Computational Offloading for Efficient Trust Management in Pervasive Online Social Networks Using Osmotic Computing

Abstract

Pervasive social networking (PSN) aims at bridging the gap between the services and users by providing a platform for social communication irrespective of the time and location. With the advent of a new era of high-speed telecommunication services, mobile users have evolved to a large extent demanding secure, private, and trustworthy services. Online social networks have evolved as pervasive online social networks (POSNs), which uses a common platform to connect users from hybrid applications. Trust has always been a concern for these networks. However, existing approaches tend to provide application-specific trust management, thus resulting in the cost of excessive network resource utilization and high computations. In this paper, a pervasive trust management framework is presented for POSNs, which is capable of generating high trust value between the users with a lower cost of monitoring. The proposed approach uses a flexible mixture model to develop the system around six different properties, and then utilizes the concept of osmotic computing to perform computational offloading, which reduces the number of computations as well as computational time. The novel concepts of lock door policy and intermediate state management procedure are used to allow trust visualization by providing efficient identification of trustworthy and untrustworthy users. The proposed approach is capable of predicting user ratings efficiently with extremely low errors, which are in the range of +/- 2%. The effectiveness of the proposed approach is demonstrated using theoretical and numerical analyses along with data set-based simulations.