An Energy-Conscious and Delay-Aware Path Allocation for Emergency Node in WBAN

Wireless Body Area Network (WBAN) offer a viable solution for continuous health monitoring and are receiving a lot of attention from academicians and researchers due to recent technological advancements. In WBAN, sensors are placed throughout the body to collect physiological data and transmit it to a sink node. Thus, transmitting medical information between sensor nodes and sink nodes while consuming minimal energy, causing little packet loss, and extending the lifespan of the network is challenging. A linear regression buffalo optimization algorithm (LRBOA) for WBAN is introduced in this paper to enable the timely transmission of vital patient data. The linear regression method is used to identify normal, aberrant, and vital patient data in LRBOA. After the classification, the protocol schedules the priority for classified data. Within the specified timeframe, high-priority patient data can be transmitted first. Energy, link dependability, bandwidth, and distance-based optimal pathways are needed for data transfer. The suggested algorithm could determine the best path based on several variables, including energy, link dependability, bandwidth, and distance. The highest-priority node data is finally sent to the sink node for further processing. In this way, priority-based, energy-aware data transmission is performed in WBAN. The proposed algorithm is tested using a MATLAB with a health monitoring dataset in WBAN.