Hybrid Metamodeling/Metaheuristic Assisted Multi-Transmitters Placement Planning

Parnianifard, Amir; Saadi, Muhammad; Pengnoo, Manus; Imran, Muhammad Ali; Otaibi, Sattam Al; Sasithong, Pruk; Vanichchanunt, Pisit; Polysuwan, Tuchsanai; Wuttisittikulkij, Lunchakorn

doi:10.32604/cmc.2021.015730

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…In 50 , a comparative study between Kriging and GA for optimal transmitter location in an indoor environment has been performed from the fields scattered in the environment. In 51 , the PSO integrated with another surrogate named Radial Basis Function (RBF) has been applied to obtain the optimal placement of multiple transmitters by maximizing the overall signal coverage in an objective function, and controlling the intersection of transmitters in a constraint. In recent work 52 , a surrogate-based evolutionary algorithm by proposing the Mahalanobis sampling surrogate model assisting Ant Lion Optimizer (ALO) method has been applied to compute optimal coverage in a single objective three-dimensional WSN model.…”

Section: Background Of Study and Motivationsmentioning

confidence: 99%

A data driven approach in less expensive robust transmitting coverage and power optimization

Parnianifard

Mumtaz

Chaudhary

et al. 2022

Sci Rep

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This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively).

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Section: Background Of Study and Motivationsmentioning

confidence: 99%

A data driven approach in less expensive robust transmitting coverage and power optimization

Parnianifard

Mumtaz

Chaudhary

et al. 2022

Sci Rep

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show abstract

“…Recent studies and literature have increasingly emphasized the advantages of utilizing metamodels in various engineering design applications, including audio-visual speech recognition. This growing preference for metamodels over alternative methods is primarily driven by the escalating complexity of real-world systems, which often require approximation techniques that are both accurate and cost-effective, as cited in [2,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Metamodeling techniques are intricately linked with the Design and Analysis of Computer Experiments (DACE).…”

Section: Metamodellingmentioning

confidence: 99%

“…On the other hand, global methods encompass evolutionary algorithms and swarm-based techniques, which have become particularly prominent in the field of computational intelligence. Notable examples of these algorithms include the Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), as referenced in [23][24][25] In the context of computational intelligence, an evolutionary algorithm represents a specialized form of evolutionary computation, which operates on a population-based metaheuristic optimization framework. Drawing inspiration from the principles of biological evolution, these algorithms incorporate mechanisms such as reproduction, mutation, recombination, and selection.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization in Audio-Video Content

Parnianifard

2024

Preprint

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This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

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“…Legacy networks have a huge cell size; however, in next-generation networks, the cell size is dramatically reduced, which creates a major challenge for telecommunication network operators to scrupulously plan their complex networks. Networks of the 5G and beyond necessitate the massive deployment of base stations [71]. Wide-area network wireless communication technology, namely 5G cellular communications, is the major form of technology used here for communication [72].…”

Section: • Virtual Reality (Vr)mentioning

confidence: 99%

Digital-Twins Towards Cyber-Physical Systems: A Brief Survey

Parnianifard¹,

Wuttisittikulkij²

2022

Preprint

Self Cite

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Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS.

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Hybrid Metamodeling/Metaheuristic Assisted Multi-Transmitters Placement Planning

Cited by 7 publications

References 39 publications

A data driven approach in less expensive robust transmitting coverage and power optimization

A data driven approach in less expensive robust transmitting coverage and power optimization

Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization in Audio-Video Content

Digital-Twins Towards Cyber-Physical Systems: A Brief Survey

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