An Adaptive Clustering Routing Protocol for Wireless Sensor Networks Based on a Novel Memetic Algorithm

Abstract

Wireless sensor networks (WSNs), as an important part of the Internet of Things (IoT), have developed rapidly in recent years. WSNs comprise spatially distributed sensor nodes to collect information from the environment. The energy of nodes is usually limited, so the speed of energy consumption determines the lifespan of the network. This article proposed an energy-efficient adaptive clustering routing algorithm named multiparent differential evolution (MPDE) and variable step-size local search swarm-intelligence-based adaptive clustering routing (VSSLS-SIACR) to minimize the energy consumption of WSNs and prolong the network lifespan. Here, SIACR is a SIACR protocol, while MPDE and VSSLS are a novel memetic algorithms (MA) that belong to the swarm intelligence algorithm. The SIACR protocol classifies WSN nodes into three categories: cluster heads (CHs), cluster members, and free nodes, and employs the MPDE and VSSLS to optimize clustering, with the objectives of reducing energy consumption and balancing the residual energy among nodes. In the comparison experiments, the algorithm presented in this study was compared to the Low-Energy Adaptive Clustering Hierarchy (LEACH), LEACH-centralized (LEACH-C), and particle swarm optimization (PSO)-SIACR algorithms for two network topologies with three data fusion rates of 0, 0.5, and 1, respectively. The experimental results revealed that MPDE and VSSLS-SIACR outperformed the other algorithms in terms of network lifetime and residual energy. Meanwhile, it is demonstrated that the proposed algorithm is highly adaptable to different topologies and different data fusion rates. The PSO-SIACR algorithm performed similarly to the MPDE and VSSLS-SIACR algorithms, indicating that the SIACR is, furthermore, an effective framework for clustering routing optimization using swarm intelligence algorithms.