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

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship’s trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends. 

A Review of Path Planning for Unmanned Surface Vehicles

In recent years, multi-agent reinforcement learning techniques have been widely used and evolved in the field of artificial intelligence. However, traditional reinforcement learning methods have limitations such as long training time, large sample data requirements, and highly delayed rewards. Therefore, this paper systematically and specifically studies the MARL algorithm. Firstly, this paper uses Citespace software to visually analyze the existing literature on multi-agent reinforcement learning and briefly indicates the research hotspots and key research directions in this field. Secondly, the applications of traditional reinforcement learning algorithms under two task objects, namely single-agent and multi-agent systems, are described in detail. Then, the paper highlights the diverse applications, challenges, and corresponding solutions of MARL algorithmic techniques in the field of MAS. Finally, the paper points out future research directions based on the existing limitations of the algorithm. Through this paper, readers will gain a systematic and in-depth understanding of MARL algorithms and how they can be utilized to better address the various challenges posed by MAS. 

A review of research on reinforcement learning algorithms for multi-agents

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

With the development of Internet of Things (IoT) networks and Mobile Edge Computing (MEC), many computing-intensive applications have been developed in large quantities. Due to the heterogeneity of tasks, different application services are required to perform each task. Caching application services and related data in edge servers is challenging. Hence, we study the service cache placement and task offloading problem in IoT networks. Since IoT devices and edge servers with limited storage resources can only cache a few services at the same time, we formulate the service cache placement and task offloading of IoT devices problem to minimize task service delay with long-term energy constraint of IoT devices, which is a mixed integer nonlinear programming problem. To solve this problem, an online Deep Reinforcement Learning guided by the Lyapunov optimization framework algorithm (LYADRL) is proposed. We first build a virtual queue model to decouple the problem by Lyapunov optimization technique to transform the problem into a single time slot optimization problem. Then, we use Deep Reinforcement Learning techniques to find the optimal edge service caching and task offloading policies for each time slot. Simulation results show that our algorithm can reduce the service delay compared with other benchmark algorithms.

Service Caching and Task Offloading of Internet of Things Devices Guided by Lyapunov Optimization

The sampling rate is too high to be accomplished for channel estimation in Ultra Wideband (UWB) Systems. Due to the sparse structure of UWB channels, compressive sensing (CS) is suitable for UWB channel estimation. Capitalizing on the sparseness of random UWB channels in the basis of eigen functions, eigen-dictionary has been adopted. While the contribution of every atoms to the reconstruction of the channels is different, we develop a weighted eigen-dictionary. Combining with Bayesian compressive sensing (BCS), the proposed Ultra Wideband channel estimation with weighted eigen dictionary could improve the estimation performance with lower sampling rate.

Ultra Wideband Channel Estimation Based on Adaptive Bayesian Compressive Sensing with Weighted Eigen Dictionary

The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost consumption under the maximum delay constraint by jointly considering the positions, speeds and computation resources of vehicles. Due to the complexity of the problem, we propose the vehicle deep Q-network (V-DQN) algorithm. In V-DQN algorithm, we firstly propose a vehicle adaptive feedback (VAF) algorithm to obtain the priority setting of processing tasks for service vehicles. Then, the V-DQN algorithm is implemented based on the result of VAF to realize task offloading strategy. Specially, the interruption problem caused by the movement of the vehicle is formulated as a return function as part of evaluating the task offloading strategy. The simulation results show that our proposed scheme significantly reduces cost consumption and improves Quality of Service (QoS).

Dynamic Vehicle Aware Task Offloading Based on Reinforcement Learning in a Vehicular Edge Computing Network

Mobile augmented reality (MAR) applications are gaining popularity. There are significant challenges in handling the latency-sensitive and computation-intensive tasks generated by MAR applications on mobile devices. The emergence of mobile edge computing (MEC) provides a new idea to improve the computing capability of resource-constrained mobile terminals. In this paper, we study the task offloading and cache placement of MAR tasks in multi-MEC server cooperation system. The problem of the task offloading and cache placement is formulated to maximize the hit ratio and minimize the service latency under the constraints of MEC server's computing resources and cache space. To solve this problem, we propose a task offloading and cache placement (MOTOCP) algorithm based on multi-objective artificial bee colony. Pareto optimal relation is introduced in the optimization process to find the optimal solution. Extensive evaluation verifies that our proposed algorithm has better performance.

Collaborative Content Caching and Task Offloading in Multi-Access Edge Computing

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