Efficient Resource Allocation for D2D-Enabled Social IoT Networks: A Tripartite and Time-Scale Optimization Approach

Abstract

In the densification of Device-to-Device (D2D)-enabled Social Internet of Things (SIoT) networks, improper allocation of resources can lead to high interference, increased signaling overhead, latency, and disruption of Channel State Information (CSI). In this paper, we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming (MINLP) problem. The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. The proposed approach prioritizes maintaining Quality of Service (QoS) and resource allocation to minimize power consumption while maximizing sum throughput. Simulated results demonstrate the superiority of the proposed algorithm over standard benchmark schemes. Validation of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17% to 93%. Additionally, the average time to deliver resources to CSI users is minimized by 60.83% through optimal power usage. This approach ensures QoS requirements are met, reduces system signaling overhead, and significantly increases D2D sum throughput compared to the state-of-the-art schemes. The proposed methodology may be well-suited to address the challenges SIoT applications, such as home automation and higher education systems.