Title: Big uncertain data of multiple sensors efficient processing with high order multi-hypothesis: an evidence theoretic approach

Authors: Hossein Jafari; Xiangfang Li; Lijun Qian; Alexander J. Aved; Timothy S. Kroecker

Addresses: CREDIT Research Center, Prairie View A&M University, Texas A&M University System, Prairie View, TX, USA ' CREDIT Research Center, Prairie View A&M University, Texas A&M University System, Prairie View, TX, USA ' CREDIT Research Center, Prairie View A&M University, Texas A&M University System, Prairie View, TX, USA ' Information Directorate, US Air Force Research Laboratory (AFRL), Rome, NY, USA ' Information Directorate, US Air Force Research Laboratory (AFRL), Rome, NY, USA

Abstract: With the proliferation of IoT, numerous sensors are deployed and big uncertain data are collected due to the different accuracy, sensitivity range, and decay of the sensors. The goal is to process the data and determine the most potential hypothesis among the set of high order multi-hypothesis. In this study, we propose a novel big uncertain sensor fusion framework to take advantage of evidence theory's capability of representing uncertainty for decision making and effectively dealing with conflict. However, the methods in evidence theory are in general very computationally expensive, thus they may not be directly applied to multiple data sources with high cardinality of hypotheses. Furthermore, we propose a Dezert-Smarandache hybrid model that can apply to applications with high number of hypotheses while the computational cost is reduced. Both synthetic and real data from experiments are used to demonstrate the feasibility of the proposed method for practical situation awareness applications.

Keywords: Dezert-Smarandache theory; DSmT; Dempster-Shafer theory; DST; internet of things; IOT; comfort zone; uncertain data fusion; multiple sensor; multi-hypothesis.

DOI: 10.1504/IJBDI.2018.092663

International Journal of Big Data Intelligence, 2018 Vol.5 No.3, pp.177 - 190

Received: 10 Jan 2017
Accepted: 20 Apr 2017

Published online: 30 Oct 2017 *

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