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A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

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Publication date10/01/2020
Number of pages6
Pages1-6
<mark>Original language</mark>English
Event2020 IEEE 17th Annual Consumer Communications and Networking Conference - Las Vegas, United States
Duration: 10/01/202014/01/2020

Conference

Conference2020 IEEE 17th Annual Consumer Communications and Networking Conference
Abbreviated titleCCNC IEEE
CountryUnited States
CityLas Vegas
Period10/01/2014/01/20

Abstract

In an effort towards designing a batteryless Internet of Things (IoT) sensor node that is powered by miniaturized energy-harvesting source(s), we combine the techniques of transient computing, approximate computing, data and energy predictions so as to handle the unpredictable power shortages of the miniaturized energy harvesting sources and reduce the overall power consumption of the IoT node. To evaluate the feasibility of our proposed approach, we build upon and extend an existing platform that consists of a peer-to-peer network (a sender node and a receiver node) where each of these nodes combines a Texas Instruments' FRAM-based micro-controller with a low cost, low power radio module and exchanging its data through SimpliciTI protocol. Our results illustrate that combining transient computing, approximate computing, data and energy predictions adds up their individual benefits to achieve an overall better utilization of the harvested energy of the node. Our results show that out of the total 60 transmissions that were due in an interval of 5 hours, for sending the temperature data from sender node to the receiver node every 5 minutes, a total of 32 transmissions were avoided, leading to a saving of more than 50% of the radio transmissions in the sender node. © 2020 IEEE.

Bibliographic note

Export Date: 4 June 2020 Funding details: European Regional Development Fund, FEDER Funding details: 668995 Funding text 1: This project has received funding partly from European Union’s Horizon 2020 Research and Innovation Program under Grant 668995 and European Union Regional Development Fund in the framework of the Tallinn University of Technology Development Program 2016–2022. This material reflects only the authors’ view and the EC Research Executive Agency is not responsible for any use that may be made of the information it contains.