| Abstract: |
Widespread deployment of Internet of Things (IoT) sensor networks in sensitive industrial, medical and urban environments have elevated the fundamental questions regarding energy conservation, latency mitigation and structural security. It combines the unprecedented bandwidth and ultra-reliable low-latency communication (URLLC) of 5G communication architectures. Nonetheless, the nature of IoT nodes, which are highly distributed and vulnerable, makes it unable to withstand advanced cyber-attacks as sinkhole, blackhole and selective-forwarding attacks in traditional routing paradigms. This empirical exploration accounts application of a hybrid Particle Swarm Optimization (PSO) framework for potential applications to multi-objective secure routing in IoT sensor networks enabled by future 5G. The proposed Secure-PSO is using a dynamic evaluation matrix combined with standard physical constraints - including the residual energy of nodes in the transmission range, link quality and multi-hop distance - to model optimal cluster-head election and path selection trajectories through mathematics. Experiments were performed in multiple densities of 100-500 sensor nodes on a local 5G macro-cell grid. Localized adversarial injection Empirical data collection that was mainly based on quantified metrics (Network Lifetime, Average Energy Consumption, Packet Delivery Ratio (PDR), End-to-End Latency, and Throughput). Quantitative results showed that the Secure-PSO framework extended network lifetime by 34.2% over traditional Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols and kept an average PDR greater than 96.5% in case of 20% node attrition from malicious nodes as well. The mathematical parameters are significant, as statistically validated by rigorous Analysis of Variance (ANOVA) testing. Weed out of the box at the end, this paper makes two structural contributions to architectural engineering by demonstrating that metaheuristic algorithmic models can integrate defense |
