Next Article in Journal
A New Type of Explosive Chemical Detector Based on an Organic Photovoltaic Cell
Next Article in Special Issue
A Formally Reliable Cognitive Middleware for the Security of Industrial Control Systems
Previous Article in Journal
Two-Dimensional Electronics and Optoelectronics: Present and Future
Previous Article in Special Issue
Exploiting Hardware Vulnerabilities to Attack Embedded System Devices: a Survey of Potent Microarchitectural Attacks
Article Menu

Export Article

Open AccessArticle
Electronics 2017, 6(3), 54; doi:10.3390/electronics6030054

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

Computer & Informatics Engineering Department, Technological Educational Institute ofWestern Greece, 30020 Antirio, Greece
*
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 20 July 2017 / Accepted: 27 July 2017 / Published: 31 July 2017
(This article belongs to the Special Issue Real-Time Embedded Systems)
View Full-Text   |   Download PDF [1950 KB, uploaded 4 August 2017]   |  

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. View Full-Text
Keywords: IoT Wireless Sensor Network platforms; data compression; hardware accelerator; Wireless Sensor Networks; embedded systems; complete solution; experimental evaluation; hardware design; ultra-low power IoT Wireless Sensor Network platforms; data compression; hardware accelerator; Wireless Sensor Networks; embedded systems; complete solution; experimental evaluation; hardware design; ultra-low power
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Antonopoulos, C.P.; Voros, N.S. A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms. Electronics 2017, 6, 54.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top