Adaptive Capacity Task Offloading in Multi-Hop D2D-Based Social Industrial IoT

Abstract

Traditional communication technologies such as cellular networks are facing problems to support high service quality when used for time-critical applications in an Industrial Internet-of-Things (IIoT) context, including real-time data transmission, route dependability, and scalability. To address these problems, device-to-device (D2D) communications based on social relationships can be used, which allow for task-offloading: resource-rich devices share unused computing resources with resource constraint devices. However, unbalanced task offloading in Social IIoT (SIIoT) might actually degrade the overall system performance, which is not desirable. In this paper, we propose an adaptive capacity task offloading solution for D2D-based social industrial IoT (ToSIIoT) which considers devices utilization ratio and strength of social relationships in order to improve resource utilization, increase QoS and achieve better task completion rate. The proposed approach consists of three aspects: social-aware relay selection in a multi-hop D2D communication context, choice of a resource-rich SIIoT device for task offloading, and adaptive redistribution of tasks. The paper proposes heuristic algorithms, as finding optimal solutions to the problems are NP-hard. Extensive experimental results show that the proposed ToSIIoT performs better than existing approaches in terms of utilization ratio, QoS violation, average execution delay, and task completion ratio.