Energy-efficient routing optimization algorithm in WBANs for patient monitoring
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ORIGINAL RESEARCH
Energy-efficient routing optimization algorithm in WBANs for patient monitoring Muhammad Aamir Panhwar1,2 · Deng Zhong Liang1 · Kamran Ali Memon1 · Sijjad Ali Khuhro3 · Muhammad Aashed Khan Abbasi4 · Noor‑ul‑Ain5 · Zulfiqar Ali6 Received: 9 July 2020 / Accepted: 5 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords WBAN · Energy · Health monitoring · Genetic algorithm · Dead nodes
1 Introduction Wireless Body Area Networks (WBANs) consist of sensor nodes single or multi, sink/Base Station (BS) and database, as illustrated in Fig. 1 (Talha and Kohno 2018). Physically, the sensor nodes are deployed on the human body, and it is
* Muhammad Aamir Panhwar [email protected] 1
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
2
Mehran University of Engineering Technology, Jamshoro, Pakistan
3
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
4
Prince Sultan University, Riyadh, Saudi Arabia
5
School of Information Communication Engineering, Beijing University of Posts and Telecommunication, Beijing, China
6
Dawood University of Engineering and Technology, Karachi, Pakistan
an environment (Bhatia and Kumar 2018), or surface so that the physiological data may be gathered and send the data to BS via a radio connection towards the database administrator (Chandra et al. 2018). These body planted sensors are temperature made of graphite-filled polyethylene oxide, thermoelectric (Eke et al. 2017), ink-based (Esmaeili and Minaei 2018), inertial (Fortino et al. 2019), antibody-coated electro-active polymer
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