Distance-, Energy-, and Density-Aware SOM Clustering-Based Routing in Low-Power IoT-Enabled WSNs

In the era of 6G, addressing the issues of constrained-resource and nonchargeable devices in Internet of Things (IoT)-enabled wireless sensor networks (WSNs) is an urgent need due to the exponential increase in data traffic generated by numerous applications and services (AASs). This article proposes a distance-, energy-, and density (DED)- aware self-organizing map (SOM) clustering-based routing (DEDSCR) scheme to improve the performance of low-power IoT-enabled WSN. The DEDSCR scheme utilizes the total sum of squared distance-based elbow (SDE) method, SOM, fuzzy logic, and ant colony optimization (ACO) to identify the number of clusters, organize the sensor nodes into the clusters, select the cluster heads (CH), and determine the optimal path from the CHs to the base station (BS). In this scheme, the fuzzy model plays an important role in selecting the proper CHs and generating the best heuristic values for clustering and routing based on the inputs from the distance toward the BS and the remaining energy and density of sensor nodes. Simulation results are presented to demonstrate the advantages of the proposed DEDSCR scheme in terms of lifespan and energy consumption compared to other methods. The impacts of SDE, SOM, fuzzy, and ACO on the performance metrics are also discussed and analyzed to provide valuable insights into the design of clustering and routing for low-power IoT-enabled WSN.