In-Network Query Processing in Massively Distributed Sensor Networks
This work discusses the principles and benefits of in-network query processing in distributed sensor networks. Each node maintains a small local database, allowing for efficient processing of queries such as identifying nodes with temperatures exceeding 35°C. Key advantages of this approach include scalability, reduced data storage needs, and resilience to node failures. Challenges include varying query processing times, energy consumption, and the impact of communication over multiple hops. The paper also explores various local graph structures and routing algorithms to optimize performance.
In-Network Query Processing in Massively Distributed Sensor Networks
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Presentation Transcript
Massively Distributed Database SystemsIn-Network Query Processing(Ad-Hoc Sensor Network) Spring 2014 Ki-Joune Li http://isel.cs.pusan.ac.kr/~lik Pusan National University
Basic Concepts – in-network query processing each node has - a local & tiny DB and - sensors a query "find the nodes where temperature is higher than 35oC" How to process it?
Why in-network query processing ? • scalable • No need to store the entire DB • Interact with neighbor nodes • A node failure is not critical • Issue • Query processing time determined by # of hops • Energy consumption • Battery is normally limited • Energy consumption for communication is relatively high • SQL-like query
Energy Consumption in S. Banerjee, A. Misra, http://pages.cs.wisc.edu/~suman/pubs/winet03.pdf
Energy Consumption Ptx Ptx Prx . . . r N r What does it imply?
Multi-hop instead of infrastructure network • No global network topology like TCP/IP • Network topology with its neighbors local stateless routing algorithm
Unit-Disk Graph • UDG: Graph G(N,E) where N is the set of nodes (sensors) andE is set of edges whose length is less than 1 (unit) • Types if UDG • RNG • Gabriel Graph • Delaunay Graph • Each node in V maintains the node IDs connected via edges in E
Gabriel Graph • Graph GG(V,E) • V is a set of nodes n (id, p) where p is a point in Euclidean space • E is a set of edge (a, b) that there is no other node within the closed disk of (a, b)
RNG – Relatively Neighborhood Graph • Graph RNG(V,E) • V is a set of nodes n (id, p) where p is a point in Euclidean space • E is a set of edge (a, b) that two points aand bby an edge whenever there is no third point cthat is closer to both aand bthan they are to each other (there is no other point within the intersection of the circles centered at a and b with radius the distance d(a, b))
Delaunay Triangulation Graph • Graph DTG(V,E) • V is a set of nodes n (id, p) where p is a point in Euclidean space • E is a set of edge e (a, b) where e is a side of triangle constructed byDelaunay Triangulation. • Delaunay Triangulation: for a set P of points in a plane is a triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P)
Routing - GPSR • in Brad Karp and H.T. Kung in MobiCom 2000, pp.243-254 • GPSR (Greedy Perimeter Stateless Routing) • A node x • broadcasts a query message with destination point D • the closest node y receives and forwards the message.
Example RNG with 200 nodes (subset of GG) full UDG with 200 nodes GG with 200 nodes (subset of full UDG) GG with 200 nodes over 2Km X 2Km where radio range is 250 m
Routing - GPSR circle(xD) • Problem • both of node x are fartherfrom the destination D • Right-Hand Rule: Perimeter • Combination of UNG and Perimeter routing