A Review of Vehicular Micro-Clouds

Phadke, Abhishek; Medrano, F. Antonio; Ustymenko, Stanislav

doi:10.1109/csci54926.2021.00139

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Deployment strategies [62] play a critical role in the effective utilization of UAV swarms over an ROI (Region of Interest). The choice of strategy depends on various factors, including the mission objectives, environmental conditions, available resources, and desired outcomes [63]. One commonly employed strategy is the grid-based deployment approach, where the ROI is divided into a grid pattern and UAVs are strategically positioned at predefined grid points.…”

Section: Deployment Strategiesmentioning

confidence: 99%

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

Phadke,

Medrano,

Sekharan

et al. 2023

Sensors

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The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.

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Section: Deployment Strategiesmentioning

confidence: 99%

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

Phadke,

Medrano,

Sekharan

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Studies show that prolonged use of UAVs, particularly for drones as a service (DAS), has been in limited deployment due to factors such as limited flight time, collision avoidance, and onboard hardware capacity. Examples include the use of drones to provide emergency communications or vehicular micro-cloud services [55]. Modified network systems are needed to improve agent performance, and this study proposes a swarming strategy of agents based on continuously monitoring swarm geometry and the environment.…”

Section: Network Structure and Characteristicsmentioning

confidence: 99%

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

Phadke

Medrano

2022

Drones

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UAVs have rapidly become prevalent in applications related to surveillance, military operations, and disaster relief. Their low cost, operational flexibility, and unmanned capabilities make them ideal for accomplishing tasks in areas deemed dangerous for humans to enter. They can also accomplish previous high-cost and labor-intensive tasks, such as land surveying, in a faster and cheaper manner. Researchers studying UAV applications have realized that a swarm of UAVs working collaboratively on tasks can achieve better results. The dynamic work environment of UAVs makes controlling the vehicles a challenge. This is magnified by using multiple agents in a swarm. Resiliency is a broad concept that effectively defines how well a system handles disruptions in its normal functioning. The task of building resilient swarms has been attempted by researchers for the past decade. However, research on current trends shows gaps in swarm designs that make evaluating the resiliency of such swarms less than ideal. The authors believe that a complete well-defined system built from the ground up is the solution. This survey evaluates existing literature on resilient multi-UAV systems and lays down the groundwork for how best to develop a truly resilient system.

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“…With the growth of consumer services and the progress of science and technology, the digitalization level of the automotive industry is becoming increasingly high. Multimedia, assisted driving, and other intelligent options are gradually becoming the new normal of automotive technology [1]. The Internet of Vehicles is the application of the Internet of Things in the field of intelligent transportation, and it is also an important component of intelligent transportation systems [2,3].…”

Section: Introductionmentioning

confidence: 99%

Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm

Zhang,

Tuo

2023

Electronics

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With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints; secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem; finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption.

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A Review of Vehicular Micro-Clouds

Cited by 3 publications

References 28 publications

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm

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