ELEG 467/667

1. ELEG 467/667 Sensor Networks Spring 2005

2. Before anything happens • Add / drop • Does anyone know of anyone who is dropping? • A few people can add! (Don’t tell anyone) • Meeting time

3. Basics • Instructor: Stephan Bohacek • TA: Vinay Sridhara • Web page: http://www.eecis.udel/~bohacek

4. Course Focus • Comprehensive introduction to sensor networks. • Network protocols at various layers. • MAC, routing, transport. • Application issues. • Data fusion, localization • Interception with other areas. • Unique issues: energy efficiency, self managing, data driven, arbitrarily large scale, etc.

5. Pre-requisites • Basic networking • For example, you know • what the transport layer does • what exponential back-off is • at least one MAC protocol • what flooding is and how it works • Programming • For example, you can implement flooding.

6. Format of class • Seminar / project oriented • Lectures • The first couple of months and some others. • Reading • Assigned reading with in class discussion • Projects • Many small/moderate projects and a large one • Presentations • ½ lecture on a topic (Grad students) • Project discussions • Paper • Midterm • Final project write-up

7. Projects • Moderate and final projects will be group projects. Groups should have 2 grad and 2 under grads. • Project selection and presentation (by 3/30) • Projects may be related to grad student lecture topic • Project progress report (4/20) • Project presentation (5/18 and 5/25)

8. Grading • Class discussion/reading (10%) • Small/moderate projects (20%) • Midterm presentations (20%) • Midterm paper (20%) • Final project (30%)

9. Reading material • Wireless sensor networks. Feng Zhao and Leonidas Guibas • Wireless sensor networks. Editors: Ragavendra, Sivalingam, and Znati • Wireless Sensor Networks. Edgar and Callaway • Handbook of sensor networks. Editors: Ilyas and Mahgoub • Papers on web page or handed out

10. Overview – today Propagation of wireless signals Energy MAC protocols Routing Transport Applications Localization Tracking Time synchronization Data Gathering Processing Compression Fusion Architecture Topics (approximate)

11. Today: Introduction • Sensor networks. • Definition, motivation, examples • Challenges. • Architecture and design Issues.

12. What are sensor networks? • Networks of devices that are able to sense the environment, perform on-board computation, (and communicate) • Why? Because we can: Technology • Circuit integration. • Ability to integrate more functions into chip with lower energy • Wireless communication. • Better communication theory • Better devices • Bit-rates are slowly increasing • Transmission power is decreasing • Sensor technology

13. Result: sensing nodes PC-104+ UCLA TAG UCB Mote

14. Embedded Networked Sensing • Micro-sensors, • on-board processing, • wireless interfaces • small scale and low cost => many • monitor phenomena “up close”. • Enables spatially and temporally dense monitoring. • Nyquist Sampling – you must sample often enough (in time or space) • Inverse problems are very difficult, e.g., by sensing the temperature at a few places, determine the temperature everywhere (numerically unstable). Instead, directly sense the temperature everywhere. • Wireless interface allow little infrastructure – easy deployment • Wireless interface allow cooperation and distributed computing

15. Vision Embed numerous sensing nodes to monitor and interact with physical world Network these devices so that they can execute more complex tasks.

16. Sensor networks applications…

17. Vision: “Embed the World” • Buildings self-detect and self-correct from structural faults (e.g., weld cracks). • Schools detect airborne toxins at low concentrations, trace contaminant transport to source. • Buoys alert swimmers to dangerous bacterial levels. • Earthquake-rubbled building infiltrated with robots and sensors: locate survivors, evaluate structural damage. • Ecosystems infused with chemical, physical, acoustic, image sensors to track global change parameters. • More??

18. Sensors are not always small… • For example, Umass’s CASA (Collaborative Adaptive Sensing of the Atmosphere). Network of meteorological radars to observe, detect, and predict atmospheric phenomena.

19. Deployments • Ecological Habitat Monitoring • UCB/Intel Berkeley: Great Duck Island • UCLA-CENS: James Reserve • Princeton: ZebraNet in Kenya • Structural Monitoring • UCLA-CENS: Factor Building • USC: Networked SHM • UCB/Intel Berkeley: SF Golden Gate Bridge • UD • Biomedical Applications • Artificial retina • “Bio-monitors” • Industrial and Commercial Apps • Ember Corp: Thermal Process Control, Shipment Tracking • CCM

20. Environmental monitoring • Petrel habitat on Great Duck Island in Maine. • Questions to answer: • Usage pattern of nesting burrows over the 24-72 hour cycle. • Changes in the burrow and surface environmental parameters. • Differences in the micro-environments with and without large numbers of nesting petrels.

21. Hierarchical deployment

22. Sensors • Mica platform • Atmel AVR w/ 512kB Flash • 916MHz 40kbps RFM Radio • Range: max 100 ft • Affected by obstacles, RF propogation • 2 AA Batteries, boost converter • Mica weather board – “one size fits all” • Digital Sensor Interface to Mica • Onboard ADC sampling analog photo, humidity and passive IR sensors • Digital temperature and pressure sensors • Designed for Low Power Operation • Individual digital switch for each sensor • Designed to Coexist with Other Sensor Boards • Hardware “enable” protocol to obtain exclusive access to connector resources • Packaging • Conformal sealant + acrylic tube • Placement • Place above ground and in burrows (propagation?)

23. Gateway • Communicate with sensor and base station. • Solar powered (sensors are just battery powered). • Directional antenna pointed toward base station.

24. Base station • Laptops • In lighthouse keepers house. • Log all data and transmit via satellite to D.C. and then on to the Internet.

25. Smart Dust • Design goals • Cubic millimeter. • Very low energy. • Result: sensor package containing: • Sensors • Optical transmitter (passive and active) and receiver • Signal processing • Solar power source

26. Smart dust applications • Environmental monitoring. • Insects. • Meteorological phenomena. • Special operations.

27. Smart dust components

28. ModulatedDownlinkDataor BeamforUplink BeamforUplink Smart dust: passive transmission UnmodulatedInterrogation Lens Photo- detector Downlink Laser Downlink DataIn DataOut Uplink Signal Selection and Processing DataIn CCD Corner-Cube Image Lens Retroreflector ModulatedReflected Sensor Array DustMote Uplink Uplink ... Data Data High power laser emitted from BS for downlink and uplink communication. Out Out 1 N Base-StationTransceiver

29. Passive transmissions • Reflect illuminating beam (from BS) back encoding data. • BS decodes data by reading the “on” and “off” reflections. • Rates of up to 1 kbps over 150m. • Low power sensor power • But, uninterrupted LoS with BS. • A single CCD can decode multiple communications at the same time. • Each CCD element can “see” a small region of space. Each element can decode one communication. • Spatial multiplexing.

30. RFID • RFID uses backscatter - Another passive transmission technique. • RADAR • Send a beam and receive reflections. • Physical radar • Put my hand out and hit what is there. • Instead of DC, I could use AC and move my hand back and forth. I could sense things just the same. However, if what I am hitting resonates at the frequency my hand moves, then the thing I am hitting will start oscillating. • A receiving antenna is not just a receiver. If current is moving along the antenna, then it is transmitting as well. • If the circuit the antenna is attached to has a resonates at the carrier frequency, then this circuit will oscillate. These oscillation will cause RF transmissions. • If the circuit is suddenly switched so it does not have a resonates, then now transmissions occur. • The RFID can switch the circuit to modulate the transmission.

31. Biomedical applications • Health monitors. • Glucose level. • Digestive system. • Vascular system, etc. • Artificial retina.

32. Sensors for vision

33. Today: Introduction • Sensor networks. • Definition, motivation, examples • Challenges. • Architecture and design Issues.

34. Challenges • Energy • Self-configuring/adapting • Data processing • Scalabilty

35. Energy • Sensors may require long life times • Great Duck Island required 9 months • If embedded in highways, 10 years is required • Pacemaker (not just a sensor!) last 5-10 years • (supervisory control and data acquisition (SCADA))

36. Energy • Sensors may require long life times • Great Duck Island required 9 months • If embedded in highways, 10 years is required • Pacemaker (not just a sensor!) last 5-10 years • (supervisory control and data acquisition (SCADA)) • Approaches • Low duty cycle systems. • Sleep: deep sleep (everything off), listening but not transmitting, periodic listening • Increases delay => impacts QoS • Nodes must by sychronized • Requires good clocks (which require more power) • As battery power drops, clocks may experience severe drift, reducing effective lifetime

37. Energy • Sensors may require long life times • Great Duck Island required 9 months • If embedded in highways, 10 years is required • Pacemaker (not just a sensor!) last 5-10 years • (supervisory control and data acquisition (SCADA)) • Approaches • Low duty cycle systems. • Sleep: deep sleep (everything off), listening but not transmitting, periodic listening • Increases delay => impacts QoS • Nodes must by sychronized • Requires good clocks (which require more power) • As battery power drops, clocks may experience severe drift, reducing effective lifetime • Low bit-rate • Low power transmissions require low bit-rate

38. Energy • Sensors may require long life times • Great Duck Island required 9 months • If embedded in highways, 10 years is required • Pacemaker (not just a sensor!) last 5-10 years • (supervisory control and data acquisition (SCADA)) • Approaches • Low duty cycle systems. • Sleep: deep sleep (everything off), listening but not transmitting, periodic listening • Increases delay => impacts QoS • Nodes must by sychronized • Requires good clocks (which require more power) • As battery power drops, clocks may experience severe drift, reducing effective lifetime • Low bit-rate • Low power transmissions require low bit-rate • Complicated communication schemes • Nodes can cooperate to transmit far with low power. • Advanced data compression • However, CPU uses power as well. But CPU power usage is decreasing as technology advances.

39. Energy • Sensors may require long life times • Great Duck Island required 9 months • If embedded in highways, 10 years is required • Pacemaker (not just a sensor!) last 5-10 years • (supervisory control and data acquisition (SCADA)) • Approaches • Low duty cycle systems. • Sleep: deep sleep (everything off), listening but not transmitting, periodic listening • Increases delay => impacts QoS • Nodes must by sychronized • Requires good clocks (which require more power) • As battery power drops, clocks may experience severe drift, reducing effective lifetime • Low bit-rate • Low power transmissions require low bit-rate • Complicated communication schemes • Nodes can cooperate to transmit far with low power. • Advanced data compression • However, CPU uses power as well. But CPU power usage is decreasing as technology advances. • Efficient protocols • Avoid retransmissions • Avoid end-to-end retransmission with link layer retransmissions and data caching • Avoid contention • FDM • TDM

40. Energy • Approaches • Renewable power/scavenging. • Solar energy. • Mechanical vibrations (sneakers) • Radio-Frequency inductance (RFID) • Infrared inductance (passive optical)

41. Self-configuring/adapting • Ad hoc deployment; inaccessible areas; • E.g, dropped from airplane • Adapt to unpredictable environment. • E.g., nodes break, crash, run out of power (consider a deployment where each node will last only a week, but nodes come out of hibernation at random times so the network has a lifetime of several months) • Unattended, untethered • There is no one to reboot • Fault tolerant and robust • approaches • Each sensor operate autonomously from neighbors. • Overlapped services area. • No single point of failure.

42. Data processing • Cooperation • Exploit computation near data to reduce communication. • Collaborative signal processing • E.g., nearby images are combined to determine the exact location of an object. The position of the object is the data sent to the base station, not the images. • Data aggregation/compression • If data is spatially correlated, then data can be aggregated and compressed, taking advantage of correlation

43. Data processing • Cooperation • Exploit computation near data to reduce communication. • Collaborative signal processing • E.g., nearby images are combined to determine the exact location of an object. The position of the object is the data sent to the base station, not the images. • Data aggregation/compression • If data is spatially correlated, then data can be aggregated and compressed, taking advantage of correlation • Limited computation and storage capabilities • Error propagation – decrease fault tolerance • The more nodes a process uses, the lower the robustness but more efficient. • Complicated cooperation may require intensive communication. • Heterogeneous power dissipation and lifetime • Nodes closer to base station must carry more data and are also in a better position to aggregate data. But these will expend energy. • Trade-off between latency and energy

44. Data processing • Distributed representation/storage • Data Centric Protocols, in-network processing: • Interpretation of spatially distributed data (Per-node processing alone is not enough). • network does in-network processing based on distribution of data. • Queries automatically directed towards nodes that maintain relevant/matching data. • Pattern-triggered data collection • Multi-resolution data storage and retrieval. • Distributed edge/feature detection. • Index data for easy temporal and spatial searching. • Finding global statistics (e.g., distribution).

45. Traditional approach: warehousing • Data extracted from sensors, stored on server. • Query processing takes place on server.

46. Sensor Database System • Sensor Database System supports distributed query processing over sensor network

47. Characteristics of a sensor network: Streams of data. Large number of nodes Multi-hop network. No global knowledge about the network. Node failure and interference is common. Energy scarce. Limited memory No administration, Can existing database techniques be reused? What are the new problems and solutions? Representing sensor data. Representing sensor queries. Processing query fragments on sensor nodes. Distributing query fragments. Adapting to changing network conditions. Dealing with site and communication failures. Deploying and managing a sensor database system. Sensor database system

48. Time and location • Unlike the Internet, node time and spatial location essential for some applications. • E.g., localization and time synchronization needed to detect events, compare detections across nodes, perform collaborative processing, geo forwarding/routing, etc. • GPS provides solution (with differential GPS providing finer granularity). • GPS not always available. • Resolution is not very good (10’s of meters) • Other approaches? • To correlate events, the time of the event must be known. • For coordinated sleeping, the sensors must be synchronized.

49. Coverage • Area coverage:fraction of area covered by sensors • Detectability: probability sensors detect moving objects • Overlap: fraction of sensors covered by other sensors • Control: • Where to add new nodes for max coverage. • How to move existing nodes for max coverage.

50. Why not Internet protocols? • Traditional networks have hosts and routers. • Sensor nodes are both hosts and routers • Internet protocols were designed following an e2e approach. • For efficiency, need to use information from lower-layers. • Sensor networks are data-driven; end points don’t matter. • IP are bidirectional • Sensor networks are directional - Data flow and control flow • TCP/IP is wasteful, lazy convergence – it will work eventually • Sensor networks must be very thrifty - energy constraint issues • IP – the system admin is never very far away • Sensor network - Self-organization, self-management. • IP – local networks are fairly small • Sensor networks could be arbitrarily large number of small sensors generating data.