Abstract
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.
Highlights
Internet of Things (IoT) short-range, ultra-low power communication technologies comprise one of the most rapidly evolving research areas attracting significant interest both from academia and the industry [1]
IoT and Cyber-Physical Systems (CPS) have significantly benefited from the advancements in hardware and Very Large Scale Integration (VLSI) design leading to very low cost, complexity, size and, most importantly, power consumption embedded systems able to be used as a suitable hardware infrastructure [2,3,4]
This paper proposes a holistic solution that allows mitigating the respective side effects based on a highly efficient and resource conservative data compression hardware accelerator
Summary
Internet of Things (IoT) short-range, ultra-low power communication technologies comprise one of the most rapidly evolving research areas attracting significant interest both from academia and the industry [1]. IoT and CPS have significantly benefited from the advancements in hardware and Very Large Scale Integration (VLSI) design leading to very low cost, complexity, size and, most importantly, power consumption embedded systems able to be used as a suitable hardware infrastructure [2,3,4] Another aspect related to hardware design, concerns the anticipated advantages yielded by the hardware accelerators. Emphasizing on the utilization of such algorithms in the context of WSNs, our elicitation process focuses on low complexity in order to offer viable solutions for typical WSN nodes It aims at minimizing the delay overhead and operating in a time constrained manner [10]. Taking into account a wide range of adequate compression approaches, the algorithms Lossless Entropy Compression (LEC) [8] and Adaptive Lossless Entropy
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