Novel Algorithm for Identifying and Fusing Conflicting Data in Wireless Sensor Networks
Open Access
- 30 May 2014
- Vol. 14 (6), 9562-9581
- https://doi.org/10.3390/s140609562
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.Keywords
This publication has 16 references indexed in Scilit:
- How to preserve the conflict as an alarm in the combination of belief functions?Decision Support Systems, 2013
- Toward an Axiomatic Definition of Conflict Between Belief FunctionsIEEE Transactions on Cybernetics, 2013
- Singular sources mining using evidential conflict analysisInternational Journal of Approximate Reasoning, 2011
- Analyzing the degree of conflict among belief functionsArtificial Intelligence, 2006
- Decision making in the TBM: the necessity of the pignistic transformationInternational Journal of Approximate Reasoning, 2005
- Belief function combination and conflict managementInformation Fusion, 2002
- A new distance between two bodies of evidenceInformation Fusion, 2001
- The combination of evidence in the transferable belief modelIEEE Transactions on Pattern Analysis and Machine Intelligence, 1990
- Representation and combination of uncertainty with belief functions and possibility measuresComputational Intelligence, 1988
- On the dempster-shafer framework and new combination rulesInformation Sciences, 1987