Enhanced IoT Supply Chain Optimization through Two-Tier Spectral Clustering and Fuzzy-Logic Pathfinding

Abstract

Web of Things integrated with logistics needs a plenty of smart devices broadly distributed across total logistics systems to track resources and monitor environmental conditions. These internet enabled resources communicate wirelessly to convey logistical information to a central monitoring station, which then sends this information through internet for analysis. However, the huge rate of information transmission rapidly depletes the energy of monitoring devices, thereby reducing the functional life expectancy of the monitoring system. To overcome this issue, we propose an enhanced clustering and routing strategy known as the Fuzzy Spectral Clustering and Routing Algorithm (FSCRA). FSCRA influences the strengths of Signless Laplacian matrices to facilitate effective resource grouping and fuzzy logic to manage logistics uncertainties, for smart decision-making. Moreover, a dual-layer clustering basis is presented to split the logistic system into local and master tiers, which effectively distributes the communication load and justifying congestion in extensive global distributions. Also, A* algorithm is used in this work to identify optimal logistics paths among the master cluster heads to minimize functional costs and energy consumption. By combining these techniques, FSCRA not only ensures well-adjusted resource distribution among nodes but also improves the overall logistics visibility and performance. Wide-ranging simulations were conducted to assess the efficiency of FSCRA related to existing logistics management methodologies. The outcomes reveal that FSCRA suggestively improves key performance metrics, including packet delivery ratio, resource monitoring time, and energy consumption. This recommends that FSCRA stands as a strong and feasible solution for optimizing modern internet enabled logistics while maintaining high levels of data transmission reliability and efficiency.