Stochastic Modeling and Analysis with Energy Optimization for Wireless Sensor Networks

Xu, Donghong; Wang, Ke

doi:10.1155/2014/672494

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Conserving energy for nodes is the primary task of wireless sensor network, data transmission are permitted to have a certain delay on the condition that energy are ensured to be efficiently conserved. To solve this problem of energy-constrain, Literature [5,6] proposed a clustering protocol based on residual energy. Therefore, on the basis of this paper, we propose an approach to select the number of micro-duty based on the remaining energy, and set the state of the micro-duty.…”

Section: Choosing and Setting Statementioning

confidence: 99%

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The energy-efficient MDA-SMAC protocol for wireless sensor networks

Wang

Xin²,

Shi

et al. 2020

J Wireless Com Network

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Nodes in original S-MAC protocol just can visit the channel in the scheduling and listening stage. The working schema may result in data latency and high conflict. To solve those above problems, we split scheduling duty into multiple micro-duties. By using different micro-dispersed contention channel, the sensor nodes reduce the collision probability of the data. Aiming at detecting the fixed duty cycle in S-MAC protocol, on the basis of the micro-duty and buffer queue length, this paper presents an adaptive duty cycle and back-off algorithm. While using different back-off algorithm with different duty cycles, sensor node Fast-Binary Exponential Backoff and Conflict-Avoid-Binary exponential Backoff algorithm separated are applied to reduce data latency further reduce the conflict probability. Combining both of the improvements, we propose a modified S-MAC protocol. Comparing the performance of S-MAC protocol and Division-Multiple Access-Media Access Control (MDA-SMAC) protocol on the NS-2 simulation platform, the results show that MDA-SMAC protocol performs better in terms of energy consumption, latency, and effective throughput than S-MAC protocol

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Section: Choosing and Setting Statementioning

confidence: 99%

“…Literature [3] solves this kind of problem by applying another Markov chain model, deriving the formula (2)(3)(4)(5)(6)(7)(8)(9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

The energy-efficient MDA-SMAC protocol for wireless sensor networks

Wang

Xin²,

Shi

et al. 2020

J Wireless Com Network

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“…If the channel is busy, the window value will be linearly changed by a factor l i and the contention window will be reselected the minimum between CW i + l i and CW min . On the basis of the algorithms Markov chain model in [32][33], we propose a theoretical analysis procedure of these algorithms. Supposing there is a current node in the network, each node will definitely send data after a period of channel competition.…”

Section: Theoretical Basis Of Micro-duty Mechanismmentioning

confidence: 99%

MDA-SMAC: An Energy-Efficient Improved SMAC Protocol for Wireless Sensor Networks

2018

KSII TIIS

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In sensor medium access control (SMAC) protocol, sensor nodes can only access the channel in the scheduling and listening period. However, this fixed working method may generate data latency and high conflict. To solve those problems, scheduling duty in the original SMAC protocol is divided into multiple small scheduling duties (micro duty MD). By applying different micro-dispersed contention channel, sensor nodes can reduce the collision probability of the data and thereby save energy. Based on the given micro-duty, this paper presents an adaptive duty cycle (DC) and back-off algorithm, aiming at detecting the fixed duty cycle in SMAC protocol. According to the given buffer queue length, sensor nodes dynamically change the duty cycle. In the context of low duty cycle and low flow, fair binary exponential back-off (F-BEB) algorithm is applied to reduce data latency. In the context of high duty cycle and high flow, capture avoidance binary exponential back-off (CA-BEB) algorithm is used to further reduce the conflict probability for saving energy consumption. Based on the above two contexts, we propose an improved SMAC protocol, micro duty adaptive SMAC protocol (MDA-SMAC). Comparing the performance between MDA-SMAC protocol and SMAC protocol on the NS-2 simulation platform, the results show that, MDA-SMAC protocol performs better in terms of energy consumption, latency and effective throughput than SMAC protocol, especially in the condition of more crowded network traffic and more sensor nodes.

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“…(1) Qualitative analysis. Literature [6] uses qualitative analysis as a commonly used and most widely used analysis method. Its main principle is to compare and analyze the monitored data or parameters with the theoretical data or parameters, so as to draw a qualitative conclusion whether the monitored data is in the safe range or conforms to the change law.…”

Section: Introductionmentioning

confidence: 99%

Roof Pressure Prediction in Coal Mine Based on Grey Neural Network

et al. 2020

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The prediction of roof pressure in mining area plays an important role in effectively preventing roof accidents and ensuring the safety of mine production. Because the roof pressure in the mine is affected by various natural and human factors, and there is a dynamic and fuzzy nonlinear relationship between the factors. At present, the lack of systematic management will seriously limit the analysis and judgment of the mine safety situation, and lead to the occurrence of mine accidents. In this paper, the compaction data of the roof of a mine return air working face in Xuzhou is taken as the experimental data, and an improved grey neural network model is proposed, which combines the grey theory with the neural network algorithm organically. It not only eliminates the shortcomings of the neural network model, but also makes up for the shortcomings of the grey network that cannot carry out self-feedback regulation. MATLAB is used to simulate and test the improved combined prediction model for roof pressure prediction, and the results are compared with those of single grey model and single BP neural network model. The simulation results show that the improved model not only improves the prediction efficiency, shortens the training time, but also improves the accuracy, so it is of great significance to the safety prediction of the mine roof. INDEX TERMS BP neural network, background value optimization, GM (1, n) model, roof pressure.

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Stochastic Modeling and Analysis with Energy Optimization for Wireless Sensor Networks

Cited by 8 publications

References 9 publications

The energy-efficient MDA-SMAC protocol for wireless sensor networks

The energy-efficient MDA-SMAC protocol for wireless sensor networks

MDA-SMAC: An Energy-Efficient Improved SMAC Protocol for Wireless Sensor Networks

Roof Pressure Prediction in Coal Mine Based on Grey Neural Network

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