Load Balance in addition to Efficient Hierarchical Data-Centric Storage in Sensor Networks

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Load Balance in addition to Efficient Hierarchical Data-Centric Storage in Sensor Networks

Murphy, Ginger, Fashion and Beauty Writer has reference to this Academic Journal, PHwiki organized this Journal Load Balance in addition to Efficient Hierarchical Data-Centric Storage in Sensor Networks Yao Zhao, List Lab, Northwestern Univ Yan Chen, List Lab, Northwestern Univ Sylvia Ratnasamy, Intel Research Outline Background in addition to Motivation Hierarchical Voronoi Graph based Routing Basic routing algorithm Practical design issues Evaluation Conclusions in addition to Future Work Generic Storage Schemes External Storage Local Storage Data-Centric Storage (DCS)

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Generic Storage Schemes External Storage Hotspot problem (if no need to store all events ) Generic Storage Schemes Local Storage Overhead of flooding Generic Storage Schemes Data-Centric Storage [CCR03] Good to avoid hotspots in addition to flooding overhead in some scenarios

Motivation Routing Primitive as long as Data-Centric Storage vs Any-to-any Routing DCS doesn’t require any-to-any routing E.g. in pathDCS [NSDI06], not all nodes are routable Any-to-any routing may not be suitable as long as DCS E.g. BVR[NSDI05] in addition to S4[NSDI07] Only a few any-to-any routing can be DCS routing E.g. VRR [Sigcomm06], GEM[Sensys03] Motivation Routing Primitive as long as Data-Centric Storage vs Any-to-any Routing Desirable Properties of DCS Routing No GPS (or other location device) Scalability Efficiency Path stretch (routing path length / shortest path length) Load Balancing In routing ( as long as warding overhead) In Storage Our Goal Design routing primitive as long as DCS with the above properties Outline Background in addition to Motivation Hierarchical Voronoi Graph based Routing Basic routing algorithm Practical design issues Evaluation Conclusions in addition to Future Work

Hierarchical Voronoi Graph based Routing Basic Routing Algorithm Hierarchical coordinate Region oriented routing Name based routing as long as DCS Practical Issues L in addition to mark selection Path stretch reduction H in addition to ling dynamic changes Voronoi Graph Hierarchical Coordinate Divide the network based on the hop distance to l in addition to marks Irregular borderline in realilty

Hierarchical Coordinate Divide the network based on the hop distance to l in addition to marks In smallest region, nodes know each other Overhead of Building Coordinate Initialization Overhead Each Layer O(mN) messages where m is the number l in addition to marks splitting a region, in addition to N is the number of nodes K Layers K ~ O(log N) Total Overhead O(mN·log N) messages Memory Usage Km ~ O(m·log N) Name Based Routing S has an event E Take a hash function H1 in addition to get j = H1(E)%3 S sends E to the jth 1st level l in addition to mark in addition to enter Lj’s region via node a Node a compute H2(E)%3 to determine the next l in addition to mark s L1 L1,2 L1,2,3 L2 L3 a Bypass l in addition to marks

Load Balancing in Storage Load Balancing Problem In naïve name based routing, non-uni as long as m division of regions causes non-uni as long as m storage distribution To divide regions uni as long as mly is very hard Our Approach: Non-uni as long as m Hash Function Collect the number of nodes in each region Hashed value is proportional to the population of possible sub-regions Outline Background in addition to Motivation Hierarchical Voronoi Graph based Routing Basic routing algorithm Practical design issues Evaluation Conclusions in addition to Future Work Evaluation Simulation Setup C++ implementation Simple MAC without collision Unit disk graph model in 2D space (communication range 1) Baseline simulation 3200 nodes Density: 3 neighbors in average Simulate HVGR, HVGR+ in addition to VRR[Sigcomm06] m = 6 (number of l in addition to marks splitting a region) Metrics Path stretch Load balancing: CDF as long as visualization Route table size Initialization overhead Maintenance overhead

Efficiency The stretch of HVGR doesn’t increase as N increase. Scalability The route table size in addition to initialization overhead increase logarithmically. Routing Load Balancing The routing load balancing feature of HVGR is close to that of shortest path routing.

Storage Load Balancing The storage load balancing feature of HVGR is close to that of ideal hash based storage. Conclusion Design HVGR/HVGR+ Topology based routing (No GPS) Good scalability (log N memory) High efficiency (close to shortest path routing) Balanced load in both routing in addition to storage Future Work Theoretical analysis Tinyos implementation Thanks! Q&A

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Name Based Routing as long as DCS Convert Name to Label Event name S A series of hash functions Hi Order the m l in addition to marks Let j = Hi(S) mod m, the ith level label is the j th l in addition to mark Voronoi Graph Voronoi Graph Divide the regions based on the closest l in addition to mark rule.

Outline Background in addition to Motivation Hierarchical Voronoi Graph based Routing Basic routing algorithm Practical design issues Evaluation Conclusions in addition to Future Work Region Oriented Routing From s to d with label (L1, L1,2, L1,2,3) s d L1 L1,2 L1,2,3 Bypass l in addition to marks a Hierarchical Coordinate Divide the network based on the hop distance to l in addition to marks

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