Automatic MAC protocol selection in wireless networks based on reinforcement learning

Gomes, André Raeli; Macedo, Daniel F.; Vieira, Luiz F. M.

doi:10.1016/j.comcom.2019.10.023

Cited by 12 publications

(2 citation statements)

References 24 publications

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“…The performance evaluations demonstrated significant advantages under varying network topologies. SOMAC (Self-Organizing MAC) is a recent RL-based MAC approach that alters the MAC dynamism with respect to current network requirements [32]. SOMAC has its basics in an intelligent selection of the best MAC scheme through the RL approach employed.…”

Section: Related Workmentioning

confidence: 99%

Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning

Acik,

Kosunalp,

Tabakcioglu

et al. 2023

Electronics

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Intelligent medium access control (MAC) protocols have been a vital solution in enhancing the performance of a variety of wireless networks. ALOHA, as the first MAC approach, inspired the development of several MAC schemes in the network domain, with the primary advantage of simplicity. In this article, we present design, implementation, and performance evaluations of the ALOHA approach, through significant improvements in attaining high channel utilization as the most important performance metric. A critical emphasis is currently focused on removing the burden of packet collisions, while satisfying requirements of energy and delay criteria. We first implement the ALOHA protocol to practically explore its performance behaviors in comparison to analytical models. We then introduce the concept of dynamic payload instead of fixed-length packets, whereby a dynamic selection of the length of each transmitted packet is employed. Another specific contribution of this paper is the integration of the transmission policy of ALOHA with the potential of Internet of Things (IoT) opportunities. The proposed policy utilizes a state-less Q-learning strategy to achieve the maximum performance efficiency. Performance outputs prove that the proposed idea ensures a maximum throughput of approximately 58%, while ALOHA is limited to nearly 18% over a single-hop scenario.

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Section: Related Workmentioning

confidence: 99%

Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning

Acik,

Kosunalp,

Tabakcioglu

et al. 2023

Electronics

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“…Cell-LEACH [20][21][22] Cell-based clustering approach is provided for reduced energy consumption; the residual energy of cell members is also considered.…”

Section: Wireless Sensor Network-routing Challenges and Design Issuesmentioning

confidence: 99%

ETH‐LEACH: An energy enhanced threshold routing protocol for WSNs

Chithaluru

Khan

Kumar

et al. 2021

Int J Communication

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Many wireless sensor-based applications use LEACH as a preferred routing protocol owing to its unique features such as optimal sleeping time, minimum packet collisions, dynamic channel selection, and least power consumption. The traditional LEACH protocol wastes the transmission opportunities as it processes data only in an event occurring, leading to wastage of resources. To resolve this issue, this paper proposes a more robust Energy Enhanced Threshold Routing Protocol (ETH-LEACH) for WSNs. The working of ETH-LEACH is conceptualized in two parts. In the first part, TDMA is implemented to estimate the opportunistic paths to remove network overhead. Furthermore, in the second part, a threshold value is calculated for choosing the forwarder nodes. The proposed technique minimizes the energy usage of the sensor nodes and consequently enhances the network's lifetime by extending the duration of node death. The ETH-LEACH protocol is contrasted with the different variants of LEACH to verify its effectiveness. The experimental results show that the proposed ETH-LEACH protocol outperforms the traditional routing protocols. In this paper, the ETH-LEACH protocol performs nearly 54.6% efficient than LEACH, 47.6% efficient than Q-LEACH, 41.3% efficient than NR-LEACH, 33.6% efficient than LEACH-GA, and 29.7% efficient than LEACH-POS in reducing the usage of energy in the overall simulation.

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Enhanced traffic-adaptive slotted MAC for IoT-based smart monitoring grid

Sood

Sharma

2021

J Supercomput

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Automatic MAC protocol selection in wireless networks based on reinforcement learning

Cited by 12 publications

References 24 publications

Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning

Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning

ETH‐LEACH: An energy enhanced threshold routing protocol for WSNs

Enhanced traffic-adaptive slotted MAC for IoT-based smart monitoring grid

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