipment failures in production fabs is very costly Predict and perform preemptive maintenance Typical fab has ~5,000 vibration sensors Pumps, scrubbers, … Electricians collect data by hand few times a year Sample: 10’s kilohertz, high precision, few seconds Fab Equipment Intranet Intranet isolation Ad Hoc Mote Network Root Node 802.11 Mesh Mote + Vibration Sensors

  25. Project ExScal: Concept of operation Put tripwires anywhere—in deserts, other areas where physical terrain does not constrain troop or vehicle movement—to detect, classify & track intruders

  26. Envisioned ExScal customer application Convoy protection Detect anomalous activity along roadside Hide Site IED Border control Canopy precludes aerial techniques Gas pipeline Rain forest – mountains – water environmental challenges

  27. ExScal summary Application has tight constraints of event detection scenarios: long life but still low latency, high accuracy over large perimeter area Demonstrated in December 2004 in Florida Deployment area: 1,260m x 288m ~1000 XSMs, the largest WSN ~200 XSSs, the largest 802.11b ad hoc network

  28. Line in the sand project Thick line allows detection & classification as intruders enter the protected region; also allows fine grain intruder localization Grid of thin lines allows bounded uncertainty tracking 1 km Thick Entry Line 250 m A S S E T

  29. ExScal sample scenarios Intruding person walks through thick line (pir) detection, classification, and fine-grain localization Intruding vehicle enters perimeter and crosses thick line (acoustic) detection, classification, and fine-grain localization Person/ATV traverses through the lines coarse-grain tracking Management operations to control signal chains, change parameters, and programs dynamically; query status and execute commands

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