Research Article
Real-time Implementation and Evaluation of an Adaptive Energy-aware Data Compression for Wireless EEG Monitoring Systems
@INPROCEEDINGS{10.4108/icst.qshine.2014.256334, author={Alaa Awad and Medhat Hamdy and Amr Mohamed and Hussein Alnuweiri}, title={Real-time Implementation and Evaluation of an Adaptive Energy-aware Data Compression for Wireless EEG Monitoring Systems}, proceedings={10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={IEEE}, proceedings_a={QSHINE}, year={2014}, month={9}, keywords={wireless healthcare applications eeg signals cross-layer design convex optimization}, doi={10.4108/icst.qshine.2014.256334} }
- Alaa Awad
Medhat Hamdy
Amr Mohamed
Hussein Alnuweiri
Year: 2014
Real-time Implementation and Evaluation of an Adaptive Energy-aware Data Compression for Wireless EEG Monitoring Systems
QSHINE
IEEE
DOI: 10.4108/icst.qshine.2014.256334
Abstract
Wireless sensor technologies can provide the lever- age needed to enhance patient-caregivers collaboration through ubiquitous access and direct communication, which promotes smart and scalable vital sign monitoring of the chronically ill and elderly people live an independent life. However, the design and operation of BASNs are challenging, because of the limited power and small form factor of biomedical sensors. In this paper, an adaptive compression technique that aims at achieving low-complexity energy-efficient compression subject to time delay and distortion constraints is proposed. In particular, we analyze the processing energy consumption, then an energy consumption optimization model with constraints of distortion and time delay is proposed. Using this model, the Personal Data Aggregator (PDA) dynamically chooses the optimal compression parameters according to real-time measurements of the packet delivery ratio (PDR) or individual users. To evaluate and verify our optimization model, we develop an experimental testbed, where the EEG data is sent to the PDA that compresses the gathered data and forwards it to the server which decompresses and reconstructs the original signal. Experimental testbed and simulation results show that our adaptive compression technique can offer significant savings in the delivery time with low complexity and without affecting application accuracies.