The rapid increase in the 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- Delay-Distortion cross-layer design. This cross-layer design aims at minimizing the total energy consumption subject to data delay deadline and distortion threshold constraints. 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, to decrease the computational complexity, a distributed algorithm that finds the optimum encoding and transmission parameters for each sensor node is proposed.


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