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

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

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

The 33 revised complete papers awarded have been rigorously reviewed and chosen from 87 submissions. The papers specialize in allotted computing matters in large-scale networked sensor platforms, together with systematic layout concepts and instruments; they disguise themes equivalent to allotted algorithms and functions, programming aid and middleware, facts aggregation and dissemination, defense, details fusion, lifetime maximization, and localization.

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

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The success ratio of DA is lower than that of B-RQ and LQ, because of high data loss due to contention caused by the high communication cost of DA (Figure 6(c)). Path Traversal Time : Figure 6(b) shows the path traversal time obtained by the different protocols. We see that DA achieves the least path traversal time. This is because DA has very low query latency compared to the other algorithms, as it requires the mobile entity to query only nearby nodes. The path traversal time of B-RQ is comparable to that of DA.

Another weakened variation is the approximate agreement problem [16] which allows inputs, decisions, and messages to be real numbers and requires the difference between any two decision values to be within a small tolerance and that any decision value is within the range of input values. Lamport invented the PAXOS algorithm [17] for a partially synchronous model of distributed systems. Asynchrony is considered timing failure. Other failures allowed are loss, duplication and reordering of messages, and crash failure of processes.

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. 8 Conclusion In summary, we propose a novel approach that integrates roadmap-based navigation with efficient query protocols for navigation in dynamic environments. We present the Roadmap Query (RQ) protocol that is specially optimized for collecting fresh data needed for navigation in the presence of dynamic obstacles and sensor node failures.

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