A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms

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Christos P. Antonopoulos ; Nikolaos S. Voros (2017)

For highly demanding scenarios such as continuous bio-signal monitoring, transmitting excessive volumes of data wirelessly comprises one of the most critical challenges. This is due to the resource limitations posed by typical hardware and communication technologies. Driven by such shortcomings, this paper aims at addressing the respective deficiencies. The main axes of this work include (a) data compression, and (b) the presentation of a complete, efficient and practical hardware accelerator design able to be integrated in any Internet of Things (IoT) platform for addressing critical challenges of data compression. On one hand, the developed algorithm is presented and evaluated on software, exhibiting significant benefits compared to respective competition. On the other hand, the algorithm is fully implemented on hardware providing a further proof of concept regarding the implementation feasibility with respect to state-of-the art hardware design approaches. Finally, system-level performance benefits, regarding data transmission delay and energy saving, are highlighted, taking into consideration the characteristics of prominent IoT platforms. Concluding, this paper presents a holistic approach based on data compression that is able to drastically enhance an IoT platform’s performance and tackle efficiently a notorious challenge of highly demanding IoT applications such as real-time bio-signal monitoring
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