223 research outputs found
Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs
This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms
Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks
We propose an adaptive diffusion mechanism to optimize a global cost function
in a distributed manner over a network of nodes. The cost function is assumed
to consist of a collection of individual components. Diffusion adaptation
allows the nodes to cooperate and diffuse information in real-time; it also
helps alleviate the effects of stochastic gradient noise and measurement noise
through a continuous learning process. We analyze the mean-square-error
performance of the algorithm in some detail, including its transient and
steady-state behavior. We also apply the diffusion algorithm to two problems:
distributed estimation with sparse parameters and distributed localization.
Compared to well-studied incremental methods, diffusion methods do not require
the use of a cyclic path over the nodes and are robust to node and link
failure. Diffusion methods also endow networks with adaptation abilities that
enable the individual nodes to continue learning even when the cost function
changes with time. Examples involving such dynamic cost functions with moving
targets are common in the context of biological networks.Comment: 34 pages, 6 figures, to appear in IEEE Transactions on Signal
Processing, 201
Research Naval Postgraduate School, v.4, no. 14, October 2012
NPS Research is published by the Research and Sponsored Programs, Office of the Vice President and Dean of Research, in accordance with NAVSOP-35. Views and opinions expressed are not necessarily those of the Department of the Navy.Approved for public release; distribution is unlimited
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