Abstract

Wireless Sensor Networks (WSNs) have expanded substantial attention owing to their wide variety of applications in various fields. However, energy consumption remains a critical challenge in WSNs, as the nodes are typically powered by limited battery resources. This paper addresses the energy consumption problem in WSNs by proposing a novel approach that combines the Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) algorithm, Dynamic Voltage and Frequency Scaling (DVFS) algorithm, and the duty cycling technique. The HF-GSO algorithm stands employed for the selection of effective cluster heads and routing in WSNs. It leverages the collective behavior of fireflies and glow-worms to achieve optimal energy utilization and network performance. By incorporating HF-GSO, the proposed approach optimizes the formation of clusters, minimizing the energy consumption associated with long-distance communication and data aggregation. Additionally, the DVFS algorithm is integrated into the system to energetically regulate the voltage and frequency levels of sensor nodes. This adaptive scaling mechanism allows the nodes to operate at lower power levels during periods of low activity, effectively reducing energy wastage. The DVFS algorithm further contributes to energy efficiency without compromising the network’s overall performance by scaling up the voltage and frequency only when necessary. Furthermore, the proposed approach utilizes duty cycling, a technique that enables the nodes to alternate between active and sleep modes. By effectively scheduling the node’s active and sleep durations, duty cycling significantly reduces idle listening and idle transmission, minimizing unnecessary energy consumption. The usefulness of the proposed method is demonstrated through extensive simulations and performance evaluations. The results indicate notable improvements in energy efficiency, network lifetime, and overall system performance compared to existing approaches. In conclusion, this research paper gives a complete solution to the energy consumption problem in WSNs. By integrating the HF-GSO algorithm, DVFS algorithm, and duty cycling, the proposed approach achieves significant energy savings and extends the lifetime of WSNs, making it highly suitable for energy-constrained WSN applications.

Authors

S. Bharathiraja¹, S. Selvamuthukumaran², V. Balaji³
A.V.C College of Engineering, India^1,2, Easwari Engineering College, India³

Keywords

Energy Consumption, Firefly algorithm, Glow Worm Swarm Optimization, Dynamic Voltage and Frequency Scaling, Duty Cycling, Wireless Sensor Networks