Network-Based Heterogeneous Particle Swarm Optimization and Its Application in UAV Communication Coverage

Wei, Du; Ying, Wen; Yang, Peng; Cao, Xianbin; Yan, Gang; Tang, Ke; Wu, Dapeng

doi:10.1109/tetci.2019.2899604

Cited by 45 publications

(17 citation statements)

References 58 publications

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“…It was shown that the RL approach achieves higher overall peak user coverage rates with the cost of slower convergence and the risk of occasional dips while the swarm intelligence-based solution resulted in lower coverage peaks and improved coverage stability and convergence. Next, the authors in Reference [71] proposed and applied network-based heterogeneous particle swarm optimization (NHPSO) in an air-to-ground downlink communication scenario, where multiple UAVs were deployed to provide wireless connectivity to a set of quasi-stationary heterogeneous users uniformly distributed in a geographical area with different required data transfer rates. The goal of NHPSO learning algorithm was to find the global optimum of a complex radio coverage problem while maximizing users’ total required data rates.…”

Section: Resource Management and Network Planningmentioning

confidence: 99%

A Survey on Machine-Learning Techniques for UAV-Based Communications

Bithas

Michailidis

Νομικός

et al. 2019

Sensors

260

144

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Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

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Section: Resource Management and Network Planningmentioning

confidence: 99%

A Survey on Machine-Learning Techniques for UAV-Based Communications

Bithas

Michailidis

Νομικός

et al. 2019

Sensors

260

144

View full text Add to dashboard Cite

show abstract

“…Step 5. Generate uniformly distributed WOA and other parameters through equation ( 13), update parameters a, A, and C at the same time, and update parameters through equation (14).…”

Section: Application Of Sca-woa Algorithm In Optimal Coverage Of Hwsnsmentioning

confidence: 99%

Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

Yue

You

Wang

et al. 2021

International Journal of Distributed Sensor Networks

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Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

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“…Clearly, θ i is bounded. Let ε 0 = 2, it follows that the parameter matrices in (11) and 12 d i2 , d i3 , d i4 , d i5 ), thus the problem (5) for nonlinear system (3) is transfomated into the DOSCP (5) for uncertain nonlinear state space equation (4). Then, we have y * = −0.9175, which is the optimal solution of problem (5).…”

Section: Simulationmentioning

confidence: 99%

“…D ISTRIBUTED coordination for large-scale networked systems with many units has attracted increasing attentions owing to its extensive applications. In these systems, units can interact with each other via a communication network to perform complex tasks, e.g., consensus, formation control, resource allocation and optimization [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal State Consensus for Multiple Circuit Systems With Disturbance Rejection

Wang

Dong

2020

IEEE Trans. Netw. Sci. Eng.

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This paper investigates the distributed optimal state consensus problem for an electronic system with a group of circuit units. The dynamics of each unit is modeled by a Chua's circuit in the presence of disturbance generated by an external system. By means of the internal model approach and feedback control, a compensator-based continuous-time algorithm is proposed to minimize the sum of all cost functions associated with each individual unit in a cooperative manner. Supported by convex analysis, graph theory and Lyapunov theory, it is proved that the proposed algorithm is exponentially convergent. Compared with the centralized algorithms, the proposed protocol possesses remarkable superiority in improving scalability and reliability of multiple circuit systems. Moreover, we also study the distributed uncertain optimal state consensus problem and a linear regret bound is obtained in this case. Finally, a state synchronization example is provided to validate the effectiveness of the proposed algorithms.

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Network-Based Heterogeneous Particle Swarm Optimization and Its Application in UAV Communication Coverage

Cited by 45 publications

References 58 publications

A Survey on Machine-Learning Techniques for UAV-Based Communications

A Survey on Machine-Learning Techniques for UAV-Based Communications

Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

Distributed Optimal State Consensus for Multiple Circuit Systems With Disturbance Rejection

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