October 7, 1999Reactive Sensor Network1 Workshop - RSN Update Richard R. Brooks Head Distributed Intelligent Systems Dept. Applied Research Laboratory Pennsylvania State University P.O. Box 30 State College, PA Tel. (814) Fax (814) Dept. (814)

October 7, 1999Reactive Sensor Network2 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.

October 7, 1999Reactive Sensor Network3 Phase I: RSN Mobile code approach Will not consider: Security beyond trusted code model Code migration Debugging support “Write once run anywhere” Interfaces between modules These topics are orthogonal. 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

October 7, 1999Reactive Sensor Network4 GUI DB engine Code Data description HW description Gateway ARL/MCN Service on WINS/NG ARL/MCN Service on WINS/NG ARL/MCN Service on WINS/NG ARL/MCN Service on WINS/NG Repository ARL/MCR ARL/MCN service on NT Scenario 1: User Request

October 7, 1999Reactive Sensor Network5 GUI DB engine Code Data description HW description Gateway ARL/MCN Service on WINS/NG ARL/MCN Service on WINS/NG ARL/MCN Service on WINS/NG ARL/MCN Service on WINS/NG Repository ARL/MCR ARL/MCN service on NT Scenario 2: Virtual Memory

October 7, 1999Reactive Sensor Network6 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

October 7, 1999Reactive Sensor Network7 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

October 7, 1999Reactive Sensor Network8 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

October 7, 1999Reactive Sensor Network  P s – Sensor position r s – Sensor position range r c – Sensor communication range  – Variance of sensor position in stochastic grid P s – Sensor position r s – Sensor position range r c – Sensor communication range  – Variance of sensor position in stochastic grid PsPs rsrs r c 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

October 7, 1999Reactive Sensor Network10 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

October 7, 1999Reactive Sensor Network11 Model evolution of network resources over time using empirical estimates of resource consumption to route data and allocate tasks to nodes

October 7, 1999Reactive Sensor Network12 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