Data Fairness Transmission and Adaptive Duty Cycle through Machine Learning in wireless Sensor Networks

Junheon Jeon - Sangmyung University, Cheonan, Republic of Korea
Hyunjoo Park - Sangmyung University, Cheonan, Republic of Korea

Citation Format:



In this paper, we propose the data fairness transmission and adaptive duty cycle through machine learning in wireless sensor networks. The mechanism of this paper is mainly composed of two parts. The proposed mechanism is based on the sleep-wake structure, which is one of the methods to increase the lifespan of the entire network by efficiently using the energy of the nodes. The first is a mechanism to support priority and data fairness. To this end, data input to the node is divided into priority classes according to transmission urgency and stored. Introduces the concept of cross-layer to rearrange data destined for the same destination. In addition, we propose a fair data transmission mechanism that allows even low-priority data to participate in transmission after a certain period. The second is an adaptive duty cycle mechanism through machine learning. For this purpose, public data related to forest fires are collected. The collected data is refined into data for each forest fire location and data for each forest fire time. For the refined data, an SVM (Support Vector Machine) model of supervised learning is used for machine learning, and a mechanism for adaptively adjusting the duty cycle of each node through the trained model is proposed. The computer language used for machine learning is Python language, and Google's Psychic Learn is used for the machine learning library. It was compared with the existing MAC protocol for evaluation, and it was confirmed that excellent energy efficiency results were obtained.


Fairness transmission; duty cycle; machine learning; support vector machine; wireless sensor networks.

Full Text:



M. Shafiq, H. Ashraf, A. Ullah, & S. Tahira, Systematic literature review on energy efficient routing schemes in WSN–a survey. Mobile Networks and Applications, 25(3), 882-895. 2020.

O.I. Khalaf, G.M. Abdulsahi. Energy efficient routing and reliable data transmission protocol in WSN. Int. J. Advance Soft Compu. Appl, 12(3), 45-53.2020.

P Maheshwari, AK Sharma, K Verma, Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110, 102317. 2021.

C Xu, Z Xiong, G Zhao, S Yu, An energy-efficient region source routing protocol for lifetime maximization in WSN. IEEE Access, 7, 135277-135289.2019.

A Maatouk, M Assaad, On the age of information in a CSMA environment. IEEE/ACM Transactions on Networking, 28(2), 818-831.2020.

W. Ye, J. Heidemann, and D. Estrin, Medium access control with coordinated adaptive sleeping for wireless sensor networks, IEEE/ACM Transaction on Networking, Vol. 12, Issue3, pp. 493-506, June 2004.

Z Xu, J Luo, Z Yin, T He, F Dong, S-MAC: achieving high scalability via adaptive scheduling in LPWAN. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications (pp. 506-515). IEEE.2020.

T. Dam, K. Langendoen, An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks, ACM Sensys’03, Nov 2003.

H Pan, SC Liew, Information update: TDMA or FDMA?. IEEE Wireless Communications Letters, 9(6), 856-860.2020.

M. Xie, X. Wang, "An Energy-Efficient TDMA Protocol for clustered Wireless Sensor Networks, 2008 ISECS International Colloquum on Computing, Communication, Control, and Management, Vol. 2, Issue, 3-4, pp. 547-551, Aug. 2008.

JS Lee, YS Yoo, HS Choi, T Kim, Energy-efficient TDMA scheduling for UVS tactical MANET. IEEE Communications Letters, 23(11), 2126-2129. 2019.

Tang Zhenzhou, Hu Qian, "An Adaptive Low Latency Cross-Layer MAC Protocol for Wireless Sensor Networks, 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 389-393, 2009.

A Li, W Liu, L Zeng, C Fa, Y Tan, An efficient data aggregation scheme based on differentiated threshold configuring joint optimal relay selection in WSNs. IEEE access, 9, 19254-19269.2021.

Q Wang, D Lin, P Yang, Z Zhang, An energy-efficient compressive sensing-based clustering routing protocol for WSNs. IEEE Sensors Journal, 19(10), 3950-3960.2019.

G. Kaur, P. Chanak, & M. Bhattacharya, Energy-efficient intelligent routing scheme for IoT-enabled WSNs. IEEE Internet of Things Journal, 8(14), 11440-11449. 2021.

C Wang, Y Zhang, X Wang, Z Zhang, Hybrid multihop partition-based clustering routing protocol for WSNs. IEEE Sensors Letters, 2(1), 1-4.2018.

D. Puri, & B. Bhushan, Enhancement of security and energy efficiency in WSNs: Machine Learning to the rescue. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 120-125). IEEE. 2019

Z Sun, L Wei, C Xu, T Wang, Y Nie, X Xing, J Lu, An energy-efficient cross-layer-sensing clustering method based on intelligent fog computing in WSNs. IEEE Access, 7, 144165-144177. 2019.

S. Huang, N. Cai, P.P. Pacheco, S. Narrandes, Y. Wang, & W. Xu, Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics, 15(1), 41-51. 2018.

S. Ray, A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). IEEE. 2019.

S. Sun, Z. Cao, H. Zhu, & J. Zhao, A survey of optimization methods from a machine learning perspective. IEEE transactions on cybernetics, 50(8), 3668-3681.2019.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.