LEACH Algorithm Enhancements: Energy-Aware Cluster Head Election and Communication Optimization

The document addresses two key improvements to the LEACH algorithm: 1. Solving the network energy imbalance caused by LEACH's cluster formation election condition temp_rand(i)<=T(n) not considering node energy factors. The enhancement proposes selecting nodes with relatively higher energy as cluster heads. The implementation requires calculating an appropriate threshold T value using the defined formula T(n) = (p/(1-p*(mod(r,round(1/p))))*(E_current/Eo), where E_current = Eo-Etx-Erx represents current node energy after transmission (Etx) and reception (Erx) deductions from initial energy (Eo). During the first election round, all nodes have identical initial energy, making the energy factor equally influential in cluster head competition. In subsequent rounds, node energy consumption becomes directly dependent on distance to cluster heads - nodes closer to cluster heads consume less energy and thus have higher probability of being elected as cluster heads in future rounds. The algorithm implementation needs to dynamically track energy levels through variables like E_current and adjust T(n) calculations accordingly. 2. Addressing the issue of excessive energy consumption and premature node "death" caused by LEACH's single-hop routing from cluster heads to base stations. The solution involves optimizing cluster size distribution: creating larger clusters for nodes nearer to the base station and smaller clusters for nodes farther away. In standard LEACH, nodes join clusters by selecting the cluster head with minimum distance dn_c through polling mechanisms. The clustering optimization in single-hop networks primarily involves balancing intra-cluster communication energy consumption and data reporting energy consumption. To reduce intra-cluster energy consumption, the algorithm should create more compact cluster structures by minimizing the distance dn_c between cluster members and their cluster head. For reducing data reporting energy consumption, the implementation should incorporate distance-based clustering logic that assigns larger cluster sizes to nodes closer to the base station while maintaining smaller clusters for distant nodes. This can be achieved by modifying the cluster formation algorithm to include base station distance as a weighting factor in cluster head selection and member assignment processes.