A Novel Energy Efficient hybrid Meta‐heuristic Approach (NEEMA) for wireless body area network
Smita Sharma, Vigyanshu Mishra, M. M. Tripathi
Abstract
Summary The technological progression in the area of Wireless Body Area Network (WBAN) has made it possible to design various sensing modules operating for detecting the multitudinous physical attributes of a patient's body. However, the limited battery of these sensing devices has restricted the scope for WBAN. Hence, it is imperative to design an energy efficient algorithm to combat the gigantic energy consumption of these sensor nodes. Therefore, in this paper, we propose a Novel Energy Efficient hybrid Meta‐Heuristic Approach (NEEMA) for WBAN. We adopt this hybrid approach by using Tunicate Swarm Algorithm (TSA) and Genetic Algorithm (GA); we name it as T‐GA, to deliver the high convergence and large exploitation and exploration capabilities. We follow the clustering approach by selecting the Cluster Head (CH) with the help of the fitness function of T‐GA. We consider the novel parameters for selecting the CH that helps in the overall energy preservation of sensor nodes. Our proposed work focuses on multi‐hop communication among the patients on whose body, the body sensor nodes are installed, so that the data through the multi‐hop reach to the healthcare. The Relay Head (RH) node is used for forwarding the data to next RH and hence to sink. RH selection follows the same method of selection as that of CH. The experimental outcomes of NEEMA outperform the state‐of‐the‐art algorithms and prove to be highly beneficial for various WBAN applications.