MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

Philco, Luis Orlando; Marrone, Luis Armando; Estupiñan, Emily

doi:10.3390/app112311134

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Simulation results demonstrate that the proposed method can sustain overall network coverage in the presence of random damage events. Furthermore, Philco et al (2021) initially employed a multi-objective black widow optimization algorithm and Tsallis entropy-enabled Bayesian probability algorithm for dynamic sensor node scheduling. Subsequently, they utilized a multi-agent SARSA to determine the optimal mobile node for repairing coverage holes.…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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Section: Wireless Sensor Networkmentioning

confidence: 99%

A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

View full text Add to dashboard Cite

show abstract

“…The problem of minimizing the average energy consumption based on the data arrival rate is developed using the features of random variation in wireless channel quality, and then, it is translated into an optimal stopping problem, which demonstrates that the optimal stopping rule exists. Additionally, the energy consumption optimization strategy of data transmission based on the data arrival rate is realized, but this method suffers from a high packet loss rate [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Optimization Algorithm for Multipath Transmission of Distance Education Resources Using Reinforcement Learning

Wang¹,

Liang²

2022

Mobile Information Systems

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Distance education resources are the essential components of modern distance learning systems, and the development of high-quality resources is a potent assurance of modern distance education quality. The standard transmission efficiency assessment approach ignores the optimal data transmission stop rule and is unable to calculate the ideal stop time, resulting in excessive data transmission energy consumption and an evaluation result that does not match the real value. As a result, this study provides a reinforcement learning-based optimization technique for multipath transmission of distance education materials, because to calculate the optimal transmission rate thresholds in different detection time periods and realize the optimization of resource data transmission efficiency. According to the simulation result, the suggested efficiency optimization approach uses less energy on average and transmits data at a faster pace, improving resource data transmission efficiency. The result also shows that the proposed method performs well in terms of packet loss as compared to other existing methods.

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A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

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Recent advances in hardware and communication technologies have accelerated the deployment of billions of wireless sensors. This transformation has created a wide range of applications adapted to the evolving trends of our daily life requirements. Wireless sensor networks (WSNs) could be deployed in several target areas including buildings, forests, oceans, and smart cities. Nevertheless, finding the optimal location for each sensor node is a challenging task, typically when the environment involves heterogeneous obstacles. Many approaches and methods have been proposed to deal with the problem of WSN deployment, each addressing one or more objectives and constraints, such as network coverage, lifetime, connectivity, and energy consumption. The purpose of this survey paper is to provide the needed background to understand and study the WSNs deployment problem with a focus on its two key aspects: the optimization model and the solving methods based on artificial intelligence (AI). Additionally, it covers recent works on WSNs deployment and identifies their advantages and limitations. Furthermore, simulation experiments were carried out to compare the performance of widely used algorithms in the context of WSNs deployment problem, primarily genetic algorithm, particle swarm optimization, flower pollination, and ant colony optimization. Finally, this paper discusses and highlights several open challenges and research issues that must be explored in the future.

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MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

Cited by 3 publications

References 33 publications

A survey on applications of reinforcement learning in spatial resource allocation

A survey on applications of reinforcement learning in spatial resource allocation

Optimization Algorithm for Multipath Transmission of Distance Education Resources Using Reinforcement Learning

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

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