Adaptive and scalable energy aware clustering for mobile wireless sensor networks using a density driven approach

Wireless Sensor Networks (WSNs) face persistent challenges in sustaining energy efficiency and operational lifetime, particularly in dynamic environments with node mobility and heterogeneous energy profiles. Traditional hierarchical clustering protocols, such as Distributed Load-balancing Hybrid Energy-Efficient Distributed Clustering (DL-HEED), Mobility-Efficient Data Fusion (MEDF), and Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR), often fall short in adapting to such conditions, leading to suboptimal performance in data transmission and load balancing. To address these limitations, this paper introduces Mobility & Energy Adaptive Density-based Clustering (MEADC), a novel strategy that enhances scalability and adaptability by segmenting the network into concentric regions and employing a hybrid metric based on residual energy, geometric centrality, and local node density to determine optimal cluster formation. The approach further incorporates a rumor-based load dissemination mechanism and an energy-centrality score to achieve robust and energy-efficient cluster head (CH) selection. Extensive simulations demonstrate that MEADC significantly outperforms baseline protocols by reducing end-to-end data transmission delays by up to 150 s, extending network lifetime by 28%-51%, and improving packet delivery efficiency by 28%. These results highlight the potential of MEADC as a reliable and scalable clustering framework for energy-critical, mobile, and real-time WSN deployments across diverse application domains.