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Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization

1
School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
2
Department of Mathematics, Government College for Women, Karnal 132001, India
3
Department of Computer Science & Engineering, GL Bajaj Institute of Technology and Management, Greater Noida 201306, India
4
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 534; https://doi.org/10.3390/electronics8050534
Received: 18 April 2019 / Revised: 5 May 2019 / Accepted: 8 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Vehicular Networks and Communications)
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Abstract

Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads. View Full-Text
Keywords: wireless sensor networks; S-MAC; T-MAC; discrete-time Markov chain; energy optimization wireless sensor networks; S-MAC; T-MAC; discrete-time Markov chain; energy optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ram, M.; Kumar, S.; Kumar, V.; Sikandar, A.; Kharel, R. Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization. Electronics 2019, 8, 534.

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