TY - GEN
T1 - Low-energy algorithm for self-controlled Wireless Sensor Nodes
AU - Bin Baharudin, Ahmad Muzaffar
AU - Saari, Mika
AU - Sillberg, Pekka
AU - Rantanen, Petri
AU - Soini, Jari
AU - Kuroda, Tadahiro
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/12/7
Y1 - 2016/12/7
N2 - In Internet of Things (IoT), the lifespan of Wireless Sensor Networks (WSN) has often become an issue. Sensor nodes are typically battery powered. However, high energy consumption by Radio Frequency (RF) module limits the lifespan of sensor nodes. In conventional WSN, the frequency of data transmission is normally fixed or adjusted according to requests from the gateway. In this paper, we present a WSN system for intelligent sensing. We propose a low-energy algorithm for sensor data transmission from sensor nodes for such system. In this algorithm, the sensor nodes are able to self-control their data transmission according to the trends of data. We adopt Adaptive Duty Cycle for adjustment of data transmission frequency and Compressive Sensing (CS) for sensor data compression. The simulation results show that Collective Transmission with CS-based data compression achieves 83.34% of RF energy reduction for the best-case transmission and 83.31% of RF energy reduction in the worst-case transmission, compared to the Continuous Transmission.
AB - In Internet of Things (IoT), the lifespan of Wireless Sensor Networks (WSN) has often become an issue. Sensor nodes are typically battery powered. However, high energy consumption by Radio Frequency (RF) module limits the lifespan of sensor nodes. In conventional WSN, the frequency of data transmission is normally fixed or adjusted according to requests from the gateway. In this paper, we present a WSN system for intelligent sensing. We propose a low-energy algorithm for sensor data transmission from sensor nodes for such system. In this algorithm, the sensor nodes are able to self-control their data transmission according to the trends of data. We adopt Adaptive Duty Cycle for adjustment of data transmission frequency and Compressive Sensing (CS) for sensor data compression. The simulation results show that Collective Transmission with CS-based data compression achieves 83.34% of RF energy reduction for the best-case transmission and 83.31% of RF energy reduction in the worst-case transmission, compared to the Continuous Transmission.
KW - Intelligent Sensing
KW - Internet of Things (IoT)
KW - Low-Energy Algorithm
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85010298679&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010298679&partnerID=8YFLogxK
U2 - 10.1109/WINCOM.2016.7777188
DO - 10.1109/WINCOM.2016.7777188
M3 - Conference contribution
AN - SCOPUS:85010298679
T3 - Proceedings - 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016: Green Communications and Networking
SP - 42
EP - 46
BT - Proceedings - 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016
A2 - El-Kamili, Mohamed
A2 - Ghennioui, Hicham
A2 - Badri, Abdelmajid
A2 - Berrada, Ismail
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016
Y2 - 26 October 2016 through 29 October 2016
ER -