A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a BioInspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates 'Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.