Efficient Optimal Backhaul-aware Placement of Multiple Drone-Cells Based on Genetic Algorithm

Li, Xiaohui; Xing, Li

doi:10.1109/robio49542.2019.8961476

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Clustering can reduce energy consumption, increase network scalability, extend battery life, decrease network delay, and boost throughput [174,175]. The minimization of completion time [111,120,148,170], maximization of throughput [166,173], optimization of device density [167] and drone placement [165], as well as energy-related objectives [121,131,138,151,170] are the most prominent among the proposed models.…”

Section: Iotmentioning

confidence: 99%

“…Some area coverage models targeted the human operators and, in particular, the reduction in their cognitive workload while piloting [222] and their safety while teleoperating a construction machine from a safe distance [216]. Authors [165] proposed the future implementation of their method in communication coverage for monitoring operations as well. Exactly half of the selected models employ multiple drones, with the other half describing single-drone area coverage operations.…”

Section: Area Coveragementioning

confidence: 99%

“…In [141], the drone was equipped with various optical, weather, and location sensors, enabling it to identify the optimum UAV altitudes, longitudes, and latitudes; in [205], the UAV was free to fly within a predetermined topology at a specified rate. In [158,159,162,165], the UAV altitude was fixed. In [134], the hovering region was enhanced through a tether, ensuring longer flight times.…”

Section: Resource Allocation For Mobile Devicesmentioning

confidence: 99%

“…Evolutionary algorithms, such as genetic algorithms [121,146,152,155,165,215], multioptimization evolutionary algorithms based on decomposition (MOEA/D) [131,166], multiverse optimization algorithms [225], covariance matrix adaptation evolution strategies (CMA-ES) [173], and bio-inspired swarm mobility algorithms [132], including particle swarm optimization (PSO)-based algorithms [176,218,223] and shuffled shepherd optimization [151], have also been used to find near-optimal solutions. In [151,181], other types of metaheuristics have been proposed.…”

Section: Optimization Modelsmentioning

confidence: 99%

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Complementarity, Interoperability, and Level of Integration of Humanitarian Drones with Emerging Digital Technologies: A State-of-the-Art Systematic Literature Review of Mathematical Models

Aretoulaki

Ponis

Plakas

2023

Drones

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The adoption of drones and other emerging digital technologies (DTs) has proven essential in revolutionizing humanitarian logistics as standalone solutions. However, the interoperability of humanitarian drones with other DTs has not yet been explored. In this study, we performed a systematic literature review to attempt to fill this gap by evaluating 101 mathematical models collected from Scopus. After conducting a descriptive analysis to identify the trends of publications in terms of year, type, source, and country of origin, a content analysis ensued to investigate the complementarity, interoperability, and level of integration of humanitarian drones with eight DTs. Next, we researched how these DTs can help drones exploit their capabilities to their full potential and facilitate the various drone operations deployed across different disaster scenarios, types, and stages. Last, the solving approaches employed by the models were examined. Overall, we shifted our research focus toward several overlooked aspects in the literature and identified multiple challenges needing to be addressed. Our work resulted in the formulation of a holistic framework aiming to standardize the cooperative utilization of DTs during the execution of humanitarian drone operations, so as to enhance their real-life application and scalability.

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Section: Iotmentioning

confidence: 99%

Section: Area Coveragementioning

confidence: 99%

Section: Resource Allocation For Mobile Devicesmentioning

confidence: 99%

Section: Optimization Modelsmentioning

confidence: 99%

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Complementarity, Interoperability, and Level of Integration of Humanitarian Drones with Emerging Digital Technologies: A State-of-the-Art Systematic Literature Review of Mathematical Models

Aretoulaki

Ponis

Plakas

2023

Drones

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An improved parallel processing-based strawberry optimization algorithm for drone placement

et al. 2022

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It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. Strawberry optimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method, it has never been used to solve problems involving optimal drone placement. SBA is preferred for optimizing drone placement in this study due to its promising results for nonlinear, mixed, and multimodal problems. Based on the references listed below, no study has investigated the need to develop a parallelized strategy version. Several studies have been conducted on the use of drones for coverage. However, no optimization algorithms have been evaluated regarding time complexity or execution time. Despite what has been said thus far, no study has looked into the significance of a systematic framework for assessing drone coverage techniques using test suits. An optimized drone placement algorithm based on strawberry optimization is presented in the paper. The strawberry optimization algorithm will solve the drone placement problem through parallelization. In addition, the authors deploy test suits that vary in size from small to large. The dataset consists of four categories with three problems each. Results indicate that strawberry optimizers outperform Genetic algorithms (GA) and particle swarm optimization algorithms (PSO) in the number of drones, convergence, and computation time. Furthermore, the proposed approach achieves the best solution in a finite number of steps. In small-scale problems, the performance of all algorithms is convergent. As the size of the data set increases, the superiority of Strawberry optimization algorithms becomes evident. Overall, Strawberry comes out on top for eleven out of twelve comparisons.

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Rotational Flying Station for Wireless Access in Large Disaster Area

Imran,

Liu

2024

2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)

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Efficient Optimal Backhaul-aware Placement of Multiple Drone-Cells Based on Genetic Algorithm

Cited by 3 publications

References 21 publications

Complementarity, Interoperability, and Level of Integration of Humanitarian Drones with Emerging Digital Technologies: A State-of-the-Art Systematic Literature Review of Mathematical Models

Complementarity, Interoperability, and Level of Integration of Humanitarian Drones with Emerging Digital Technologies: A State-of-the-Art Systematic Literature Review of Mathematical Models

An improved parallel processing-based strawberry optimization algorithm for drone placement

Rotational Flying Station for Wireless Access in Large Disaster Area

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