Wireless body area network is a type of wireless sensor network that enables efficient healthcare system. To minimize frequent sensor replacement due to resource restrictions, it is necessary to improve energy efficiency in wireless body area network. This article deals with energy efficiency and quality-of-service improvement together in novel wireless body area network architecture. A novel wireless body area network architecture is designed with dual sink nodes in order to minimize delay and energy consumption. A novel insistence-aware medium access control protocol which is aware of criticality of sensed data is presented in the proposed wireless body area network. Prior knowledge-based weighted routing algorithm is responsible to select optimal route for data transmission. In prior knowledge-based weighted routing, weight value is computed by considering significant metrics such as residual energy, link stability, distance, and delay in order to improve energy efficiency and quality of service in the network. Energy consumption is further minimized by incorporating graph-based sleep scheduling algorithm. In graph-based sleep scheduling, criticality of sensor node is also considered as major metric. In coordinator, split and map-based neural network classifier is involved to perform packet classification. After classification, packets are assigned to corresponding sink node according to packet type. Then, throughput and delay metrics are improved by frame aggregation process which is involved in sink node. Extensive simulation in OMNeT + + shows better performance in network lifetime, throughput, residual energy, dropped packets, and delay.

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In this paper, we proposed a scheme to extend the lifetime of a wireless sensor network, in which each sensor node decides whether to transmit a message or not and with what range to transmit the message, based on its own energy reserve level and the information contained in each message. The information content in each message is determined through a system of rules describing prospective events in the sensed environment, and how important such events are. The messages deemed to be important are propagated by all sensor nodes and with different transmission ranges depending on nodes energy resource level, while messages deemed to be less important are handled by only the nodes with high energy reserves level and transmitted with different transmission ranges based on nodes energy resource level. The results show that by adapting the transmission range based on nodes energy reserve and message importance, a considerable increase in network lifetime and connectivity can be achieved.