CAE Adaptive Compression, Transmission Energy and Cost Optimization for m-Health Systems

Abeer Z. Al-Marridi, Amr Mohamed, A. Erbad, M. Guizani
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Abstract

The rapid increase in the number of patients requiring constant monitoring inspires researchers to investigate the area of mobile health (m-Health) systems for intelligent and sustainable remote healthcare applications. Extensive real-time medical data transmission using battery-constrained devices is challenging due to the dynamic network and the medical system constraints. Such requirements include end-to-end delay, bandwidth, transmission energy consumption, and application-level Quality of Services (QoS) requirements. As a result, adaptive data compression based on network and application resources before data transmission would be beneficial. A minimal distortion can be assured by applying Convolutional Auto-encoder (CAE) compression approach. This paper proposes a cross-layer framework that considers the patients’ movement while compressing and transmitting EEG data over heterogeneous wireless environments. The main objective of the framework is to minimize the trade-off between the transmission energy consumption along with the distortion ratio and monetary costs. Simulation results show that an optimal trade-off between the optimization objectives is achieved considering networks and application QoS requirements for m-Health systems.
移动医疗系统的CAE自适应压缩、传输能量和成本优化
需要持续监测的患者数量的迅速增加激发了研究人员对移动医疗(m-Health)系统领域的研究,以实现智能和可持续的远程医疗应用。由于动态网络和医疗系统的限制,使用电池受限设备进行广泛的实时医疗数据传输具有挑战性。这些需求包括端到端延迟、带宽、传输能耗和应用级服务质量(QoS)需求。因此,在数据传输前根据网络和应用资源进行自适应数据压缩是有益的。采用卷积自编码器(CAE)压缩方法可以保证最小的失真。本文提出了一种考虑患者运动的跨层框架,同时在异构无线环境下压缩和传输脑电图数据。该框架的主要目标是最小化传输能耗、失真率和货币成本之间的权衡。仿真结果表明,考虑到移动健康系统的网络和应用QoS需求,实现了优化目标之间的最优权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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