Prediction-Based Data Transmission for Energy Conservation in Wireless Body Sensors

Feng Xia, Zhenzhen Xu, Lin Yao, Weifeng Sun, Mingchu Li School of Software Dalian University of Technology Dalian 116620, China f.xia@ieee.org

Abstract — Wireless body sensors are becoming popular in healthcare applications. Since they are either worn or implanted into human body, these sensors must be very small in size and light in weight. The energy consequently becomes an extremely scarce resource, and energy conservation turns into a first class design issue for body sensor networks (BSNs). This paper deals with this issue by taking into account the unique characteristics of BSNs in contrast to conventional wireless sensor networks (WSNs) for e.g. environment monitoring. A prediction-based data transmission approach suitable for BSNs is presented, which combines a dual prediction framework and a low-complexity prediction algorithm that takes advantage of PID (proportional- integral-derivative) control. Both the framework and the algorithm are generic, making the proposed approach widely applicable. The effectiveness of the approach is verified through simulations using real-world health monitoring datasets.
Keywords - dual prediction; body sensor network; energy conservation; PID; data transmission I. INTRODUCTION Over years the field of wireless sensor networks (WSNs) has achieved huge advancements in various aspects including fundamental theory, key technologies, and real-world applications. With an increasing number of wireless sensors becoming commercially available, WSNs have been enabling a broad range of novel applications, among which human health monitoring is a typical example. Continuous health monitoring is a key technology for realizing the transition of current health care systems to more proactive and affordable healthcare, especially for the elderly. This service has tremendous potential to help address the world-scale challenge of population aging. A WSN used in the context of health care is usually named under body sensor network (BSN) [1,2], a relatively new term with a history of only a few years.
From a historical perspective, however, body sensors are an established technology that has emerged more than one hundred years ago [3]. One notable milestone is the Allbutt’s invention of the clinical thermometer used for taking temperature of a person in 1867. During the past century, body sensors of various types and with disparate functionalities have been put into use, becoming smaller and smaller in size. Thanks to recent technological developments in electronics and wireless communications, especially the personal area networking standards such as IEEE 802.15.1 (Bluetooth), 802.15.4 (Zigbee), and 802.15.6 (the work-in-progress body area network protocol), many physiological sensors have transformed into wireless sensors in possession of sensing, computing, and (wireless) communication capabilities. These on-body or in-body wireless sensors, capable of communicating with other devices, make it possible to realize continuous monitoring of patients in hospital and long-term health care for the aged and/or the disabled in their homes.
A BSN consists of a number of wireless body sensor nodes, which measure diverse physiological phenomena of a human body, such as blood oxygen, electrocardiography (ECG), electroencephalography (EEG), electromyogram (EMG), central venous pressure (CVP), respiratory impedance, pulmonary arterial pressure (PAP), and temperature. Generally, there is an additional base station (also called sink) possibly functioned by a mobile phone or a PDA (personal digital assistant), etc. Healthcare systems built upon BSNs can potentially transform how people’s health is monitored, how chronic illnesses are treated, and how the damages of acute events are minimized. For instance, health data collection is traditionally conducted intermittently, e.g., once every several days via a doctor visit. As a consequence, the time points of health monitoring are very limited. On one hand, abnormal, even life-threatening events might be unobserved. On the other, the value of the data collected as for aid of diagnosis is restricted due to undersampling [2]. In contrast, BSNs allow to closely monitor, in a continuous manner, the physiological states of an individual, and to provide real-time warnings of abnormalities, both helping address the drawbacks of the traditional health data collection approach. To realize the great potential of BSNs in practice, however, a number of obstacles associated with size, lifetime, compatibility, privacy and security must be tackled. In particular, BSNs have to operate on extremely limited energy budget. In order for the users to feel comfortable and acceptable to wear or even implant body sensors, they should be made as small as possible. This imposes critical con

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