George, K. J

Trust plays a crucial role in establishing and retaining relationships. Sociopsychological analysis identifies three major constructs, such as ability, benevolence and integrity, upon which trust is being built up. On a similar note, in a Wireless Sensor Network (WSN), it is indispensable to have trust among nodes since nodes collectively sense physical parameters and send them to the base station. The nodes, however, can behave fraudulently and send bad information, mostly due to hardware and software faults. Taking inspiration from the sociopsychological account, the present paper introduces a novel model for computing trust of sensor nodes. Additionally, the immune inspired model is suggested for removing fraudulent nodes whose trust ratings fall below the threshold. Roles of the three factors, viz. ability, benevolence and integrity, are examined in WSN domain. The proposed model proves itself to be more advantageous than other methods that adopt machine learning and neural network models in performance metrics such as detection time, reliability, scalability, efficiency and complexity. Proposed work has been implemented on LabVIEW platform and the results substantiate the reliability of the proposed mathematical model.

In any connected network, resource scarcity, perceived road blocks, and incongruent objectives can potentially ensue conflicts among stakeholders. In the existing literature, trust has been cited as a crucial component in effective conflict management (CM). Besides trust, empathy, and social intelligence (SI) play decisive roles in enhancing cooperation, encouraging information sharing, and promoting problem solving. In this article, we discuss the three major components of CM and propose a computational model, which is inspired from social psychology for CM in connected vehicles (CVs). Our mathematical algorithm focuses on three factors, namely, trust, empathy, and SI that are learned via social interactions among vehicles to ensure safety of vehicles and passengers. The triad of trust, empathy, and SI is used to aid reinforcement learning (RL) for obtaining the optimal q -values and rewards in the shortest duration of time in the CV network. We have examined how the three factors influence the learning process and analyzed their CM potentials. Results show that the proposed model is 118.18% more efficient than the trust-only-based RL algorithm.