Privacy-Preserved Ranking of Industrial Sensing Services Using Topological Information

The sensing-as-a-service paradigm has realized a number of contemporary industrial Internet-of-things applications. In this article, we have considered an order-driven scenario of a smart factory, where wearable devices provide various services and communicate with the service providers. The discovery process generates numerous smart factory services. The selection of an appropriate (or a set of) service(s) remains a challenge while preserving the service data privacy. It is required to involve an anonymous communication mechanism to design a privacy-preserved ranking model. The prevalent techniques for the prioritization of semantically equivalent sensor-provided services rely on quality-of-service (QoS) information. However, the QoS information is not always readily available at the node level. Moreover, the existing topological information-based (i.e., node importance and energy) solutions do not consider imperative features, such as a degree. The objective of this study is to design a privacy-preserved ranking model based on the onion routing technique and features along with the stochastic shortest route. Onion routing enables anonymous communication and prevents unauthorized entities from accessing ranking results. The weighted valuation is then derived to compute the cost of the homogeneous and dynamic sensor-provided services. Finally, the ranking is computed based on each service cost. The proposed model is extensively evaluated in two different network configurations of varying sizes. The evaluation results show that the proposed method performs 10% better in terms of ranking quality and 32% in terms of energy efficiency across different network configurations, as compared with the existing cost-based method while offering a desired level of privacy.