On-body and Off-body Transmit Power Control in IEEE 802.15.6 Scheduled Access Networks

Wireless Body Area Networks (WBANs) have received much attention due to the possibility to be used in healthcare applications. For these applications, energy saving is a critical issue, as in many cases, batteries cannot be easily replaced. A transmit power control scheme, able to adapt tothe variations of the wireless body channel, will allow consistent energy saving and longer battery life. In this paper we propose a transmit power control scheme suitable for IEEE 802.15.6 narrowband scheduled access networks,in which the transmission power is modulated frame by frame according to a run-time estimation of the channel propagation conditions. A simple and effective line search algorithm is proposed to estimate the channel quality based on the signal power received from the hub; in addition, an adaptive fade margin estimator is presented to determine an optimum margin based on the channel conditions. The approach allows tracking the highly variable propagation conditions due to the body mobility and the deployment of the sensors close to the human body. An experimental study in different test cases proves the effectiveness of the scheme in comparison with alternative solutions in the literature

Achieving Longevity in Wireless Body Area Network by Efficient Transmission Power Control for IoMT Applications

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT).  IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithm to extend the network lifetime in IoMT networks for healthcare applications by eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%

Channel estimation and transmit power control in wireless body area networks

Wireless body area networks have recently received much attention because of their application to assisted living and remote patient monitoring. For these applications, energy minimisation is a critical issue since, in many cases, batteries cannot be easily replaced or recharged. Reducing energy expenditure by avoiding unnecessary high transmission power and minimising frame retransmissions is therefore crucial. In this study, a transmit power control scheme suitable for IEEE 802.15.6 networks operating in beacon mode with superframe boundaries is proposed. The transmission power is modulated, frame-by-frame, according to a run-time estimation of the channel conditions. Power measurements using the beacon frames are made periodically, providing reverse channel gain and an opportunistic fade margin, set on the basis of prior power fluctuations, is added. This approach allows tracking of the highly variable on-body to on-body propagation channel without the need to transmit additional probe frames. An experimental study based on test cases demonstrates the effectiveness of the scheme and compares its performance with alternative solutions presented in the literature

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs