The Performance of the IEEE 802.11 DCF for Different Contention Window in VANETs

Karabulut, Muhammet Ali; Shah, A. F. M. Shahen; İlhan, Hacı

doi:10.1109/tsp.2018.8441481

Cited by 16 publications

(9 citation statements)

References 7 publications

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“…They set the initial CW value to CW min , and increase it exponentially after each transmission failure until it reaches CW max [ 6 ]. In [ 6 , 7 , 8 ], DCF and EDCA are modeled and simulated, and the CW value is also discussed. The results show that the CW value greatly influences communication performance such as throughput, fairness, and collision probability.…”

Section: Related Workmentioning

confidence: 99%

“…The action space, set according to the CW value used by the security messages in EDCA, is [3,7,15,31,63,127,255]. Based on the four possible states and seven possible actions, the size of the Q-table maintained by a node is 4 × 7.…”

Section: Qmac-2nd Designmentioning

confidence: 99%

“…Algorithm 1 describes the detailed implementation of QMAC-2ND, where s t ∈ [0, 3] represents four different levels of the number of two-hop neighbors, 0, 1, 2, 3 correspond to [0, 10], [11,30], [31,70], and [71+], respectively. And a t ∈ [0, 6] represents seven different CWs in Action [7]. When the ACK times out, the AckFactor of the replying node in the neighbor table is set to 0.…”

Section: Detailed Implementation Of Qmac-2nd Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Joint Optimization of Multi-Hop Broadcast Protocol and MAC Protocol in Vehicular Ad Hoc Networks

Pei

Wang

Lei

et al. 2021

Sensors

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Beacon messages and emergency messages in vehicular ad hoc networks (VANETs) require a lower delay and higher reliability. The optimal MAC protocol can effectively reduce data collision in VANETs communication, thus minimizing delay and improving reliability. In this paper, we propose a Q-learning MAC protocol based on detecting the number of two-hop neighbors. The number of two-hop neighbors in highway scenarios is calculated with very little overhead using the beacon messages and neighbor locations to reduce the impact of hidden nodes. Vehicle nodes are regarded as agents, using Q-learning and beacon messages to train the near-optimal contention window value of the MAC layer under different vehicle densities to reduce the collision probability of beacon messages. Furthermore, based on the contention window value after training, a multi-hop broadcast protocol combined with contention window adjustment for emergency messages in highway scenarios is proposed to reduce forwarding delay and improve forwarding reliability. We use the trained contention window value and the state information of neighboring vehicles to assign an appropriate forwarding waiting time to the forwarding node. Simulation experiments are conducted to evaluate the proposed MAC protocol and multi-hop broadcast protocol and compare them with other related protocols. The results show that our proposed protocols outperform the other related protocols on several different evaluation metrics.

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Section: Related Workmentioning

confidence: 99%

Section: Qmac-2nd Designmentioning

confidence: 99%

Section: Detailed Implementation Of Qmac-2nd Algorithmmentioning

confidence: 99%

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Joint Optimization of Multi-Hop Broadcast Protocol and MAC Protocol in Vehicular Ad Hoc Networks

Pei

Wang

Lei

et al. 2021

Sensors

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“…Data transmission occurs at MAC layer using IEEE 802.11 distributed coordination function (DCF). Performance analysis of the IEEE 802.11 DCF is provided in [7–14]. To ensure QoS at the MAC layer an enhanced distributed channel access (EDCA) mechanism is implemented.…”

Section: Related Workmentioning

confidence: 99%

CB‐MAC: a novel cluster‐based MAC protocol for VANETs

Shah

İlhan

Tureli

2018

IET Intelligent Transport Systems

Self Cite

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“…The impact of IEEE 802.11 in FANETs has been studied recently in [2]. It is apparent that the performance is contingent on the size of the contention window (CW) [2,[17][18][19]. In this article, in order to optimize performance, CW size is optimized using three meta-heuristic optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimization of drones communication by using meta-heuristic optimization algorithms

Shah

Karabulut

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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Unmanned aerial vehicles (UAVs), more generally known as drones or remotely piloted aircraft, h ave b een e xtensively u sed i n b oth c ivilian a ctivities a nd m ilitary m issions b ecause of their high mobility and low cost. Multi-UAV systems structured in an ad hoc manner called UAVs ad hoc network which is also familiar as flying ad hoc Network (FANET). For FANETs, the IEEE 802.11 standard offers Medium Access Control (MAC) layer requirements. Optimization of the contention window (CW) size will optimize the output. Three distinct meta-heuristic optimization algorithms are used in this paper to improve the efficiency of FANETs, which are the Cuckoo Search Algorithm (CUCO), the Differential Evolution Algorithm (DEA)\ and the Honey Bee Algorithm (HBA). Optimum CW size is defined through meta-heuristic optimization algorithms. Performance comparison among CUCO, DEA, HBA, and traditional MAC based on IEEE 802.11 is presented. Relationships among parameters are obtained through Markov chain based analytical study. Performance metrics such as successful transmission probability, collision probability, channel busy probability, throughput, packet dropping rate (PDR), and delay expressions are derived. Simulation results reveal that meta-heuristic optimization algorithms improve the quality and reliability of communication by increasing throughput and decreasing PDR and latency.

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The Performance of the IEEE 802.11 DCF for Different Contention Window in VANETs

Cited by 16 publications

References 7 publications

Joint Optimization of Multi-Hop Broadcast Protocol and MAC Protocol in Vehicular Ad Hoc Networks

Joint Optimization of Multi-Hop Broadcast Protocol and MAC Protocol in Vehicular Ad Hoc Networks

CB‐MAC: a novel cluster‐based MAC protocol for VANETs

Optimization of drones communication by using meta-heuristic optimization algorithms

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