1. 1 Collaborative Processing in Sensor Networks Lecture 2 - Mobile-agent-based Computing Hairong Qi, Associate Professor Electrical Engineering and Computer Science University of Tennessee, Knoxville http://www.eecs.utk.edu/faculty/qi Email: hqi@utk.edu Lecture Series at ZheJiang University, Summer 2008

2. 2 Research Focus - Recap Develop energy-efficient collaborative processing algorithms with fault tolerance in sensor networks –Where to perform collaboration? –Computing paradigms –Who should participate in the collaboration? –Reactive clustering protocols –Sensor selection protocols –How to conduct collaboration? –In-network processing –Self deployment

3. 3 Architecture of Mobile Agent Itinerary –Route of migration Identification –Unique for each mobile agent Data buffer –Carries the partially integrated results Method –Execution code carried with the agent 160.10.30.100 itinerary data buffer method identification

4. 4 Distributed Computing Paradigms Mobile-agent-based Computing Client/Server Computing Transfer UnitComputing Client/Server ComputingData Centralized, occurs at the servers Mobile agent ComputingMobile agent Distributed evenly among sensor nodes Energy and network bandwidth requirement Scalability Reliability Progressive accuracy Task adaptivity Fault tolerance

5. 5 Temporal and Spatial Comparison Data migration Mobile agent migration

6. 6 Performance Evaluation of Computing Paradigms Different conditions may affect the performance of computing paradigms, need to determine the affecting factors Need a thorough comparison of two paradigms, determine under which condition one paradigm performs better than the other

7. 7 Metrics Execution Time Energy Consumption m: number of mobile agents n: number of nodes each agent migrates : overhead of mobile agent : overhead of data file

8. 8 Simulation Method Using ns-2 4 experiments are designed In each experiment, only one parameter is changed Randomly deployed in a 10m by 10m area MAC layer protocol: 802.11 Routing protocol: DSDV Transmission power is 0.6W and receiving power is 0.3W Default parameters:

9. 9 Experiments and Results - 1 Effect of the number of nodes (p): Number of nodes changes from 2 to 30 (A) Execution Time(B) Energy Consumption

10. 10 Experiments and Results - 2 Effect of the number of mobile agents (m): 100 nodes, number of mobile agent changes from 1 to 50

11. 11 Experiments and Results - 3 Effect of data size/mobile agent size : the ratio changes from 1 to 50

12. 12 Experiments and Results - 4 Overhead ratio : changes from 0.1 to 4

13. 13 Discussion Situations to use the mobile agents computing paradigm –the number of nodes is large – is large In sensor networks with large amount of sensors, mobile agent computing paradigm provides an energy efficient solution

14. 14 Hybrid Computing Paradigms Scheme A Scheme B Scheme C Scheme D

15. 15 Simulation Results 100 nodes Keep other default parameters Number of clusters changes from 1 to 50

16. 16 Discussion Can further improve performance by dividing the sensor network into clusters and having different computing paradigms within clusters and between clusters

17. 17 Mobile Agent Planning (MAP) How to select a subset of sensor nodes? How to choose the order of migration? Mobile agent itinerary has a significant impact on –Energy consumption –Network lifetime –Fusion accuracy –Execution time

18. 18 Mobile Agent Planning Determine a mobile agent route that has low energy consumption, long network lifetime, and less execution time. Two branches –Static Mobile Agent Planning (SMAP): Derive an efficient path at a central processing center before dispatching the agents. Less computation, suitable for less dynamic environment –Dynamic Mobile Agent Planning (DMAP): Determine the route on the fly at each stop. Need more computation, suitable for dynamic environment

19. 19 Beacon Frames Beacons are periodically broadcasted by a sensor node to its neighbors Functions –Obtain location and measurement information from a neighbor node for the target localization algorithm –Calculate cost function values to the neighbor nodes –Indicate the aliveness of the neighbor nodes

20. 20 Which Sensor to Migrate to? Given –A set of neighbor nodes Find –A sensor i whose measurement z i gives greatest contribution to the success of the task Model of information gain A simplified model

21. 21 Dynamic Mobile Agent Planning Modeling Need to consider Energy consumption Information gain on the neighbor nodes Remaining energy on the neighbor nodes Define cost function Total cost is s.t. Decision

22. 22 Information-driven Dynamic Mobile Agent Planning Algorithm (IDMAP) Step 1: at t=0 Step 2: at time t Step 3: return to the processing center

23. 23 Dynamic Mobile Agent Planning

24. 24 Prediction of Target Movement Mobile agent on node A, which node, B or C, to migrate? Assume in very short interval, the direction and the speed of target are constant, so that Then the predicted position at The mobile agent at time t performs target localization to estimate the target location, it also carries the previous estimated target location.

25. 25 Predictive Information-driven Dynamic Mobile Agent Planning Algorithm (P-IDMAP) Step 1: at t=0 Step 2: at time t Step 3: return to the processing center

26. 26 Predictive Dynamic Mobile Agent Planning

27. 27 (a) Static itinerary result (b) Dynamic itinerary result (c) Predictive dynamic itinerary result

28. 28 Simulation and Algorithms Evaluation Develop a sensor network simulator in JAVA Metrics –Energy consumption: the total energy consumes to finish a processing task –Network lifetime: the time from node deployment to the time the first node is out of function because of energy depletion –The number of hops: reflects the time spent for the mobile agent to finish a task Parameters in simulation –Network area: 20m by 20m –Number of nodes: 500 –Sensing range: 10m –Beacon interval: 0.1s –Desired information gain: 18 Units –Initial energy: 36 Joule

29. 29 The Effect of the Target Speed (v) (A) Energy Consumption (B) Network lifetime (C) The number of hops

30. 30 The Effect of the Number of Nodes - Target Speed at 10m/s (A) Energy Consumption (B) Network lifetime (C) The number of hops

31. 31 Discussion Predictive Dynamic Itinerary algorithm is suitable for a wide range of target speed. It has advantages over other algorithms in terms of energy consumption, network lifetime, and the number of hops. It provides an energy efficient, near optimal, and fault tolerant itinerary solution for collaborative processing in wireless sensor networks.

32. 32 Implementation of MAF CSIP API (C++) SWIG Shared Libraries MA Daemon - Python Execution code and partial result Pickled/Unpickled SWIG Shared Libraries Diffusion API (C++) Sensoria RF modem API CSIP API (C++) SWIG Shared Libraries MA Daemon - Python Execution code and partial result Pickled/Unpickled SWIG Shared Libraries Diffusion API (C++) Sensoria RF modem API

33. 33 Reference H. Qi, Y. Xu, P. T. Kuruganti, “Chapter 41: The mobile agent framework for collaborative processing in sensor networks,” Frontiers in Distributed Sensor Networks. Editor: R. Brooks, S. S. Iyengar, pages 783-800, CRC Press, 2004. Y. Xu, H. Qi, “Mobile agent migration modeling and design for target tracking in wireless sensor networks,” Ad Hoc Networks (Elsevier) Journal, 6(1):1-16, January 2008. Y. Xu, H. Qi, “Distributed computing paradigms for multi-sensor data fusion in sensor networks,” Journal of Parallel and Distributed Computing, 64(8):945-959, August 2004. Y. Xu, H. Qi, “On mobile agent itinerary for collaborative processing,” IEEE Wireless Communications and Networking Conference (WCNC), vol. 4, pages 2324-2329, Las Vegas, NV, April 3-6, 2006. Y. Xu, H. Qi, P. T. Kuruganti, “Mobile-agent-based computing model for collaborative processing in sensor networks,” IEEE Global Telecommunications Conference (GLOBECOM), vol. 6, pages 3531 - 3535, Los Angeles, CA, December 2003. Y. Xu, H. Qi, “Performance evaluation of distributed computing paradigms in mobile ad hoc sensor networks,” The 9th IEEE International Conference on Parallel and Distributed Systems (ICPADS), pages 451-456, Taiwan, Dec 2002.