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

With the development of industrialization and intelligence, the Industrial Internet of Things (IIoT) has gradually become the direction for traditional industries to transform into modern ones. In order to adapt to the emergence of a large number of edge access devices such as sensors, as well as the demand for high-consumption and low-latency computing tasks, Mobile Edge Computing (MEC) has been proposed as an effective paradigm by the academic community. Users can offload tasks to MEC servers, greatly reducing the computing latency and energy consumption when using services. However, traditional single access point edge computing networks cannot meet the usage requirements of a large number of users. In addition, the privacy leakage issues arising from the offloading process are also easily overlooked. In this paper, we propose a privacy-preserving offloading scheme based on stochastic game theory considering multiple access points. We first construct a multi-access point offloading framework and quality of service (QoS) model. In terms of privacy, the privacy risks caused by the offloading preferences of different edge nodes are studied, and the privacy entropy is used to evaluate the privacy protection level. We comprehensively consider the energy consumption, latency requirements, user experience, and privacy protection of the system and formulate the problem as a Markov Decision Process (MDP). Finally, a joint optimal DRL algorithm with privacy preservation (JODRL-PP) is proposed to achieve the optimal offloading scheme of the system. Simulation results verify the effectiveness of our model and algorithm. 

Privacy-Preserving Offloading Scheme in Multi-access Mobile Edge Computing Based on MADRL

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

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

In recent years, edge computing has made up for the shortcomings of cloud computing’s centralized data processing. It migrates computation to edge devices close to users, which reduces the user’s transmission time, calculation time, propagation time, and other times, so it meets the request of delay-sensitive tasks. In this multi-access edge computing system, edge devices are divided into different cooperation spaces. Edge devices in the same cooperation space collaborate with others through sharing resources. Tasks are divided into multiple computations, each of which can be executed on different edge devices. A task offloading problem is formulated to minimize the average delay of all tasks in multi-access edge computing system. An algorithm based on ant colony optimization is proposed in order to find the best solution for task offloading. To make better decisions in the first iteration, the pheromone matrix is initialized considering two factors of base station load and distance between users and base stations. According to the relationship between fitness function and the global optimal value or local optimal value, the values of pheromones are updated dynamically. A large number of experiments show that our algorithm has better performance. • A multi-server, multi-user edge computing system is considered. • Our goal is to minimize the average delay. • For BS selection, we consider the two factors of base station load and distance. • Task Offloading combine Ant Colony Optimization Algorithm is designed. 

Delay-sensitive tasks offloading in multi-access edge computing

Deployment and Exploration of a Gas Storage Well Pattern Based on the Threshold Radius

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

Delay-sensitive tasks offloading in multi-access edge computing

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

Deployment and Exploration of a Gas Storage Well Pattern Based on the Threshold Radius