Social-Internet of Things (S-IoT) fortifies the relationship between computing entities for efficient utilization of resources. S-IoT accentuates on building convivial circles between the devices, which sanction sharing of information without compromising own performance. Trust and privacy are the two factors responsible for the maintenance of these social circles. Trust establishes faith between the entities and privacy supports abstraction of device information. There exist a plethora of approaches, which fixate on trust and privacy in social networks. However, the subsisting solutions rely on a trust scoring system and utilize a single centralized server for these calculations. Such scoring systems are manipulable by eavesdroppers, which engender erroneous reputation leading to high trust values. Considering the desideratum of an efficient trust and privacy-preserving solution, this paper proposes a novel solution in the form of fission computing. The proposed approach relies on the edge-crowd integration for maintenance of trust and preservation of privacy rules in S-IoT. The proposed solution uses crowdsources as mini-edge servers and entropy modeling for defining trust between the entities. Fission managers are responsible for maintaining the privacy rules, which operate for every S-IoT application. The proposed approach is analyzed through numerical simulations by using a safe network as a performance benchmark. Further, the article presents a case study on the detection of fake news sources in S-IoT as an application of the proposed approach.
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Networks and Communications