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Article type: Research Article
Authors: You, Ziyia; b; * | Chen, Shiguoa; b | Wang, Yia; b
Affiliations: [a] Department of Physics and Electronic Science, Guizhou Normal University, Guiyang, Guizhou, China | [b] Key Laboratory of Special Automotive Electronics Technology of the Education Department of Guizhou Province, Guiyang, Guizhou, China
Correspondence: [*] Corresponding author: Ziyi You, Department of Physics and Electronic Science, Guizhou Normal University, Guiyang, Guizhou 550001, China. E-mail:357534271@qq.com
Abstract: The data aggregation with multi-resources traffic information is a very important issue for intelligent vehicle systems (IVS). It can not only collect the most critical data from the traffic environment, but also prevent sensor network congestion. Unfortunately, we so far have no suitable solutions for WSN based IVSs. This paper presents an efficient approach for traffic data aggregation, applied in IVSs. At first, our approach adopts a hybrid network structure which is the combination of the chain structure and the unequal clusters structure. In this structure, all sensor nodes from the same cluster use parameters such as leading code and geographical position etc. to guarantee secure data aggregation. Furthermore, a method is introduced depending on creditability evaluation and reliability allocation so that the application layer can calculate aggregation results accurately, and then makes the decisions. Finally, the performance of the proposed scheme has been verified using simulation, showing that it is superior to similar protocol VLEACH (an improvement on LEACH) and ESDA (Efficient and Secure Data Aggregation protocol) such as the sensor nodes energy consumption and aggregation precision. The analysis result indicates that our scheme is effective and feasible in the next generation of sensor technologies of ITSs.
Keywords: Intelligent vehicle system, data aggregation, creditability, evidence theory, security analysis, performance evaluation
DOI: 10.3233/IDT-150242
Journal: Intelligent Decision Technologies, vol. 10, no. 2, pp. 105-114, 2016
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