A Cognitive Energy-Driven Routing Strategy for Ultra-Efficient Data Transfer in Wireless Sensor Networks
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WSNs deploy multi-hop routes to transfer information from distributed nodes to central points because of their established use for environmental inspection and data acquisition. The effective transmission of data plays a critical role in Wireless Sensor Networks particularly in challenging conditions that produce temporary network interruptions leading to data loss. The present body of work faces energy utilization constraints of Pegasus at 75% efficiency alongside scalability limitations at 300 nodes in A-Leach and packet delivery performance at 94% PDR in DSO-EHO. The current work presents a new optimization method called Levy Flight fine-tuned Red Deer Optimization (LFRDO) for route path optimization. Red Deer Optimization and Levy Flight produce an algorithm that optimizes energy expenditure by promoting active exploration techniques to simultaneously minimize network delays and lengthen operational life. The proposed method achieves a 98% reduction in energy usage together with enhanced packet delivery ratio (96% for 500 nodes) at a throughput rate of 0.9 Mbps. The LFRDO simulation shows a 95% energy efficiency level surpassing DSO-EHO at 92% while operating effectively with networks having up to 500 nodes. The system prolongs network operational time by 35% when combined with intelligent routing decisions that minimize end-to-end delay. The proposed method provides solutions to resolve three primary WSN issues concerning scalability together with energy efficiency alongside dependable data transmission during system changes
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