Workshop - RSN Update

1. Workshop - RSN Update Richard R. Brooks Head Distributed Intelligent Systems Dept. Applied Research Laboratory Pennsylvania State University P.O. Box 30 State College, PA 16804-0030 email: rrb@acm.org Tel. (814) 863-5698 Fax (814) 863-1396 Dept. (814) 863-5735 Reactive Sensor Network

2. Research Problems • Phase 1: How to best implement communications and computation mobility for ad hoc wireless sensor networks. • Phase 2: Methods, algorithms, and software for: distributed dynamic calibration of redundant sensors, and ad hoc routing for sensor data to conserve bandwidth. • Phase 3: Local behaviors for globally desirable behavior of the system in response to random, chaotic, non-linear network disruptions. • Phase 4: Find the limits of a global system’s ability to adapt using purely local actions. Reactive Sensor Network

3. Phase I: RSN Mobile code approach • Will support: • Compiled languages • Interpreted languages • Hardware dependencies • Internet and wireless nodes • Adaptation to system state • Virtual memory model • Explicit programming • Data pipelines • On the fly compression/decompression • On the fly compilation • Resource recovery • Will not consider: • Security beyond trusted code model • Code migration • Debugging support • “Write once run anywhere” • Interfaces between modules • These topics are orthogonal. Reactive Sensor Network

4. ARL/MCN Service on WINS/NG ARL/MCN service on NT Code ARL/MCN service on NT ARL/MCN Service on WINS/NG Repository ARL/MCR Data description Gateway ARL/MCN Service on WINS/NG HW description DB engine Scenario 1: User Request ARL/MCN Service on WINS/NG GUI Reactive Sensor Network

5. ARL/MCN Service on WINS/NG ARL/MCN service on NT Code ARL/MCN service on NT ARL/MCN Service on WINS/NG Repository ARL/MCR Data description Gateway ARL/MCN Service on WINS/NG HW description DB engine Scenario 2: Virtual Memory ARL/MCN Service on WINS/NG GUI Reactive Sensor Network

6. REAP -Remote Execution and Action Protocol • Request and control of remote code execution • Transaction based with multiple concurrent requests in a transaction • A single transaction may involve multiple nodes • Multiple concurrent transactions supported • Transaction synchronization supported • Push and Pull data access • Data pipelines supported • API provided for use by others in Sensor IT community • URLs identify data and code • Allows data gathering and scattering • Designed to minimize power consumption by ACK & NAK packets Reactive Sensor Network

7. Version 1.0 Delivery in January 2000 • January delivery will support: • Windows NT / Windows CE • IP connections • Well-defined C++ API for use by other research groups • Registration of code, hardware, and data types • Programs registered can be in any language (with caveats) • .DLL and .EXE • Garbage collection • Explicit execution • Data pipelines • Updates during phase II / III will provide more complete support: • WINS NG API • On-the-fly compilation / build • On-the-fly compression / decompression • Dependency graphs • Scheduling and adaptation support Reactive Sensor Network

8. Phase II: Sensor Collaboration • 1) Use of redundant readings increase accuracy / dependability • Set of redundant sensor data • Weight by variance from estimate • Dynamic calibration • Distributed approach • Asynchronous algorithm • 2) Use of redundant data consumes bandwidth • Queries for limited area • Design “reverse-multicast” tree • Combine only local information • Conserve resources • 3) Both are extremes of a continuum • Implement and test both • Quantify costs/ benefits • Physical tests on WINS NG • Simulations test scalability • Merge into a common approach • Allow graceful degradation Reactive Sensor Network

9. 0.7 Ps– Sensor position rs – Sensor position range rc – Sensor communication range  – Variance of sensor position in stochastic grid 0.6 0.5 0.4  0.3 rc 0.2 rs 0.1 0 Ps Network model for simulation • Regular grid • regular tessellation • Stochastic grid • position variance within grid • Single neighborhood (1 gateway) • Number of nodes • Node density • NG node variables • simulated by stochastic variables • sensor range • communications range • battery lifetime • Data types • different method for each type • binary • code book / enumeration • continuous value • vectors of continuous • 1-D (time series) • 2-D (image) • 3-D (sequence of images) • Queries • entire grid until failure • position at random • following target through grid Reactive Sensor Network

10. Phase III: Task / Data routing • IP resources have fewer power constraints • Route to nearest gateway • Similar to mobile ad-hoc routing • Queries tied to physical location • Queries not tied to machine identity • Routing tables unnecessary, expensive • Power & congestion information unstable • Routing to conserve energy (trade-off) • Routing to minimize delay (trade-off) • Decision made at each hop • Decision based on immediate neighborhood • “Water flowing downstream” • Example initial conditions Reactive Sensor Network

11. Model evolution of network resources over time using empirical estimates of resource consumption to route data and allocate tasks to nodes Reactive Sensor Network

12. Conclusion • Phase I underway • Draft C++ API • Remote Execution and Action Protocol • Windows CE / NT Service • v. 1.0 delivery 01/2000 • API available for use by other programs • Requests accepted, as well as .DLLs and .EXEs for testing • Initial planning for phase II • Experimental designs for physical tests and simulations • Coding for dynamic calibration • Conception of network topology • Resource conservation concepts • Phase III will build on I & II • Survey of ad hoc routing methods • Sensor IT specific routing constraints established • Network modeling methodology Reactive Sensor Network