Squaring Back off Based Media Access Control for Vehicular Ad-hoc Networks

Sharma, Kamal; Aggarwal, Mukul; Yadav, Neha

doi:10.1007/978-81-322-1768-8_40

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…Therefore, adaptive duty cycle in MDA-SMAC protocol means adjusting the duty cycle of the micro-duty. [11] in the S-MAC protocol set the maximum back-off window to be 63. However, fixed setting method for window back-off interval is not suitable for dynamically changing wireless sensor networks, and therefore, developing the flow-adaptive back-off algorithm is a direction for improving S-MAC protocol.…”

Section: Suitable Methods For Traffic Duty Cyclementioning

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: Suitable Methods For Traffic Duty Cyclementioning

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

View full text Add to dashboard Cite

show abstract

“…In adapting mechanism of CSMA/CA and competitionbased MAC protocol, the exponential back-off algorithm is an important component [27][28][29] and it decides the efficiency of CSMA/CA. Nodes need to follow a certain back-off algorithm to wait for resending data when the channel is listened to be occupied or clashed with other nodes.…”

Section: Self-adaptive Back-off Algorithmmentioning

confidence: 99%

An adaptive traffic MAC protocol based on correlation of nodes

Wang

2015

J Wireless Com Network

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EA-MAC protocol is proposed on the basis of SMAC protocol to remedy the shortcomings of SMAC. In the EA-MAC, node correlation analysis algorithm and traffic adaptive duty cycle mechanism are added. Meanwhile, the residual energy is introduced into the existing traffic adaptive back-off mechanism to measure the saving efficiency. In the node correlation algorithm, all network nodes are divided into several areas by computing node correlation according to the collected information. The clustering mechanism is applied for choosing representative node in each area for transmitting data. This method can effectively reduce redundant nodes transmitting duplicate data. In traffic adaptive duty cycle mechanism, the duty cycle is regulated dynamically to decrease idle listening by comparing the threshold set with the flow value obtained from the predict flow model. In back-off mechanisms, by adjusting the value of contention window and back-off time, data collisions can be effectively avoided when network traffic is heavy. In addition, nodes with more remaining energy have priority to access the channel and have shorter back-off time, which can keep the balance of the whole network energy consumption and lengthen network lifetime. Simulation results show that the EA-MAC protocol has better energy saving, throughput, shorter delay performance, and low packet loss rate than that of SMAC protocol in dynamic traffic sensor networks.

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“…The hierarchical model is used to determine the membership degree, and the gray correlation degree is used to solve the weight method to estimate the state. [10] the grey model is applied to the prediction model of coal mine working face pressure, and the PSO algorithm is used to select the optimal parameters in the grey model, so as to achieve good prediction effect [11].…”

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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Squaring Back off Based Media Access Control for Vehicular Ad-hoc Networks

Cited by 4 publications

References 3 publications

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

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

An adaptive traffic MAC protocol based on correlation of nodes

Roof Pressure Prediction in Coal Mine Based on Grey Neural Network

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