Wireless sensor networks Overview applications Murat Demirbas Wireless
Wireless sensor networks Overview & applications Murat Demirbas
Wireless sensor networks A sensor node (mote) 8 K RAM, 4 Mhz processor magnetism, heat, sound, vibration, infrared wireless (radio broadcast) communication up to 100 feet costs ~$10 (right now costs $200) 2
Outline • Vision Ubiquitous [pervasive | proactive] computing Design space Challenges • Applications Ecology monitoring Precision agriculture Asset management Military surveillance 3
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 4
Ubiquitous computing… • Ubiquitous PC: Tab : post-it sized; e. g. , badge, shrink/store window on a tab Pad : A 4/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. ” 5
i. Comp Ubiquitous Computing Lab @ Furnas 210
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-time, fast responses from computers need to be arbitrated • Getting out human above the loop (hidden Markov models) Reinventing computer science 7
Next century challenges: Scalable coordination in sensor networks Embedded Networked Exploit collaborative Sensing, action Control system w/ Small form factor Untethered nodes Sensing Tightly coupled to physical world • Distributed local algorithms are needed for scalability! 8
log (people per computer) New Class of Computing Number Crunching Data Storage Mainframe Minicomputer productivity interactive Workstation PC Laptop PDA streaming information to/from physical world year 9
Technology Push • CMOS miniaturization • Micro-sensors (MEMS, Materials, Circuits) acceleration, vibration, gyroscope, tilt, magnetic, heat, motion, pressure, temp, light, moisture, humidity, barometric chemical (CO, CO 2, 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 10 x solar, vibration, flow 10
Design space • Deployment • Mobility (random vs manual) (static vs mobile; occasional vs continuous; active vs passive) • Cost, Size, Resources • Heterogeneity (brick vs matchbox vs grain) (homogenous vs heterogeneous) • Communication modality • Infrastructure (radio vs light vs inductive) (ad hoc vs infrastructure) 11
Design space … • Network topology • Coverage (sparse vs dense) • Connectivity • Network size • Lifetime (single-hop vs multihop) (connected vs intermittent vs sporadic) (10 vs 1000 vs 10, 000 vs 100, 000) (day vs month vs year vs decade) • QOS requirements (none vs real-time) 12
Challenges in sensor networks • Energy constraint : Nodes are battery powered • Unreliable communication • Unreliable sensors : Radio broadcast, limited bandwidth, bursty traffic : False positives • Ad hoc deployment : Pre-configuration inapplicable • Large scale networks : Algorithms should scale well • Limited computation power : Centralized algorithms inapplicable : Difficult to debug & get it right • Distributed execution 13
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 14
References for assignment 1. Great Duck island 2. Agricultural applications 3. Analysis of a habitat monitoring application 4. NASA Sensor. Web 5. Meteorology and Hydrology in Yosemite 6. Monitoring redwoods 7. Zebra. Net 8. Virtual fences 9. Active visitor guidance system 10. UVA flock control 15
References for assignment 1. Counter-sniper system 2. Self-healing land mines 3. Damage detection in civil structures 4. Smart-tag based data dissemination 5. Continuous medical monitoring 6. Elder care 7. Aware home 8. Smart kindergarten 9. Media production 10. Factory floor monitoring 16
Assignment 2 • Summarize one of the following Some computer science issues in ubiquitous computing (Weiser) Proactive computing (Tennenhouse) Next century challenges (Estrin. ) 17
Outline • Vision Ubiquitous [pervasive | proactive] computing Design space Challenges • Applications Ecology monitoring Precision agriculture Asset management Military surveillance 18
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 19
Ecology monitoring • Monitoring nesting behavior of birds Great Ducks experiment • Detecting forest fires • Detecting chemical or biological attacks • Monitoring Redwood trees 20
Dense Self-Organized Multihop Network 21
10 m 20 m 34 m 30 m 36 m Bottom Top 2003, unpublished 22
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 23
Equipment Health Monitoring in Semiconductor Fab • Equipment failures in production fabs is very costly Fab Equipment 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 Intranet isolation Ad Hoc Mote Network Root Node 80 2. 1 1 M es h Mote + Vibration Sensors 24
Project Ex. Scal: 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 25
Envisioned Ex. Scal customer application Convoy protection Detect anomalous activity Hide Site along roadside IED Border control Canopy precludes aerial techniques Gas pipeline Rain forest – mountains – water environmental challenges 26
Ex. Scal 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, 260 m x 288 m • ~1000 XSMs, the largest WSN • ~200 XSSs, the largest 802. 11 b ad hoc network 27
Line in the sand project 1 km Thick Entry Line 250 m ASSET • 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 28
Ex. Scal 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 29
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