A Sleep Scheduling Mechanism Based on Power Law Distribution for Mobile Delay Tolerate Networks

Feng, Yankun; Bai, Xiangyu; Na, Dang; Shufang, Wang

doi:10.1109/cyberc.2015.102

Cited by 3 publications

(2 citation statements)

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“…Besides, in practical applications, in order to obtain stringent accuracy requirements for target monitoring while maximizing network lifetime in WSN-based applications, only a few sensors are incessantly active while most of others are activated occasionally, which also lead to long tail issue. A typical example is sleep scheduling mechanism [25] in WSNs, which brings the result that most sensors only provide a few observations and only a few sensors make many observations [26], [27], which often causes some nodes with very few observations. As for the long-tail phenomenon, a confidence-aware truth discovery method is proposed in [8] to automatically estimate truths from conflicting data with long-tail issue.…”

Section: Related Workmentioning

confidence: 99%

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Estimate Information Fusion Weight of WSNs Nodes Based on Truth Discovery Optimization Method Among Conflicting Sources of Data

2019

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In practical wireless sensor networks (WSNs)-based applications, often only a few nodes are active while most of the others are activated occasionally due to limited resource of the WSNs. Thus most sensors only provide a few observations and only a few sensors make many observations, which often cause long-tail issue to undermine information fusion performance. So we present a confidence-aware information fusion scheme named CAIF to solve such a problem. In particular, we first make a quantitative study on the long-tail data phenomenon and the relationship between node-target distance and node sensing capability, which can provide a guideline to improve node weight estimation error caused by long-tail data. Then, we propose a truth discovery-based method in WSNs via incorporating node-target distance into the truth discovery optimization solution framework to infer the sensor node's fusion weight. In order to adapt to the distribution characteristic of the WSNs, we propose a distributed implementation to estimate the sensor nodes' weights via confidence level and node-target distance to further improve the fusion performance. Besides, the iterative process shown in CAIF converges to a stationary point of the optimization problem and its time complexity is linear with respect to the total number of observations. Finally, we conduct extensive experiments on real data to validate and evaluate CAIF. The experimental results demonstrate the superior performance of our method over existing solutions in terms of root-mean-square error and accuracy.

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Section: Related Workmentioning

confidence: 99%

“…(13). Although two sensor nodes with different numbers of observations, which is caused by missing data or sleep scheduling mechanism [25], may have the sameσ 2 s , the confidence interval of σ 2 n for these two nodes can be significantly different.…”

Section: ) Case 1: Truth Calculationmentioning

confidence: 99%