Energy Efficient Cross-Layer Design for Wireless Body Area Monitoring Networks in Healthcare Applications

Abstract

Growing number of patients with chronic diseases requiring constant monitoring has created a major impetus to developing scalable Body Area Sensor Networks (BASNs) for remote health applications. In this paper, to anatomize, control, and optimize the behavior of the wireless EEG monitoring system under the energy constraint, we develop an Energy-Rate-Distortion (E-R-D) analysis framework. This framework extends the traditional distortion analysis by including the energy consumption dimension. Using the E-R-D model, an Energy-Delay-Distortion cross-layer design that aims at minimizing the total energy consumption subject to data delay deadline and distortion threshold constraints is proposed. The source encoding and data transmission are the two dominant power-consuming operations in wireless EEG monitoring system. Therefore, in the proposed cross-layer design, the optimal encoding and transmission energy are computed to minimize the energy consumption in a delay constrained wireless BASN. This cross-layer framework is proposed, across Application-MAC-Physical layers, under a constraint that all successfully received packets must have their delay smaller than their corresponding delay deadline and with maximum distortion less than the application distortion threshold. In addition to that, for efficient use of the bandwidth, a variable bandwidth allocation scheme that assigns the time-frequency slots to the sensor nodes is proposed, which results in significant energy savings over the conventional constant bandwidth allocation scheme, as shown in the simulation results.