Providing trusted data for industrial wireless sensor networks

Abstract

The deployment of wireless sensor networks, or WSNs, in industrial domains has attracted much attention over the past few years. An increasing number of applications have been developed such as for condition monitoring in the railway industry. Nevertheless, compared with traditional WSNs, the industrial environment is harsher, noisier, and more complex, which poses a higher requirement for the network security especially in terms of data trustiness and which further deters WSN practical integration in industrial applications. The main contribution of this research is to partially address the security issues by means of providing trusted data for industrial WSNs. To this end, a negative binomial distribution-based trust scheme combined with the D–S belief theory and a noise filter method is proposed and designed for industrial WSNs. In this paper, we first discuss the trust theory in WSNs and the disadvantages of traditional trust schemes for industrial applications, then analyze and evaluate the proposed method, and finally compare the performance of our method with some classic trust schemes. Through simulation tests about temperature readings of a factory workshop, it shows that the proposed method can improve the data trustiness, reliability, and robustness in the trust evaluation process under industrial environments and ensure the security of the network.