Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
IEEE/ACM Transactions on Networking (TON)
Supporting asynchronous update for distributed data cubes
Journal of Network and Computer Applications
International Journal of Sensor Networks
Bernoulli sampling based (ε, δ)-approximate aggregation in large-scale sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
IKNOS: inference and knowledge in networks of sensors
International Journal of Sensor Networks
Processing continuous top-k data collection queries in lifetime-constrained wireless sensor networks
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Stochastically consistent caching and dynamic duty cycling for erratic sensor sources
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
MAX–MIN aggregation in wireless sensor networks: mechanism and modeling
Wireless Communications & Mobile Computing
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In this paper, we propose a novel approach for efficiently sensing a remote field using wireless sensor networks. Our approach, the infer algorithm, is fully distributed, has low overhead and saves considerable energy compared to using just the data aggregation communication paradigm. This is accomplished by using a distributed algorithm to put nodes into sleep mode for a given period of time, thereby trading off energy usage for the accuracy of the data received at the sink. Bayesian inference is used to infer the missing data from the nodes that were not active during each sensing epoch. As opposed to other methods that have been considered, such as wavelet compression and distributed source coding, our algorithm has lower overhead in terms of both inter-node communication and computational complexity. Our simulations show that on average our algorithm produces energy savings of 59% while still maintaining data that is accurate to within 7.9%. We also show how the parameters of the algorithm may be tuned to optimize network lifetime for a desired level of data accuracy.