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Collaborative Processing in Sensor Networks Lecture 2 - Mobile-agent-based Computing

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

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Collaborative Processing in Sensor Networks Lecture 2 - Mobile-agent-based Computing

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  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. Research Focus - Recap • Develop energy-efficientcollaborative 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. Architecture of Mobile Agent method • Itinerary • Route of migration • Identification • Unique for each mobile agent • Data buffer • Carries the partially integrated results • Method • Execution code carried with the agent itinerary data buffer identification 160.10.30.100

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

  5. Temporal and Spatial Comparison Data migration Mobile agent migration

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

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

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

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

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

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

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

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

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

  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 Decision Total cost is s.t.

  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. Dynamic Mobile Agent Planning

  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 The mobile agent at time t performs target localization to estimate the target location , it also carries the previous estimated target location . Then the predicted position at

  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. Predictive Dynamic Mobile Agent Planning

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

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

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

  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.

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