Energy-efficient routing optimization algorithm in WBANs for patient monitoring

With the recent technological innovations for measuring the physiological characteristics in the human body, Wireless Body Area Networks (WBANs) have received much attention from the industry and academics. One of the feasible solutions provided by today's WBAN is the continuous health monitoring in which sensors planted in various parts of the body, which measure and send information about physiological health status to a sink. The energy constraint WBAN has to perform these measurements with minimum energy consumptions of the nodes, maintaining the durable health monitoring process. This paper uses the meta-heuristic Genetic Algorithm (GA) to select the best routing path by calculating distances between the nodes under multiple scenarios, in contrast to the available direct distance optimization method. This study considers the use of energy by sensor nodes, number of rounds, number of sensors, the position of the deployed sensors and distance between the sensors. The comprehensive results show that direct distance optimization method drops more packets, i.e. 12,000 as compared to 8000 packets by the genetic algorithm when 8000 rounds were executed. The proposed optimization also outperforms the previous approach in terms of the number of dead nodes, which results in saving the energy to increase the lifetime of the WBAN significantly.