1. By Ryan Berger

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 responseEcosystems, Biocomplexity

7. Possible Scenario  May 1 st, 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 2 nd, 2003  Movement in the rock structure detected  Data archiving begins  Models generate predictions, provided to local emergency managers for planning

9. Possible Scenario  May 3 rd, 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 24 th, 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 Embedded Control system w/ Small form factor Untethered nodes Networked Exploit collaborative Sensing, action Sensing Tightly coupled to physical world

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. Coverage Measures  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 Sensor field (either known sensor locations, or spatial density) S D

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 Sensor DB Front-end 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. 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) S

32. ESRT Approach Sensor DB Index Node DB Sensor DB Front-end 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