TY - GEN
T1 - Real-time implementation and evaluation of an adaptive energy-aware data compression for wireless EEG monitoring systems
AU - Awad, Alaa
AU - Hamdy, Medhat
AU - Mohamed, Amra
AU - Alnuweiri, Hussein
N1 - Publisher Copyright:
© 2014 ICST.
PY - 2014/10/17
Y1 - 2014/10/17
N2 - Wireless sensor technologies can provide the leverage 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.
AB - Wireless sensor technologies can provide the leverage 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.
KW - Convex optimization
KW - Cross-layer design
KW - EEG signals
KW - Wireless healthcare applications
UR - http://www.scopus.com/inward/record.url?scp=84912089184&partnerID=8YFLogxK
U2 - 10.1109/QSHINE.2014.6928668
DO - 10.1109/QSHINE.2014.6928668
M3 - Conference contribution
AN - SCOPUS:84912089184
T3 - Proceedings of the 2014 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2014
SP - 108
EP - 114
BT - Proceedings of the 2014 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2014
Y2 - 18 August 2014 through 20 August 2014
ER -