Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks

Wireless sensor networks are applied in different domains such as agriculture, military, healthcare, and defense. The most critical issue in wireless sensor networks is achieving greater energy efficiency fairness measurement. This research proposes new evolutionary modeling using genetic algorithms with an energy-efficient clustering routing strategy to resolve this critical issue in healthcare networks of hospitals. This article develops a new model of homogeneous and heterogeneous network structures with the novelty of combining the consumption model of original energy to construct new evolutionary modeling to predict the optimal clusters count for minimizing the consumption of the total energy in wireless sensor healthcare applications. The simulation results show that the lifetime is maximum when the routing factor, α = 0.1 compared to other values of α. For the homogeneous healthcare networks instances, the death round value of the first node is for a smaller value of α . The better routing factor is obtained as α ranging from 0.2 to 0.5 for homogeneous and heterogeneous healthcare networks instances. For the range of energy levels that ranges from 0.4 to 0.8, the proposed evolutionary model achieves better measures for the rounds from 1800 up to 2400 compared to the recent methods. The proposed model evaluates optimal clusters based on the structure of the healthcare network, and then the clusters head is identified based on the clustering locations. The proposed method achieves a higher throughput percentage in all cases of homogeneous and heterogeneous instances where the energy lies between (0.4, 0.8). Simulation outcomes of the proposed evolutionary model result in the performance metrics mainly significant reduction of energy consumption decay rate of the network while maximizing the healthcare network’s lifetime by improving the network’s throughput compared to the state-of-the-art methods.