Optimized Cluster Routing Protocol With Energy-Sustainable Mechanisms for Wireless Sensor Networks

Abstract

Clustering algorithms have a key role in decreasing energy consumption and increasing network longevity in wireless sensor networks. This work advances on previous homogeneous and heterogeneous algorithms, including low-energy adaptive clustering hierarchical routing protocol (LEACH), distributed residual energy LEACH (DIS-RES-EL), residual energy LEACH (RES-EL), energy efficient LEACH (EEL), and stable election protocol (SEP), by introducing novel clustering methodologies. It introduces novel improved residual energy LEACH (IMP-RES-EL) and energy efficient stable election protocol (EE-SEP) to improve the efficiency of clustering algorithms in energy savings for homogeneous and heterogeneous wireless sensor networks. The simulation result shows that, in addition to prolonging network lifetime and optimal routing, these methods transported more data packets from the cluster to sensor nodes and then to base stations than other techniques. When compared to the stable election protocol (SEP), the proposed energy-efficient stable election protocol (EE-SEP) influences the number of bunch heads formed over their lifetime, the organization’s stability, the number of nodes shipped off the base station from each cluster head, and the organization’s overall lifetime. When comparing the two current algorithms, EE-SEP and LEACH, for various topologies, the findings demonstrate that EE-SEP is the most energy efficient directing convention for extending the previously described qualities. This attribute has not been discussed thus far. The results also show that the IMP-RES-EL algorithm successfully increases network lifespan while minimizing energy dissipation and transmissions between sensor nodes and base stations or cluster heads (CHs). For all of the suggested homogeneous and heterogeneous algorithms, network lifetime in rounds rose by 36%, aggregated data packets from CHs to BS increased by 44%, and total data packets to BSs improved by 20%.

Keywords

• Base station
• clustering algorithms
• cluster heads
• heterogeneous network
• homogeneous network
• routing protocol