A Score-Based Game Approach Considering Resource Heterogeneity and Social Dynamics for Traffic Optimization in Social IoT Networks

The incorporation of human-like social concepts into the Internet of Things (IoT) has given rise to the paradigm of Social IoT (SIoT). In these networks, objects autonomously form social relationships to enhance network scalability in information and service discovery, focusing on their own benefits. However, social likeness or dislikeness among nodes can result in selfish behavior, adversely affecting network performance. Existing node stimulation mechanisms primarily focus on ad hoc and IoT networks, emphasizing topological structures and traffic patterns, while overlooking the social and behavioral factors crucial to the SIoT. This work proposes a novel node stimulation scheme for the SIoT that incorporates both social and behavioral characteristics and network topology. The mechanism employs a virtual currency-based game to incentivize cooperation by considering parameters such as proximity, energy levels, buffer size, correlated relays, and data quality. Additionally, social factors-including social preference, node importance, interaction history, and the probability of vital data transfer-are integrated into the decision-making process. Simulation results demonstrate that the proposed mechanism outperforms existing approaches in terms of energy efficiency, throughput, packet delivery ratio, and end-to-end delay, making it a robust solution for improving cooperation and performance in SIoT networks.