Download Distributed Computing in Sensor Systems: Second IEEE by Christopher J. Rozell, Don H. Johnson (auth.), Phillip B. PDF

By Christopher J. Rozell, Don H. Johnson (auth.), Phillip B. Gibbons, Tarek Abdelzaher, James Aspnes, Ramesh Rao (eds.)

The publication constitutes the refereed lawsuits of the second one foreign convention on disbursed Computing in Sensor platforms, DCOSS 2006, held in San Francisco, California, united states in June 2006.

The 33 revised complete papers offered have been rigorously reviewed and chosen from 87 submissions. The papers specialise in allotted computing concerns in large-scale networked sensor structures, together with systematic layout suggestions and instruments; they conceal subject matters corresponding to dispensed algorithms and purposes, programming help and middleware, facts aggregation and dissemination, defense, details fusion, lifetime maximization, and localization.

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Read or Download Distributed Computing in Sensor Systems: Second IEEE International Conference, DCOSS 2006, San Francisco, CA, USA, June 18-20, 2006. Proceedings PDF

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Additional resources for Distributed Computing in Sensor Systems: Second IEEE International Conference, DCOSS 2006, San Francisco, CA, USA, June 18-20, 2006. Proceedings

Sample text

1 0 35 2500 Start time 50s Start time 200s Number of msgs Success Ratio Roadmap Query for Sensor Network Assisted Navigation 2000 Start time 50s Start time 200s 1500 1000 500 0 B-RQ R-RQ LQ (a) Success Ratio DA B-RQ R-RQ LQ (b) Communication Cost Fig. 7. Performance comparison in the presence of node failures more than that of B-RQ since it requires nodes to respond if they have failed neighbors even if their temperatures are below the threshold. More specifically, R-RQ achieves 73% and 69% savings in communication cost over LQ, at a start time of 50s and 200s, respectively.

2. Roadmap Query (RQ) Algorithm hood information is also required by other common services such as routing and power management. RQ uses two rules to determine which nodes should forward the query message or respond to the query. We call the rule that determines if a node should forward the query message, as the forwarding rule and the rule that determines if a node should respond to a query, as the reply rule. The forwarding rule identifies backbone nodes while the reply rule identifies non-backbone nodes.

The backbone and non-backbone nodes form a tree structure with the mobile entity as the root. The formed tree is used to aggregate and deliver the query results to the mobile entity. Thus, RQ reduces communication cost not only by reducing the number of nodes that forward the query message but also by reducing the number of nodes that respond to a query, within a query area. In order to achieve communication cost reduction, RQ requires the queried nodes to have knowledge of the roadmap and to maintain 2-hop neighborhood information.

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