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Synopsis diffusion for robust aggregation in sensor networks
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OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
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SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
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IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
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IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
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A Shape Recognition Scheme for Wireless Sensor Networks Based on a Distance Field Method
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
Information storage, reduction and dissemination in sensor networks: a survey
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
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Future Generation Computer Systems
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ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Think globally, act locally: on the reshaping of information landscapes
Proceedings of the 12th international conference on Information processing in sensor networks
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We present a robust approach to data collection, aggregation, and dissemination problems in sensor networks. Our method is based on the idea of a sweep over the network: a wavefront that traverses the network, passes over each node exactly once, and performs the desired operation(s). We do not require global information about the sensor field such as node locations. Instead, in a preprocessing phase, we compute a potential function over the network whose gradients guide the sweep process. The sweep itself operates asynchronously, using only local operations to advance the wavefront. The gradient information provides a local ordering of the nodes that helps reduce the number of MAC-layer collisions as the wavefront advances, while also globally shaping the wavefront so as to conform to the sensor field layout. The approach is robust to both link volatility and node failures that may be present in real network conditions. The potential is computed by a stable diffusion process in which each node repeatedly set its potential to the average of the potentials of its neighbors. Aggregation paths are decided on-line as the sweep proceeds and no fixed tree structure is needed over the course of the computation. We present simulation results illustrating the correctness of the algorithm and comparing the performance of the sweep to aggregation trees under various network conditions.