Tuna-Osprey Optimization for Energy Efficient Cluster-based Routing: Modified Deep Learning for Node's Energy Prediction
Keywords:
Network lifetime maximization, Tuna Osprey Optimization (TOO), Modified DL, M-LSTM, Link lifetime, optimal CHSAbstract
The main consideration of WSN design is maximization of the network lifetime. It is proven that the effective balancing of network energy consumption along with the maximization of network lifetime can be performed by clustering and routing approaches. Accordingly, a new Tuna Osprey Optimization algorithm for energy-efficient cluster-based routing has been developed in this work. This approach includes 2 working phases: clustering and routing process. Initially, a modified DL model named M-LSTM is proposed for predicting the node’s energy. Subsequently clustering process is carried out by the TOO algorithm, which considers the energy, link lifetime, distance, trust, and delay as constraints for the selection of optimal CH. Finally, with the same TOO algorithm, the routing process is conducted, which considers the link quality as the constraint to provide optimal routing. Results proved that the proposed TOO for energy-efficient cluster-based routing can reduce energy utilization while attaining maximum network lifetime.
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