UAV-Assisted Content Delivery in Intelligent Transportation Systems-Joint Trajectory Planning and Cache Management

Al-Hilo, Ahmed; Samir, Moataz; Assi, Chadi; Sharafeddine, Sanaa; Ebrahimi, Dariush

doi:10.1109/tits.2020.3020220

Cited by 92 publications

(34 citation statements)

References 37 publications

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“…Linear Discriminant Analysis (LDA) [123] Quadratic Discriminant Analysis (QDA) [123] Gaussian Mixture Model (GMM) [153,190-195] [196-198] Autoencoder [199] Fuzzy Logic [200] [117,201,202] [ [203][204][205][206][207] Logistic Regression [131,150] [126,208] Bayesian Network [30,[209][210][211] Multi-Layer Perceptron (MLP) [125,163,251] Graph-Theoretic Clustering [253] Hidden Markov Model (HMM) [254] Markov Decision Process (MDP) [78,235,240,255] Not referenced [256,257] [258]…”

Section: Categorisation Insightsmentioning

confidence: 99%

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

et al. 2022

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Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

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Section: Categorisation Insightsmentioning

confidence: 99%

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

et al. 2022

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“…Improved UAV-aided network operation in the Internet of Vehicles is the topic of [171]. Due to the increased mobility and dynamic content requests, content delivery becomes challenging.…”

Section: B Energy Efficiencymentioning

confidence: 99%

“…through VFA [165]. Other works have developed joint content placement and trajectory design solutions [171], [172] through DRL with promising performance. However, currently, hybrid learningbased solutions, combining offline training using historical data and online RL-aided operation for faster convergence and UAV deployment are missing.…”

Section: Volume 10 2022mentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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“…Unmanned Aerial Vehicles (UAVs), due to their great versatility, have been recently used for tasks such as smart farming [ 1 ], surveillance [ 2 , 3 , 4 ], wildfire control [ 5 , 6 ], delivery [ 7 , 8 , 9 ], and natural disaster situations [ 10 , 11 ] among others. UAVs are mainly classified into two categories: fixed-wing and rotary wing.…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Coverage Path Planning in Urban Environments

Muñoz

López

Quevedo

et al. 2021

Sensors

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Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

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UAV-Assisted Content Delivery in Intelligent Transportation Systems-Joint Trajectory Planning and Cache Management

Cited by 92 publications

References 37 publications

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Multi UAV Coverage Path Planning in Urban Environments

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