Abstract
Wireless body area network (WBAN) is the emerging field in domain of healthcare to monitor vital signs of patients with the support of bio-medical sensors. The design of delay-aware and energy efficient routing protocol based on the traffic prioritization is the key research theme in WBAN. In addition, WBAN has challenging issues of packet loss, temperature rise, delay with retransmission of the lost packets due to which it does not extend the network life time and is not acceptable for life critical data. In this context, this paper proposes traffic priority based delay-aware and energy efficient path allocation routing protocol for wireless body area network (Tripe-EEC), which selects the optimal paths with high residual energy of nodes with minimum temperature rise. Specially, the design of Tripe-EEC routing protocol is mostly divided into four Folds. Firstly, the patient’s data is classified into four classes that included normal data, data on-demand, Emergency data of low threshold readings and high threshold readings. These classifications assist in allocation of paths on the priority basis by removing conflicts along with support of an improved equation. Secondly, energy efficient and delay-aware path allocation algorithm is developed for normal data focusing on the selection of optimal and shortest paths with minimum temperature rise (hotspot). Thirdly, data on-demand algorithm is developed for on-demand traffic to transmit immediately to the medical doctor which is usually asked if any criticality or emergency situation happens with patient. Forth, criticalities (abnormal readings of vital signs i.e. low and high threshold values) detection algorithms are developed for measuring criticalities of vital signs and allocation of adaptive and energy efficient paths on the priority basis by removing conflict between them. Extensive simulations are performed in realistic medical environments for comparing performance of the proposed Tripe-EEC protocol with the state-of-the-art protocols.
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More From: Journal of Ambient Intelligence and Humanized Computing
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