DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks Shan-Hung Wu Kun-Ta Chuang Chung-Min Chen Ming-Syan Chen

Outline Introduction Related Work Design of DIKNN KNN Boundary Estimation Performance Evaluation Conclusion

Introduction The problem of efficient KNN search has been a major research topic –traditional KNN query processing techniques assume location data are available in a centralized database and focus on improving the index performance. –“ in-network ” KNN query processing techniques for sensor networks rely on certain in-network infrastructure (index or data structure)distributed among the sensor nodes. Drawbacks of the in-network approaches in large-scale mobile sensor network: distributed indexing structrues, supernodes, fixed network

Introduction A Density-aware Itinerary KNN query processing (DIKNN) for mobile sensor networks –key idea: let sensor nodes collect partial results and propagate the query along a well-devised,conceptual itinerary structure –the first KNN processing technique dose not rely on any in-network indexing structure support: no constant maintenance or fixed data aggregation point Several challenging issues arising in the design of DIKNN: estimate of search radius and design of efficient itinerary

Related Work The centralized approach performs the queries in a centralized database containing locations of all the sensor nodes. The in-network approach propagates the query directly among the sensor nodes in the network and collects relevant data to form the final result.

Related Work The Peer-tree and DSI decentralize the index structures (R-tree) to distributed environments. A network is partitioned into a hierarchy of Minimum Bounding Rectangles (MBRs). For KNN, index nodes become system bottlenecks easily and there are many unnecessary hops.

Related Work KPT is proposed to handle the KNN query without fixed indexing. The KNN Perimeter Tree (KPT) builds upon GPSR for processing KNN queries. Two serious drawbacks: considerable overhead and conservative boundaery

Design of DIKNN Definitions and Network Model –Definition 1 (k nearest neighbor problem) Given a set of sensor nodes S, a geographical location q (i.e., query point), and valid time T, find a subset S ’ of S with k nodes (i.e., S ’ ⊆ S, |S ’ | = k) such that at time T, ∀ n1 ∈ S ’,n2 ∈ S−S ’ :DIST(n1, q)≤DIST(n2, q), where DIST denotes the Euclidean distance function. –Network is under ad-hoc mode; all sensor nodes can store data locally and answer the queries individually; moving speed and directions are arbitrary; each sensor node is aware of its geo- location and maintains a table of IDs and locations of neighbor nodes falling within its radio range.

Design of DIKNN Execution Phases –Routing phase: Q is geographically routed from s to the nearest neighbor around q –KNN boundary estimation phase:the home node estimates KNN boundary with radius R using KNNB algorithm –Query dissemination phase: the home node disseminates the query message to all sensor nodes inside the KNN boundary

KNN Boundary Estimation Routing Phase –Q is routed from s to the nearest neighbor n p around q utilizing the geographic face routing protocol (e.g.GPSR). An additional list L about information of the sensor network is sent along with Q. – On the i th hop to the destination,the corresponding node appends its own location loc i and the number of newly encountered neighbors enc i to L.

KNN Boundary Estimation Linear KNNB Algorithm –On receiving the query and list L,N q estimats the KNN boundary by determining its radius length R. Determination of R must balance two conflicting factors. –A weaker assumption adopted by KNNB: sensor node are uniformly distributed only within the optimal KNN boundary (the boundary containing exactly k nearest neighbors).

KNN Boundary Estimation : the radius of the optimal KNN boundary n i : the corresponding node of the i th hop in the routing path D :density of nodes (nodes/m 2 ) within the optimal KNN boundary

KNN Boundary Estimation

Design of DIKNN Itinerary-Based Query Dissemination –On the KNN boundary is determined, the home node enters the query dissemination phase. Q-nodes are chosen for query dissemination D-nodes: neighbor nodes that are qualified to reply the query

Design of DIKNN Primitives of itinerary-based solution –The itinerary width w specifies how close between segments of an itinerary. w= r/2 –Data collection from multiple D-nodes needs to be better scheduled to avoid collisions and delays. timer= –Discussions on the other issues (i.e., itinerary void)

Design of DIKNN Concurrent itinerary structures KNN boundary is partitioned into multiple sectors. In each sector,the query is propagated along a sub-itinerary. The distance between sub-itineraries in adjacent sectors is w to ensure full coverage of the KNN boundary when w ≤ r/2.

Design of DIKNN Each sub-itinerary consists of three segments: the init-,adj- and peri-segments.

KNN Boundary Estimation Interaction with Environments –Spatial Irregularity :when k is large, the sensor nodes tend to irregularity and their density becomes unpredictable. Inverse the direction of peri-segments in every interseptal sector. In such a configuration, the face-to-face adj-segments of different sub-itineraries together form rendezvous.

KNN Boundary Estimation Interaction with Environments –Mobile Concern: Mobility of sensor nodes degrades query accuracy because nodes may move in or move out the KNN boundary during dissemination. –DIKNN address this issue in the query dissemination phase, where the last Q-node is obligated to determine how much farther a sub- itinerary should continue. g :assurance gain,0≤g≤1 μ:the fastest moving speed R’= R+g(te − ts)μ, ts and te denote timestamps for the start and end of the query dissemination.

Performance Evaluation Settings and Performance Metrics –Query Latency: The elapsed time (in second) between the time a query is issued by the sink and the time the query responses are returned. –Energy Consumption: Amount of energy (in Joule) consumed in a simulation run. –Query Accuracy: the pre-accuracy and post-accuracy are measured separately in experiments.

Performance Evaluation Scalability

Performance Evaluation Impact of Network Dynamics

Conclusion DIKNN integrates query propagation with data collection along a well-designed itinerary traversal. A simple and effective KNNB algorithm has been proposed to estimate the KNN boundary under the trade-off between query accuracy and energy efficiency. Dynamic adjustment of the KNN boundary has also been addressed to cope with spatial irregularity and mobility of sensor nodes.

Finish Thank you!