Wireless sensor networks Overview & applications Murat Demirbas

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.”

iComp Ubiquitous Computing 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 EmbeddedNetworked Sensing Control system w/ Small form factor Untethered nodes Exploit collaborative Sensing, action Tightly coupled to physical world Distributed local algorithms are needed for scalability!

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

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 1.Great Duck islandGreat Duck island 2.Agricultural applicationsAgricultural applications 3.Analysis of a habitat monitoring applicationAnalysis of a habitat monitoring application 4.NASA SensorWebNASA SensorWeb 5.Meteorology and Hydrology in YosemiteMeteorology and Hydrology in Yosemite 6.Monitoring redwoodsMonitoring redwoods 7.ZebraNetZebraNet 8.Virtual fencesVirtual fences 9.Active visitor guidance systemActive visitor guidance system 10.UVA flock controlUVA flock control

16 References for assignment 1.Counter-sniper systemCounter-sniper system 2.Self-healing land minesSelf-healing land mines 3.Damage detection in civil structuresDamage detection in civil structures 4.Smart-tag based data disseminationSmart-tag based data dissemination 5.Continuous medical monitoringContinuous medical monitoring 6.Elder careElder care 7.Aware homeAware home 8.Smart kindergartenSmart kindergarten 9.Media productionMedia production 10. 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

, unpublished Bottom Top 36m 34m 30m 20m 10m

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 Fab Equipment Mote + Vibration Sensors Ad Hoc Mote Network Intranet Mesh Intranet isolation Root Node 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

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

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

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 b 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 Thick Entry Line A S S E T 1 km 250 m

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