Optimizing Energy Efficiency in IoT-Enabled Wireless Sensor Networks Using an Integrated EEKA-K-means Approach

Substantial growth in the Internet of Things (IoT) has raised the demand for high-performance wireless sensor networks (WSNs) that can gather and send information from remote locations.However, the constrained energy resources of sensor nodes provide substantial hurdles to extending the network's operating lifetime.This paper offers a unique strategy for enhancing energy management and performance in IoT-enabled WSNs that combines the Energy-Efficient Knapsack Algorithm (EEKA) with K-means clustering.EEKA optimizes sensor node transmission power levels while considering energy limits and network topology.Clustering using the K-means algorithm organizes nodes by energy and proximity for effective data transmission and load balancing.Simulation results demonstrate that the proposed EEKA-K-means strategy enhances network performance compared to baseline methods, including EEKA, SMOFCM, PSO-GA and LEACH.Specifically, the network lifetime increased by 1% to 7%, energy consumption decreased by 12%, and throughput improved by 5.4% to 12.5%.Additionally, the Packet Delivery Ratio (PDR) improved by 1.1% to 6.8%, and the number of active nodes increased by 40%.These results were obtained using simulations conducted on a network of 100 sensor nodes randomly deployed in a 100m 100m area, with initial energy set to 2J per node, assuming a heterogeneous energy model and a constant bit rate (CBR) traffic model.