Interference-aware energy-efficient cross-layer design for healthcare monitoring applications

Awad, A., A. Mohamed, A. A. El-Sherif, and O. A. Nasr, "Interference-aware energy-efficient cross-layer design for healthcare monitoring applications", Computer Networks, vol. 74, Part A, pp. 64 - 77, 2014.


Body Area Sensor Networks (BASNs) leverage wireless communication technologies to provide healthcare stakeholders with innovative tools and solutions that can revolutionize healthcare provisioning; BASNs thus promotes new ways to acquire, process, transport, and secure the raw and processed medical data to provide the scalability needed to cope with the increasing number of elderly and chronic disease patients requiring constant monitoring. However, the design and operation of BASNs is challenging, mainly due to the limited power source and small form factor of the sensor nodes. The main goal of this paper is to minimize the total energy consumption to prolong the lifetime of the wireless BASNs for healthcare applications. An Energy–Delay–Distortion cross-layer framework is proposed in order to ensure transmission quality for medical signals under limited power and computational resources. The proposed cross-layer framework spans the Application–MAC–Physical layers. The optimal encoding and transmission energy are computed to minimize the total energy consumption in a delay constrained wireless BASN. The proposed framework considers three scheduling techniques: TDMA, TDMA–Simultaneous Transmission and dynamic frequency allocation scheduling. The TDMA–ST scheme schedules the weakly interfering links to transmit simultaneously, and schedules the strongly interfering links to transmit at different time slots. The dynamic frequency allocation scheme allocates the time–frequency slots optimally based on the application’s requirements. Simulation results show that these proposed scheduling techniques offer significant energy savings, compared to the algorithms that ignore cross-layer optimization.



Related External Link

computernets_health_2014.pdf1.86 MB