Sensor Networks

1. By Ryan Berger Sensor Networks

2. What are sensor networks? • Network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. • The sensors themselves can range from small passive microsensors (e.g, "smart dust") to larger scale, controllable weather-sensing platforms.

3. Quick Rundown • They have micro-sensors, on-board processing, wireless interfaces feasible at very small scale • Can monitor phenomena “up close” • Enables spatially and temporally dense environmental monitoring

4. Who uses Sensor Networks? • The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. • Sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control.

5. Potential Uses • High-rise buildings self-detect structural faults (e.g., weld cracks) • Schools detect airborn 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 • Battlefield sprinkled with sensors that identify track friendly/foe air, ground vehicles, personnel • Parking lots or garages keep track of which spots are occupied and which aren’t

6. Seismic Structure response Ecosystems, Biocomplexity

7. Possible Scenario • May 1st, 2003 • Two days before the collapse of the Old Man in the Mountain • Could this have been prevented by using sensors?

8. Possible Scenario • May 2nd, 2003 • Movement in the rock structure detected • Data archiving begins • Models generate predictions, provided to local emergency managers for planning

9. Possible Scenario • May 3rd, 2003 • Because instability was detected early, a team is sent in to brace the structure to prevent further movement • Team begins renovations on structure • Local residents and tourists are evacuated to prevent possible injury

10. Possible Scenario • May 24th, 2003 • The old man lives! • Renovations are complete • Sensors have reported that the rocks are structurally sound (for now) • Citizens are welcomed back into their homes • This use of sensors is known as area monitoring

11. Another Application Invaluable Fire Fighting Tool FIRE Eye

12. Receiver Hardware Types • ZigBee

13. Other Hardware Types • Wibree • 6lowpan

14. Programming Languages Implemented • c@t (Computation at a point in space (@) Time ) • DCL (Distributed Compositional Language) • galsC • nesC • Protothreads • SNACK • SQTL

15. Generally Runs Using… • TinyOS

16. An Example of an Interface (MonSense)

17. Characteristics of Each Sensor

18. Types of Sensors • Passive elements: seismic, acoustic, infrared, strain, salinity, humidity, temperature, etc. • Passive arrays: imagers (visible, IR), biochemical • Active sensors: radar, sonar • High energy, in contrast to passive elements

19. Desired Designs • Self-configuring systems that adapt to unpredictable environment • Dynamic, messy (hard to model), environments include pre-configured behavior • Leverage data processing inside the network • Collaborative signal processing • Achieve desired behavior with localized algorithms (distributed control)

20. Why simply adapting an IP “end-to-end” network doesn’t work • Internet routes data using IP Addresses in Packets and Lookup tables in routers • Humans get data by “naming data” to a search engine • Many levels of indirection between name and IP address • Embedded, unattended systems can’t tolerate communication overhead of indirection • Special purpose system functions: don’t need or want Internet general purpose functionality designed for elastic applications that may change without warning.

21. The Importance of Time and Location • Unlike Internet, node time/space location essential for local/collaborative detection • Fine-grained localization and time synchronization needed to detect events in space and compare detections across nodes • GPS provides solution where available • GPS not always available, too “costly,” too bulky • other approaches under study • Localization of sensor nodes has many uses • Beamforming for localization of targets and events • Geographical forwarding • Geographical addressing

22. Area coverage: fraction of area covered by sensors Detectability: probability sensors detect moving objects Node coverage: fraction of sensors covered by other sensors Control: Where to add new nodes for max coverage How to move existing nodes for max coverage Coverage Measures D S Sensor field (either known sensor locations, or spatial density)

23. Traditional Approach: Warehousing Warehouse Front-end Sensor Nodes

24. Alternative Approaches • Distributed Storage • Event-to-Sink Reliable Transport

25. Distributed Storage • Data Centric Protocols, In-network Processing goal: • 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 (quick access to recently recorded data)

26. Distributed Storage Approach SensorDB SensorDB SensorDB SensorDB SensorDB Front-end SensorDB SensorDB SensorDB Sensor Nodes

27. Performance of Distributed Storage • High accuracy? • Distance between ideal answer and actual answer differs • Ratio of sensors participating in answer also differs • Low latency • Time between data is generated on sensors and answer is returned within a short period of time • Limited resource usage • Energy consumption is high

28. Distributed Storage Issues • Need for Coordination/Distributed Resource Allocation • Multiple sensors need to collaborate on tasks • View objects of interest from multiple angles with different types of sensors • Sensing time windows need to be closely aligned • Environmental Dynamics • Sensor configuration changes as target moves • Multiple target in overlapping sensor regions

29. Distributed Storage Issues, cont. • Soft Real-time • Limited time window for sensing • Must anticipate where target is moving in order to effectively allocate sensor resources • Time for coordination affects time for sensing • Scalability: need to be able to handle large numbers of sensor nodes • Robustness: local failures should not induce global collapse • Handle uncertain information, sensor/processor/communication failures

30. Soft vs. Hard Real-Time • Soft: There are not catastrophic effects if events are occasionally not interpreted correctly • If lose sight of target for a bit, time steps and then reacquire (generally works okay) • Hard: Computation/Sensing after the “deadline” may or may still have value • Reduction in certainty of target location

31. S Event-to-Sink Reliable Transport (ESRT) • Event-to-sink reliability • Self-configuration • Energy awareness (low power consumption requirement!) • Congestion Control • Variation in complexity at source and sink (computation complexity)

32. ESRT Approach SensorDB SensorDB SensorDB SensorDB SensorDB Front-end IndexNode DB SensorDB SensorDB Sensor Nodes

33. Reliability of an ESRT • Reliability is measured in terms of the number of packets received • Number of received data packets in decision interval at the sink • Number of packets required for reliable event detection • Normalized reliability = observed ÷ desired

34. Issues with ESRT • Information can be lost if the indexing node fails • Indexing node can become overloaded • Because of this, indexing node may need to be selective in the nodes it processes • Time taken for selection/transfer from sensors to index may result in the processing of “old” data

35. How to “Overcome” Shortcomings • Avoid processing overloads • Avoid communication overloads • Have information/processing co-located • Avoid failure of network based on single location failure • Allocate sensing so that as many targets can be tracked with reasonable success • Allocate processing/sensing so that real-time constraints can be met

36. Radar Parameter Display Image Transfer with 90% losses

37. Radar Parameter Display Image Transfer with no losses

38. Error Detection • Node information is propagated through the use of directory services • Sensors provide sector managers with their information. • “Track managers” query sector managers for sensor details. • This information is cached for future use at each step • The directory held in sector manager maintains historical query information • New data is analyzed for relevance to those queries • Relevant information is automatically propagated to the query source • This process quickly updates each node’s data, allowing them to adapt to change

39. What We’ve Learned (In a Nutshell)… • What sensor networks are • Examples of how they might be used • Overview of how they work • Desired designs • Coverage measures • Different approaches to set-up • Error detection (very brief)

40. Sensor Networks in the News • Researchers plan to install 100 sensors by 2011 on streetlamps throughout the city of Cambridge, MA • Distributed Traffic Light Control • Microfluidics for water supply protection

41. In Conclusion… • Sensor Networks = Incredibly useful, perhaps vital technology • There is no one best approach • Very sensitive to characteristics/capabilities of sensors, quality of sensor data, amount and type of processing required, system objectives, communication and processing capabilities, environment, etc… • This is a technology that will only become more prevalent in our everyday lives