TY - JOUR
T1 - Ultra-Low Power, Secure IoT Platform for Predicting Cardiovascular Diseases
AU - Yasin, Muhammad
AU - Tekeste, Temesghen
AU - Saleh, Hani
AU - Mohammad, Baker
AU - Sinanoglu, Ozgur
AU - Ismail, Mohammed
N1 - Funding Information:
This work was supported by the Mubadala-SRC Center of Excellence for Energy Efficient Electronic Systems Research Contract 2013-HJ2440. This paper was recommended by Associate Editor E. Sanchez-Sinencio.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2017/9
Y1 - 2017/9
N2 - Internet of Things (IoT) promises to revolutionize the health-care sector through remote, continuous, and non-invasive monitoring of patients. However, there are two main challenges faced by the IoT-enabled medical devices: energy-efficiency and security/privacy concerns. Researchers have independently attempted to develop solutions, such as low-power ECG-processors and security protocols, that address these challenges on an individual basis. However, it is imperative to investigate holistic solutions that integrate in a synergistic manner, delivering an overall secure and energy-efficient product. In this paper, we develop an ultra-low power and secure IoT sensing/pre-processing platform for prediction of ventricular arrhythmia using ECG signals. Our proposed solution is able to predict the on-set of the critical cardiovascular events upto 3 h in advance with 86% accuracy. Moreover, the proposed architecture is designed using an Application Specific Integrated Circuits design flow in 65-nm Low Power Enhanced technology; the power it consumes is 62.2% less than that of the state-of-the-art approaches, while occupying 16.0% smaller area. The proposed processor makes use of ECG signals to extract a chip-specific ECG key that enables protection of communication channel. By integrating the ECG key with an existing design-for-trust solution, the proposed platform offers protection also at the hardware level, thwarting hardware security threats, such as reverse engineering and counterfeiting. Through efficient sharing of on-chip resources, the overhead of the multi-layered security infrastructure is kept at 9.5% for area and 0.7% for power with no impact on the speed of the design.
AB - Internet of Things (IoT) promises to revolutionize the health-care sector through remote, continuous, and non-invasive monitoring of patients. However, there are two main challenges faced by the IoT-enabled medical devices: energy-efficiency and security/privacy concerns. Researchers have independently attempted to develop solutions, such as low-power ECG-processors and security protocols, that address these challenges on an individual basis. However, it is imperative to investigate holistic solutions that integrate in a synergistic manner, delivering an overall secure and energy-efficient product. In this paper, we develop an ultra-low power and secure IoT sensing/pre-processing platform for prediction of ventricular arrhythmia using ECG signals. Our proposed solution is able to predict the on-set of the critical cardiovascular events upto 3 h in advance with 86% accuracy. Moreover, the proposed architecture is designed using an Application Specific Integrated Circuits design flow in 65-nm Low Power Enhanced technology; the power it consumes is 62.2% less than that of the state-of-the-art approaches, while occupying 16.0% smaller area. The proposed processor makes use of ECG signals to extract a chip-specific ECG key that enables protection of communication channel. By integrating the ECG key with an existing design-for-trust solution, the proposed platform offers protection also at the hardware level, thwarting hardware security threats, such as reverse engineering and counterfeiting. Through efficient sharing of on-chip resources, the overhead of the multi-layered security infrastructure is kept at 9.5% for area and 0.7% for power with no impact on the speed of the design.
KW - biomedical classifier
KW - design-for-trust
KW - ECG
KW - hardware security
KW - Internet of Things
KW - ventricular arrythmia
UR - http://www.scopus.com/inward/record.url?scp=85018889819&partnerID=8YFLogxK
U2 - 10.1109/TCSI.2017.2694968
DO - 10.1109/TCSI.2017.2694968
M3 - Article
AN - SCOPUS:85018889819
SN - 1057-7122
VL - 64
SP - 2624
EP - 2637
JO - IEEE Transactions on Circuits and Systems I: Regular Papers
JF - IEEE Transactions on Circuits and Systems I: Regular Papers
IS - 9
M1 - 7927419
ER -