UAV Trajectory Planning for Computational Offloading and Resource Allocation Optimizing in UAV-assisted MEC with Unavailable Base Station

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions.

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 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.

In the current fifth-generation (5G) and Beyond 5G (B5G) era, the Unmanned Aerial Vehicles (UAVs) have been playing a vital role and attracting interest in different application areas in the military, and civil applications such as communications, disaster management, search and rescue, security, control, agriculture, Internet of things (IoT), etc. In these networks, ultra-heterogeneous IoT devices generate time-sensitive traffic. However, those devices have limited resources to compute tasks. Recently, Mobile Edge Computation Offloading (MECO) has been considered as an encouraging model to enable the computation tasks of IoT devices to be performed by MEC servers and support ultra-low latency IoT applications to ensure Quality of services (QoS). However, terrestrial network failure due to natural and human-made disasters has been increasing, and difficult to provide reliable computation offloading and resource allocation services to IoT networks. Nowadays, UAVs have been promising technology to quickly deploy and recover the system to provide efficient services to edge nodes. The offloading and resource allocation problems in current network technology are complex, and offloading task to edge server is vulnerable to security risks. Hence, we utilize a deep reinforcement learning method to handle a complex problem for computation offloading and resource allocations in a dynamic environment. And also, we explore a blockchain-based multi-UAV-assisted MEC architecture in securing and optimizing the offloading problems.

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV has two roles to play: MEC server or wireless relay. The UAV makes task offloading decisions online, in which the collected tasks can be executed locally on the UAV or offloaded to the BS for remote processing. The TCTO problem involves multi-objective optimization as its objectives are to minimize the task delay and the UAV's energy consumption, and maximize the number of tasks collected by the UAV, simultaneously. This problem is challenging because the three objectives conflict with each other. The existing reinforcement learning (RL) algorithms, either single-objective RLs or single-policy multi-objective RLs, cannot well address the problem since they cannot output multiple policies for various preferences (i.e. weights) across objectives in a single run. An evolutionary multi-objective RL (EMORL) algorithm is applied to address the TCTO problem. We improve the multi-task multi-objective proximal policy optimization of the original EMORL by retaining all new learning tasks in the offspring population, which can preserve promissing learning tasks. The simulation results demonstrate that the proposed algorithm can obtain more excellent non-dominated policies by striking a balance between the three objectives regarding policy quality, compared with two evolutionary algorithms, two multi-policy RL algorithms, and the original EMORL.

In recent years, the utilization of unmanned aerial vehicles (UAVs) has surged in a variety of applications, including weather monitoring, emergency search and rescue operations, and smart agriculture. In these applications, UAVs are instrumental in executing data collection tasks. However, due to the scarcity of resources such as battery capacity, the duration of work is limited, necessitating the optimization of UAV trajectories and resource allocation. In this paper, we introduce a hierarchical task scheduling and resource allocation scheme for UAV data collection tasks, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous actions, including UAV trajectories, UAV communication power, and UAV CPU main frequency, are first optimized in the lower layer, while the upper one concentrates on the discrete action space for the target allocation. In particular, the well-trained lower-layer network is integrated into the upper layer, facilitating rapid global reward feedback between the two-layer network and accelerating the training process of the upper-layer network. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the amount of collected data as well as UAV energy consumption.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

With the growing demands of mobile IoT applications, unmanned aerial vehicles (UAVs) have attracted increasing attention for their flexibility and mobility, and are being widely employed as aerial edge servers in mobile edge computing (MEC) systems to extend coverage and improve adaptability in dynamic environments. This paper jointly optimizes task offloading, resource allocation, and UAV trajectory planning in a multi-UAV-assisted MEC system, aiming to minimize total energy consumption and delay. To cope with environmental dynamics, reinforcement learning (RL) is employed. Specifically, we propose an Option mechanism based on hierarchical RL, enabling agents to leverage knowledge from peers before decisionmaking, which enhances exploration efficiency and accelerates convergence. Additionally, mean field theory is integrated with neural networks to approximate the collective influence of numerous agents, reducing interaction complexity in large-scale systems. Experimental results verify the effectiveness and scalability of the proposed framework.

Unmanned aerial vehicles (UAVs) are increasingly utilized in smart city applications, particularly for urban surveillance. UAVs can be provisioned as mobile surveillance to avoid various difficulties in ground operations and reduce extensive labor cost. However, their limited energy capacity restricts flight time and coverage, making it difficult to build a large-scale, long-term city-wide monitoring network. To address this problem, an ubiquitous public transportation network is introduced for UAVs to periodically recharge by landing on public transportation buses. We propose a novel public transportation-assisted UAV scheduling framework that leverages the existing bus network to enable recharging of UAVs. Two intertwined sub-problems are addressed: the UAV trajectory planning and the surveillance task offloading problems. The trajectory planning is modeled as a Traveling Salesman Problem (TSP) and it is solved via the Lin-Kernighan heuristic (LKH), decomposing the bus station network into sub-graphs for efficient routing. For task offloading, a time-slot-based scheduling method is proposed that dynamically assigns UAVs to monitor points of interest (PoIs) while ensuring energy constraints and full coverage. Experimental results demonstrate that the proposal outperforms baseline algorithms, achieving a 1.72%-3.46% average extension in system lifetime compared to state-of-the-art baselines, while maintaining computational efficiency (average runtime: 277.57 ms). The robustness of the proposal is further validated across diverse testing instances with various parameter settings.

<p>The vehicular ad hoc network (VANET) has emerged as a heterogeneous network with no fixed infrastructure. In VANET, vehicles are the mobile nodes that communicate with one another and with the infrastructure. One of the major challenges in VANET is to allocate resources efficiently due to environmental characteristics such as highly mobile vehicles, latency-sensitive and short connection times. The conventional resource management mechanisms are not very effective in such an environment. The data-driven Artificial Intelligence (AI) based techniques are a very efficient alternative to traditional statistical techniques. This project proposes an AI-empowered task offloading and computing resource allocation model which can dynamically organize the computing resources in VANET. The model is divided into two layers. First, the task offloading layer, where the Random Forest (RF) algorithm is used to determine whether the vehicle’s computing tasks should be offloaded to the cloud computing (CC) server or mobile edge computing (MEC) server or to be processed locally (in-vehicle computing). Second, the resource allocation layer, where the deep deterministic policy gradient (DDPG) algorithm is used to determine the computing platform again when the task is determined to be offloaded to either MEC servers or the cloud servers. To evaluate the performance of the RF classifier, we applied the model to a real-world driving trajectory dataset then compared the results with a different set of machine learning algorithms namely, K-nearest neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The results show the RF model outperformed other models in classification accuracy score of 99.83% for task offloading decision, where the KNN, MLP and SVM achieved 98%, 94.81% and 90.94%, respectively. Moreover, the DDPG based resource allocation scheme converges within 150 episodes. Thus, the proposed model can find the optimal offload decision and the computational resource allocation simultaneously and achieves a high delay to quality-of-service (QoS) satisfaction ratio.</p>

Joint optimization of trajectory and resource allocation in cellular-connected multi-UAV MEC networks

Owing to the rapid development of unmanned aerial vehicles (UAVs), UAV-enabled data collection has emerged as a promising technology. However, for the scenarios where the UAVs are dispatched to gather surrounding information actively and dynamically, the existing data collection methods can hardly fulfill the corresponding demands. To this end, we aim to study the paradigm data acquisition, where the UAV dynamically gathers information by on-board sensors. In this paper, we consider the scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time data acquisition characteristics, we construct a novel data acquisition rate model, which is with respect to the UAV speed. Next, we formulate a UAV energy consumption minimization problem that jointly considers UAV trajectory, transmission power, and CPU frequency. To tackle the highly complex problem, we propose an efficient iterative algorithm, and rigorously derive a closed-form solution for the UAV transmission power and computation resources allocation. With the obtained numerical results, we further validate the superiority of the proposed system design and the effectiveness of our algorithm against benchmark schemes.

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.