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Optimizing Power-Efficient Spatial Query in Topology Management

Explore heuristic algorithms like Greedy, Centralized, and Decentralized for selecting connected sensors forming a logical routing topology. Research context includes Sensor Database, Art Gallery Problem, and geometric set cover problems. Learn about power-efficient organization and coverage concepts. Algorithm details and examples from Factory Warehouse scenario provide insight into query monitoring and aggregation. Discover the significance of power-efficient organization in Wireless Sensor Networks through exclusive covering sets and critical elements. Despite drawbacks of centralized communication, the Greedy algorithm presents a decentralized alternative. Motivated by limited battery power in Sensor Databases, the approach aims for optimal energy consumption while ensuring accurate query responses.

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Optimizing Power-Efficient Spatial Query in Topology Management

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  1. Topology Management--Power Efficient Spatial Query Presented by Weihang jiang

  2. Today’s plan • Introduction: 10-15 mins • Details : • heuristic algorithm 15 mins • Greedy • Centralized 10mins • Decentralized 5mins • Questions

  3. Problem definition • Select a small number of sensors that are sufficient to answer the query accurately. Also these selected sensors should form a connected communication path, so that they form a logical routing topology

  4. Context of research Sensor Database [4] Art Gallery Problem [20,10] Query operation Optimal placement VS Optimal selections Power efficient organization[25] Decentralized, cost of communication Geometric set cover problems [16 ,17 ,5] Notion of connectivity Connected sensor Cover Broadcast MDCS [14] [9,18,26,1,17] Nodes cover VS area cover

  5. Sensor database • P. Bonnet, J. Gehrke, and P. Seshadri. Towards sensor database systems. In Proc. of Intl. Conf. on Mobile Data Management, 2001. • Example • Factory Warehouse • Sensor Database • stored data: the set of sensors and environment • sensor data: produced by signal processing functions. • Query • Monitoring queries are long running. • The desired result of a query is typically a series of notifications of system activity • Queries need to correlate data produced simultaneously by different sensors. • Queries need to aggregate sensor data over time windows. • Most queries contain some condition restricting the set of sensors that are involved (usually geographical conditions). Back

  6. Art Gallery Problem • DEMO: • http://valis.cs.uiuc.edu/~sariel/research/CG/applets/art_gallery/artgal.html • http://www.cs.mcgill.ca/~thierry/507applet/triangulize.html Back

  7. Art Gallery Problem

  8. Broadcast -- MDCS • The idea is to suppress redundant broadcast by using only a small number of nodes to broadcast, but ensuring that all the nodes in the network receive the broadcast message • Coverage: all the area Back

  9. Back

  10. Power efficient organization Power Efficient Organization of Wireless Sensor NetworksSasa Slijepcevic, Miodrag Potkonjak • Choose nodes rather than deploy nodes • Divide sensors into mutually exclusive sets, each of those sets completely covers query area • Power saving • Divide to as many groups as possible

  11. Algorithm • Definition of field • A set of points. Two points belong to the same field iff they are covered by the same set of sensors • Critical Element • A field covered by the minimal number of sensors • 2,3,6,8 are critical elements • Find as many as possible exclusive covering sets • 1) Start with a critical element • 2) Then use objective function to choose one sensor which covers this critical element • 3) If now all the chosen sensor cover the query area • we got one exclusive covering set Goto 1) Else • Goto 1)

  12. objective function • (1) favor sets that cover a high number of uncovered elements (less sensors) • (2) favor sets that cover more sparsely covered elements • (3) favor sets that do not cover the area redundantly (more exclusive sets) • (4) favor sets that redundantly cover the elements that do not belong to sparsely covered areas

  13. The heuristic

  14. Power efficient organization(cont) • Drawbacks • Centralized • Communication cost Back

  15. Connected sensor Cover Compared with breath first flooding D+2qm VS 2qn (n>>m) Back

  16. Important definitions • Subelement; Valid Subelement • = Field ; a field in query area • Candidate Sensor; Candidate path • A sensor contains a Subelement which has not been chosen • A path connects a candidate sensor with previously chosen sensors • Uncovered Valid Subelement; Benefit of a Candidate path • Benefit = # of uncovered Valid Subelement / # of sensor on the path but not chosen

  17. Greedy algorithm (centralized) • Start with chosen sensor set M (the original sensor) • Find out SC (set of candidate sensors) • Basing on Benefit of a Candidate path, choose one candidate sensor, add it and the path into M • Goto beginning

  18. Decentralized • Instead send Candidate Path Search to the SC (set of candidate sensors, which is hard to find out locally), send CPS to the Candidate sensors around newest added sensor • Seems no much impact on # of selected sesors

  19. END!!! Question???

  20. Motivation • Sensor Database • Limited Battery Power

  21. Overview • Motivation

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