Residual Energy Based Cluster-head Selection in WSNs for IoT Application (1902.01445v1)

Abstract: Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

• The paper introduces R-LEACH, a novel protocol that selects cluster-heads based on residual energy to overcome LEACH's limitations.
• Simulation results show a 60% boost in throughput and a 66% increase in network lifetime compared to traditional LEACH.
• This protocol enhances energy efficiency in IoT applications, extending network viability in energy-constrained environments.

Residual Energy-Based Cluster-head Selection in WSNs for IoT Applications

The paper presents an innovative adaptation of the LEACH protocol intended to improve efficiencies in Wireless Sensor Networks (WSNs) as they apply to the Internet of Things (IoT). Recognizing the central role of energy consumption and network longevity in the deployment of WSNs, the authors propose a novel protocol named R-LEACH, which addresses the inherent limitations of the LEACH protocol through intelligent cluster-head (CH) selection based on residual energy.

Summary of Contributions

The core contribution of the paper is the development of the R-LEACH protocol, which improves upon the LEACH protocol's methodology for cluster-head selection. Traditionally, LEACH randomly assigns cluster-heads in each round without regard to residual node energy, which leads to prematurely dying nodes and a decreased network lifespan. R-LEACH circumvents this oversight by implementing a selection process where nodes with the highest residual energy are preferenced as cluster-heads. This intelligent selection algorithm integrates key parameters such as the residual energy, initial energy, and an optimal number of clusters to bolster overall performance.

Simulation Results

Simulation results demonstrated that R-LEACH significantly enhances network throughput, lifetime, and residual energy metrics. Specifically, the proposed protocol improved throughput by 60%, network lifetime by 66%, and residual energy by 64% compared to the conventional LEACH protocol. These robust performance metrics underscore the protocol's efficacy in extending the viability of IoT applications reliant on WSNs, particularly in environments where energy resources are constrained, and node replacement is impractical.

Implications and Future Directions

The theoretical and practical implications of the proposed R-LEACH protocol are substantial. By optimizing cluster-head selection based on residual energy, WSNs can achieve more considerable energy efficiency, reducing total energy consumption and prolonging the operational lifetime of the network. This holds profound potential for IoT applications such as environmental monitoring and smart city systems, where sensor networks must operate reliably over extended periods without human intervention.

The paper also opens avenues for future research, including exploring more sophisticated models that incorporate additional parameters such as mobility patterns and node heterogeneity into the cluster-head selection algorithm. Furthermore, adapting the protocol to function within dynamic network conditions where the nodes are not fixed but mobile presents an intriguing challenge.

In conclusion, the authors present a compelling case for the adoption of R-LEACH in WSNs dedicated to IoT infrastructure. The enhancements over LEACH demonstrate not only theoretical advancements but offer practical benefits for real-world applications, supporting the continued growth and development of IoT systems.