Algorithms for Transmission Power Control in Biomedical Wireless Sensor Networks

Abstract—Wireless sensor networks are increasingly being used for continuous monitoring of patients with chronic health conditions such as diabetes and heart problems. As biomedical sensor nodes become more wearable, their battery sizes dimin-ish, necessitating very careful energy management. This paper proposes feedback-based closed-loop algorithms for dynamically adjusting radio transmit power in body-worn devices, and evalu-ates their performance in terms of energy savings and reliability as the data periodicity and feedback time-scales vary. Using experimental trace data from body worn devices, we first show that the performance of dynamic power control is adversely affected at long data periods. Next for a given data period we show that modifying the transmit power at too long time-scales (around a minute) reduces the efficacy of dynamic power control, while too short a time-scale (few seconds or less) incurs a high feedback signaling overhead. We therefore advocate an intermediate range of time-scales (when permitted by the data periodicity), typically in the few tens of seconds, at which the control algorithms should adapt transmit power in order to achieve maximal energy savings in body-worn sensor devices used for medical monitoring. I

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted

Towards the fast and robust optimal design of Wireless Body Area Networks

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.Comment: Authors' manuscript version of the paper that was published in Applied Soft Computin

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

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application