Data Research, Vol. 2, Issue 3, Jun  2018, Pages 88-96; DOI: 10.31058/ 10.31058/

A Comprehensive Model for Energy Consumption in Wireless Sensor Networks Using the Markov Model

, Vol. 2, Issue 3, Jun  2018, Pages 88-96.

DOI: 10.31058/

Seyyedjalaleddin Dastgheib 1 , Farzaneh Fekrisafarizadeh 2*

1 Department of Computer Engineering, Shiraz University, Shiraz, Iran

2 Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

Received: 8 July 2018; Accepted: 24 August 2018; Published: 16 October 2018

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Due to the availability of energy-efficient sensors, microprocessors, and radio frequency circuits for data transfer, wireless sensor networks developed rapidly and spread. Wireless sensor networks that include thousands of low-cost sensor nodes are used in various applications such as health surveillance, battlefield surveillance, and environmental monitoring. The sensor node non-rechargeable, non-replaceable and limited power supply is considered as the main challenges of these type of networks. With the completion of the nodes power supply, the node actually remains unused. Sleep-wake scheduling is used to reduce energy consumption and extend the life of nodes. In this paper we try to investigate sleep-wake scheduling in sensor nodes with the Markov model. In probability theory, a Markov model is a stochastic model used to model randomly changing systems where it is assumed that future states depend only on the current state not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modeling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property. It is expected that the proposed Markov model covers all aspects of sleep/wake scheduling in wireless sensor networks.


Wireless Sensor Network, Markov Model, Scheduling, Sleep/Awake


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


[1] Gogu, A.; Nace, D.; Natalizio, E.; Challal, Y. Using dynamic programming to solve the Wireless Sensor Network Configuration Problem. Journal of Network and Computer Applications, 2017, 83, 140-154.

[2] Liu, Y.; Liu, D.; Zhao, Y.; Wang, L. The reliability analysis of wireless sensor networks based on the energy restrictions. International Journal of Wireless and Mobile Computing, 2016, 10(4), 399-406.‏

[3] Le, D.T;. LeDuc, T.; Zalyubovskiy, V.V.; Kim, D.S.; Choo, H. Collision-tolerant broadcast scheduling in duty-cycled wireless sensor networks. Journal of Parallel and Distributed Computing, 2017, 100, 42-56.‏

[4] Cheng, H.; Su, Z.; Xiong, N.; Xiao, Y. Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model. Information Sciences, 2016, 329, 461-477.‏

[5] Golbon-Haghighi, M.H.; Mahboobi, B.B.; Ardebilipour, M. Linear Pre-coding in MIMO-CDMA Relay Networks. Wireless Personal Communications (Springer), 2014, 79( 2), 1321-1341.

[6] Golbon-Haghighi, M.H. Beamforming in Wireless Networks, in Towards 5G Wireless Networks. A Physical Layer Perspective, H.K. Bizaki, Editor, 2016, InTech.

[7] Zhen, C.; Liu, W.; Liu, Y.; Yan, A. Energy-efficient sleep/wake scheduling for acoustic localization wireless sensor network node. International Journal of Distributed Sensor Networks, 2014, 10( 2), 970524.

[8] Zhang, H.; Ni, W.; Li, X.; Yang, Y. Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 48(2), 177-194‏.

[9] Zhu, J.; Jiang, D.; Ba, S.; Zhang, Y. A game-theoretic power control mechanism based on hidden Markov model in cognitive wireless sensor network with imperfect information. Neurocomputing, 2017, 220, 76-83.‏

[10] Dbibih, I.; Zytoune, O.; Aboutajdine, D. On/off Markov model based energy-delay aware mac protocol for wireless sensor network. Wireless personal communications, 2014, 78(2), 1143-1155.‏

[11] Zhang, Y.; Li, W. Modeling and energy consumption evaluation of a stochastic wireless sensor network. EURASIP Journal on Wireless Communications and Networking, 2012, 1, 282.‏

[12] Li, W.; Fang, W. Performance evaluation of wireless cellular networks with mixed channel holding times. IEEE Transactions on Wireless Communications, 2008, 7(6), 2154-2160.‏

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