Measurement Sum Master (MSM-Clustering) Approach: Increasing Lifespan and Scalability for Reliable Data Delivery of WSNs

In wireless sensor networks (WSNs), energy consumption is a critical concern, and replacing batteries at the node level is often costly and impractical. Network lifespan extension not only improves operational capacity but also minimizes maintenance overhead. As the network size increases, overall network management and scalability become challenging, especially when handling a large amount of sensor data. To address these issues, we proposed an approach known as Measurement Sum Master (MSM) clustering, which leverages particle swarm optimization to detect healthier inter-cluster routing directions. This approach introduces a master node strategy to select the average cluster size for maintaining scalability according to the dimensional factor. The proposed approach is designed to analyze such issues in clustering based on the concept of particle swarm methodology for identifying the best-suited inter-cluster routing, that is, master-based sum-measured optimization offers improvements over existing methods by maintaining the fitness of the cluster head in terms of energy and packet delivery. It is formulated with an algorithm to identify the best cluster size to handle the dimension. It is finally compared with the existing protocol in terms of energy consumption to analyze the performance of the protocols. The suggested approach introduces a new methodology of clustering for WSNs to improve efficiency and enhance operational longevity. The MSM-PSO protocol was used for the analysis, and the results were compared in terms of lifespan and scalability. The power efficiency increased by approximately 8–9% and the packet transmission cost was reduced by approximately 5% along with the improved fitness measurement. A comparative analysis with existing protocols was also presented to demonstrate that the MSM clustering approach significantly minimizes energy consumption, improves data delivery reliability, and increases network lifetime. This novel clustering approach offers an energy-efficient and scalable solution for enhancing the reliability of WSNs.