Applications of Artificial Intelligence and Machine learning in smart cities

In recent years, unmanned aerial vehicles (UAVs) have attained significant interest in different applications including aerial surveillance, providing wireless coverage, precision agriculture, power lines & oil rigs monitoring and construction, etc. The UAVs implicit peculiarities, e.g., swift mobility, increase in payload capabilities and airborne time, place it as a potential candidate for many applications in next-generation wireless communications. In this article, a comprehensive study on UAVs challenges, potential applications, and regulations is presented. In particular, fundamental challenges such as UAVs standardization, channel modeling, interference mitigation, collision avoidance and optimal trajectory design using deep reinforcement learning (RL) algorithms, energy harvesting techniques, security, and regulations are explored in light of most recent research development. Moreover, we propose a set of UAVs regulations to ensure its steady social integration and new business opportunities. Finally, various problems and future research directions regarding UAVs are presented.

Cognition in UAV-Aided 5G and Beyond Communications: A Survey

A survey of UAV-based data collection: Challenges, solutions and future perspectives

Over the past decade, Unmanned Aerial Vehicles (UAVs) have provided pervasive, efficient, and cost-effective solutions for data collection and communications.Their excellent mobility, flexibility, and fast deployment enable UAVs to be extensively utilized in agriculture, medical, rescue missions, smart cities, and intelligent transportation systems.Machine learning (ML) has been increasingly demonstrating its capability of improving the automation and operation precision of UAVs and many UAV-assisted applications, such as communications, sensing, and data collection.The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy.This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps.We emphasize the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation.We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses.This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications.It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications.While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation.It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before the full automation of UAVs and potential cooperation between UAVs and humans come to fruition. 

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

In recent years, multi-access edge computing (MEC) has become a hot topic. With its distributed characteristics, MEC provides more possibilities for delay-sensitive tasks. In this paper, we study a task offloading problem to shorten task delay. The problem consists of two aspects, bandwidth allocation and task offloading decision-making. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Game participants are all users. We take into account the dissatisfaction of each alliance and find the least dissatisfaction of the alliance. Then, we formulate the task offloading decision-making to minimize task delay. Task delay consists of communication delay and execution delay. Computing and storage capacity are treated as limiting conditions for decision-making. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm. 

Joint bandwidth allocation and task offloading in multi-access edge computing

Path planning of multi-UAVs based on deep Q-network for energy-efficient data collection in UAVs-assisted IoT

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the targe...

https://journals.sagepub.com/doi/pdf/10.1177/1550147719865882

Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks:

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Path planning of multi-UAVs based on deep Q-network for energy-efficient data collection in UAVs-assisted IoT

Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks: