An Effective UAV Scheduling Algorithm for Public Transportation-Assisted Urban Surveillance System

Unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) with a blockchain consensus algorithm is a promising solution for addressing mobile offloading of computation-intensive or latency-sensitive tasks with extensive service range, while ensuring the authenticity of the offloading. Existing UAV-aided MEC studies focus on task offloading or trajectory planning of single-antenna UAVs, missing multiple-input multiple-output (MIMO) UAV systems. In addition, most blockchain-based UAV-aided edge computing studies adopt energy-consuming proof-of-work-based consensus mechanisms, which are unsuitable for energy-limited UAV scenarios. To address the above issues, in this paper, we develop a joint deadline-aware task offloading and UAV trajectory planning scheme, utilizing the effects simulated through an energy-efficient consensus protocol. In order to prevent the exhaustion of UAVs' energy and ensure the authenticity of decision-making, we propose an energy-based Raft (E-Raft) consensus algorithm, enabling dynamic leader selection through a series of decreasing energy thresholds. Subsequently, we present a computational profit maximization problem to jointly optimize deadline-aware task offloading and the UAV swarm's trajectory planning. To address this NP-hard problem, we develop an online priority-based task offloading and trajectory selection algorithm, which is performed by the selected leader of the E-Raft consensus in each round. Simulation results demonstrate that our proposed scheme achieves up to 40% performance improvement over other approaches.

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

When solving the path planning problem for Unmanned Aerial Vehicle (UAV) in a three-dimensional complex environment, traditional algorithms often face issues like falling into local optimum easily, insufficient global search ability, poor efficiency and defective optimization result. To address these issues, a three-dimensional path planning method is proposed based on the Starfish Optimization Algorithm (SFOA). This algorithm, inspired by the exploration, preying, and regeneration behaviors of starfish, balances global search and local exploitation, enhancing UAV trajectory planning in complex environments. The study constructs a complex three-dimensional environment model and designs a comprehensive optimization objective by covering constraints like trajectory length, safety, flight height, and smoothness. The trajectory planning framework proposed in this study is designed for pre-mission planning, generating UAV paths offline based on known static terrain and threat information. Comparative experimental results with Ant Colony Optimization and Particle Swarm Optimization show that the SFOA-based UAV trajectory planning achieves significant improvements in comprehensive cost and convergence speed, demonstrating superior global optimization performance. This offers an innovative solution for UAV efficiently and safe trajectory planning in complex environments.

The integration of unmanned aerial vehicles (UAVs) and high-altitude platform (HAP) in hierarchical aerial computing presents a promising paradigm for delivering supplementary computational services to mobile IoT devices. Despite the great potential, this system faces the challenge of timescale. Specifically, the dynamic nature of IoT devices and their corresponding task generations may initialize the frequent adjustment of UAVs-HAP task delegation for fullfiling their needs in small-timescale, but UAVs' trajectory planning and replacement can only operate in large-timescale because of the severe overhead in high-speed maneuvers, motivating the multi-timescale asynchronous decision making which may further complicate the system. To this end, we propose a two-timescale optimization framework, aiming to maximize the system energy efficiency of the hierarchical aerial computing. We formulate the problem as two coupled multi-agent stochastic games, capturing UAV-HAP interactions and system uncertainties, and prove the existence of a Nash Equilibrium (NE). A reinforcement learning-based algorithm, UAV trajectory planning, replacement, task delegation and resource allocation (UTRTD), is developed to achieve the NE. Simulation results demonstrate the effectiveness and superiority of the proposed UTRTD over counterparts.

Unmanned aerial vehicle (UAV) trajectory planning in urban environments is considered. Equipped with a three- dimensional (3-D) environment map, the UAV navigates by fusing global navigation satellite systems (GNSS) signals with ambient cellular signals of opportunity. A trajectory planning approach is developed to allow the UAV to reach a target location, while constraining its position uncertainty and multipath- induced biases in cellular pseudo-ranges to be below a desired threshold. Experimental results are presented demonstrating that following the proposed trajectory yields a reduction of 30.69% and 58.86% in the position root-mean squared error and the maximum position error, respectively, compared to following the shortest trajectory between the start and target locations.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

Unmanned Aerial Vehicles (UAVs), in multi-UAV collaborative search systems, need to realize adaptive autonomous trajectory planning based on their own state and surrounding environmental conditions. To allow for a fast trajectory planning of multiple UAVs from the current state to the target state, this paper proposes a trajectory planning method for multi-UAV collaborative search tasks based on genetic algorithm (GA). And using genetic algorithm, the UAV's trajectory can be optimized online based on the current mission. Through the analysis of the tasks of the multi-UAV collaborative decision-making part and the autonomous trajectory planning part, the genetic algorithm is used to model and optimize the trajectory planning problem to obtain a feasible and efficient optimal search trajectory from the current state to the decision-making target state. Finally, through specific simulation experiments, the optimal trajectory from the current state to the target state of the UAV i in the multi-UAV system is obtained, which verifies the effectiveness of the algorithm.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs' trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

This letter jointly investigates the trajectory and radio resource optimization for multiple unmanned aerial vehicles (UAVs) to fully deliver critical data in vehicular networks during disaster situations. We aim to minimize the number of deployed UAVs to fully serve all vehicles. The formulated problem is generally NP-hard. To solve it, we employ a sequence of convex approximates. Then, we develop an efficient algorithm to sequentially solve this problem. Our numerical results demonstrate the effectiveness of our proposed design and show that during the mission time, the UAVs adapt their velocities in order to fulfill the requirement of each vehicle.

Deploying wireless routers on the ground and unmanned aerial vehicles (UAVs) in the air is believed to be a fast and efficient approach to providing the emergency communication service to disaster areas. The network lifetime is restricted to the lifetime of mesh networks of routers that are scattered across a complex disaster environment. We consider the problem of multi-UAVs relaying and moving with the goal of maximizing the network lifetime. UAVs must learn where to move in order to relay messages from the routers efficiently. However, under the dynamics of the router traffic, that is, the uncertainty of the environment, it is challenging for UAVs to find a movement mechanism that maximizes the network lifetime. By embracing the on-policy reinforcement learning algorithm Sarsa, we are able to demonstrate a greedy movement policy. Specifically, we study the trajectory planning of multiple UAVs in a dense wireless router mesh networks (WMNs) on the ground. UAVs can learn the unknown environment by a little movement of UAVs in each step, in which communication connections between routers with UAVs are retained. A Q-table of movement actions and environment states is formed after multiple attempts of movements. Simulation results show the proposal effectiveness comparing with other methods.

Terahertz (THz) communication networks, combined with Intelligent Reflecting Surfaces (IRS), hold significant potential for advancing the capabilities of Sixth-Generation (6G) wireless systems. The integration of these technologies offers opportunities for ultra-fast data transmission and substantial improvements in network capacity, meeting the growing demand for higher bandwidth and faster connectivity. This study investigates the use of flying IRS-assisted Unmanned Aerial Vehicles (UAVs) within a THz communication framework, aiming to overcome challenges in environments with complex signal propagation, such as urban settings. To facilitate this integration, we introduce the Fly-IRS algorithm, designed to optimize key aspects of network performance, including user device grouping, IRS phase shifts, and UAV positioning. These factors are critical for maximizing data transmission rates, boosting network capacity, and minimizing service disruptions that affect user experience. Our approach leverages a combination of Multi-Agent Deep Reinforcement Learning (MADRL) and Particle Swarm Optimization (PSO) to effectively handle user grouping, IRS phase shift optimization, and UAV trajectory planning. By merging the strengths of both techniques, the Fly-IRS algorithm enhances system adaptability and efficiency. Simulation results demonstrate significant improvements in data rates, with the Fly-IRS algorithm delivering up to 10% better performance compared to the MADRL method alone. These findings highlight the transformative potential of integrating THz communication networks with IRS and UAV technologies, emphasizing their ability to shape future wireless communication systems. This innovation could lead to more reliable, high-capacity networks, capable of addressing the advanced requirements of 6G, offering improved service quality even in difficult environments.

Global Satellite navigation systems are widely used for their wide coverage and low operating costs. However, due to the use of MEO, IGSO and GEO satellites, the signal power received in land is low and susceptible to interference. Therefore, in order to ensure the normal use of GNSS, it is necessary to carry out research on the detection and positioning of navigation interference sources. In view of the problems of the existing ground interference source positioning equipments, such as weak maneuverability, small coverage, and high environmental dependence, this paper presents a design of a navigation interference source positioning system based on unmanned aerial vehicle(UAV), and researches its key technologies such as UAV trajectory planning. By carrying a direction-finding load on the UAV, interference sources can be located in the air. It has the advantages of strong maneuverability, large coverage, low environmental dependence, etc. It can quickly locate the interference source and is suitable for use in mountains, jungles and cities.

Large number of Internet of Things (IoT) devices deployed on demand to monitor surroundings and offload computation-intensive tasks to edge servers, which favors low-latency IoT applications. However, some of them scatter in areas with limited or no available communication infrastructure (no-ACI), which rises a huge challenge for task offloading, due to when and which devices have tasks cannot be known in advance. In this article, a trust-based active notice task offloading (TANTO) scheme in the aerial computing system (ACS) assisted by the unmanned aerial vehicle (UAV) is proposed to provide trust and low-delay task offloading for resource-limited IoT devices in areas with no-ACI. The main innovations of TANTO in ACS are as follow: 1) a novel task offloading mechanism for IoT devices in networks with no-ACI is proposed, where devices broadcast task offloading notice, making it easy for UAV to know where tasks need to offload without aimless search, which can effectively reduce UAV’s flight distance and task completion delay at the same time; 2) a trust calculation and reasoning method is proposed to calculate network trust, and divide the network into grids with different trust values, which guides UAV through grids with higher trust to increase the task completion rate and reduce delay; and 3) an online UAV trajectory optimization algorithm is proposed to dynamically prioritize tasks with urgent deadline. A large number of experimental results show that TANTO has better performance than previous studies in terms of task completion rate, tasks’ average completion time, and UAV’s flight cost.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.