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ABSTRACT
Future wireless sensor networks (WSNs) will transport high-bandwidth, low-latency streaming data, and will host sophisticated processing, such as image fusion and object tracking, in-network on sensor network nodes. Recent middleware proposals provide capabilities for in-network processing, reducing energy drain based on communication costs alone. However, hosting complex processing on WSN nodes incurs additional processing energy and latency costs that impact network lifetime and application performance. There is a need for a WSN planning framework to explore energy saving and application performance trade-offs for models of future sensor networks that account for processing costs in addition to communication costs. In this work, we present a simulation framework to analyze the interplay between resource requirements for compute-and communication-intensive in-network processing for streaming applications. We simulate a surveillance application workload with middleware capabilities for data fusion, adaptive policy-driven migration of data fusion computation across network nodes, and prefetching of streaming data inputs for fusion processing. Our study sheds light on application figures of merit such as latency, throughput, and lifetime with respect to migration policy and node CPU and radio characteristics.
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CITED BY 2
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Umakishore Ramachandran , Rajnish Kumar , Matthew Wolenetz , Brian Cooper , Bikash Agarwalla , Junsuk Shin , Phillip Hutto , Arnab Paul, Dynamic data fusion for future sensor networks, ACM Transactions on Sensor Networks (TOSN), v.2 n.3, p.404-443, August 2006
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