Articlegold2014

Novel Algorithm for Identifying and Fusing Conflicting Data in Wireless Sensor Networks

Zhenjiang Zhang · Tonghuan Liu · Wenyu Zhang

99 citations0/0 files storedSensors

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Multi-Criteria Decision Making Target Tracking and Data Fusion in Sensor Networks Distributed Sensor Networks and Detection Algorithms

Abstract

There is continuously increasing interest in research on multi-sensor data fusion technology. Because Dempster's rule of combination can be problematic when dealing with conflicting data, there are numerous issues that make data fusion a challenging task, including the exponential explosion, Zadeh Paradox, and one-vote veto. These issues lead to a great difference between the fusion results and real results. This paper applies the idea of analyzing distance-based evidence conflicts, introduces the concept of vector space, and proposes a new cosine theorem-based method of identifying and expressing conflicting data. In addition, this paper proposes a new data fusion algorithm based on the degree of mutual support between beliefs, which is based on the Jousselme distance-based combination rule proposed by Deng et al. Simulation results demonstrate that the presented algorithm achieves great improvements in both the accuracy of identifying conflicting data and that of fusing conflicting data.

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References

1987On the dempster-shafer framework and new combination rules 1990The combination of evidence in the transferable belief model 2000Combining belief functions when evidence conflicts 2001A new distance between two bodies of evidence 1988Representation and combination of uncertainty with belief functions and possibility measures 2004Decision making in the TBM: the necessity of the pignistic transformation 2004Combining belief functions based on distance of evidence 2002Belief function combination and conflict management

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2017A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion 2017A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis 2016Conflict management based on belief function entropy in sensor fusion 2019An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure 2019A Novel Fuzzy Approach for Combining Uncertain Conflict Evidences in the Dempster-Shafer Theory

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