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Topology Management --Power Efficient Spatial Query Presented by Weihang jiang Today’s plan Introduction: 10-15 mins Details : • heuristic algorithm 15 mins • Greedy Centralized Decentralized Questions 10mins 5mins 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 Context of research Sensor Database [4] Art Gallery Problem [20,10] Query operation Optimal placement VS Optimal selections Geometric set cover problems [16 ,17 ,5] Broadcast MDCS [14] [9,18,26,1, 17] Power efficient organization[25] Decentralized, cost of communication Notion of connectivity Connected sensor Cover 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 Art Gallery Problem DEMO: http://valis.cs.uiuc.edu/~sariel/resea rch/CG/applets/art_gallery/artgal.ht ml http://www.cs.mcgill.ca/~thierry/50 7applet/triangulize.html Back Art Gallery Problem 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 Back 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 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) 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 The heuristic Power efficient organization(cont) Drawbacks •Centralized •Communication cost Back Connected sensor Cover Compared with breath first flooding D+2qm VS 2qn (n>>m) Back 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 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 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 END!!! Question??? Motivation Sensor Database Limited Battery Power Overview Motivation