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

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction. / Khan, S.Z.; Kakar, R.; Alam, M.M. et al.
2020. 1-6 Paper presented at 2020 IEEE 17th Annual Consumer Communications and Networking Conference, Las Vegas, United States.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Khan, SZ, Kakar, R, Alam, MM, Moullec, YL & Pervaiz, H 2020, 'A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction', Paper presented at 2020 IEEE 17th Annual Consumer Communications and Networking Conference, Las Vegas, United States, 10/01/20 - 14/01/20 pp. 1-6. https://doi.org/10.1109/CCNC46108.2020.9045177

APA

Khan, S. Z., Kakar, R., Alam, M. M., Moullec, Y. L., & Pervaiz, H. (2020). A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction. 1-6. Paper presented at 2020 IEEE 17th Annual Consumer Communications and Networking Conference, Las Vegas, United States. https://doi.org/10.1109/CCNC46108.2020.9045177

Vancouver

Khan SZ, Kakar R, Alam MM, Moullec YL, Pervaiz H. A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction. 2020. Paper presented at 2020 IEEE 17th Annual Consumer Communications and Networking Conference, Las Vegas, United States. doi: 10.1109/CCNC46108.2020.9045177

Author

Khan, S.Z. ; Kakar, R. ; Alam, M.M. et al. / A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction. Paper presented at 2020 IEEE 17th Annual Consumer Communications and Networking Conference, Las Vegas, United States.6 p.

Bibtex

@conference{a951f667e0274e9885f572a2a4bca5bf,

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

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. {\textcopyright} 2020 IEEE.",

keywords = "Approximate Computing, Data Prediction, Energy Prediction, SimipliciTI, Transient Computing, Energy harvesting, Ferroelectric RAM, Forecasting, Green computing, Low power electronics, Peer to peer networks, Radio broadcasting, Radio transmission, Sensor nodes, Battery-less, Energy prediction, Internet of Things (IOT), Low power radios, Power shortage, Receiver nodes, Temperature data, Texas Instruments, Internet of things",

author = "S.Z. Khan and R. Kakar and M.M. Alam and Y.L. Moullec and H. Pervaiz",

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{\textquoteright}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{\textquoteright} view and the EC Research Executive Agency is not responsible for any use that may be made of the information it contains.; 2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC IEEE ; Conference date: 10-01-2020 Through 14-01-2020",

year = "2020",

month = jan,

day = "10",

doi = "10.1109/CCNC46108.2020.9045177",

language = "English",

pages = "1--6",

}

RIS

TY - CONF

T1 - A Green IoT Node Incorporating Transient Computing, Approximate Computing and Energy/Data Prediction

AU - Khan, S.Z.

AU - Kakar, R.

AU - Alam, M.M.

AU - Moullec, Y.L.

AU - Pervaiz, H.

N1 - 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.

PY - 2020/1/10

Y1 - 2020/1/10

N2 - 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.

AB - 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.

KW - Approximate Computing

KW - Data Prediction

KW - Energy Prediction

KW - SimipliciTI

KW - Transient Computing

KW - Energy harvesting

KW - Ferroelectric RAM

KW - Forecasting

KW - Green computing

KW - Low power electronics

KW - Peer to peer networks

KW - Radio broadcasting

KW - Radio transmission

KW - Sensor nodes

KW - Battery-less

KW - Energy prediction

KW - Internet of Things (IOT)

KW - Low power radios

KW - Power shortage

KW - Receiver nodes

KW - Temperature data

KW - Texas Instruments

KW - Internet of things

U2 - 10.1109/CCNC46108.2020.9045177

DO - 10.1109/CCNC46108.2020.9045177

M3 - Conference paper

SP - 1

EP - 6

T2 - 2020 IEEE 17th Annual Consumer Communications and Networking Conference

Y2 - 10 January 2020 through 14 January 2020

ER -

Research

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