Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks

Computationally intensive and latency sensitive applications put forward strict requirements on mobile user equipments (UEs) to provide the required computation capacity and delay. Mobile edge computing (MEC) in ultra-dense network (UDN) is considered as a promising solution that can reduce the computation limitations of UEs and extend their battery life by computation offloading. But the dense deployment of UEs, and the limited resources of small cell base stations (SBSs), which imposes more delays on computation offloading in MEC environment. In this paper, using the characteristics of UDN, we study the computation offloading management scheme based on the weighted sum of task completion time and energy consumption in MEC system. Then, we propose a computation offloading framework and formulate joint task offloading and resource allocation problem in UDNs to minimize overall cost consumption. Based on Genetic algorithm (GA) and Whale optimization algorithm (WOA), we adopt a heuristic algorithm named as GAWOA to solve this problem. Numerical results show that the algorithm has a fast convergence speed and better performance than other benchmark algorithms.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

Re-establishing emergency communication and ensuring rapid response are critical for rescue operations in natural disaster scenarios, such as earthquakes, floods, and wildfires. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks have emerged as a promising solution to re-establish communication links and provide flexible computational support in these complex environments. However, the existing UAV-assisted MEC research has not fully investigated the joint optimization of task offloading and resource allocation (JTORA) problem, considering both terrain obstacles and high-interference zones. In this paper, we investigate the JTORA problem to minimize the energy consumption for communication and computation in a rate-splitting multiple access (RSMA)-based UAV-assisted mobile edge computing (MEC) network. RSMA is utilized to enhance interference management and improve spectral efficiency. We propose a proximal policy optimization (PPO)-based method to optimize the task offloading ratio, message splitting ratio, and RSMA precoding matrix for the proposed JTORA problem. Simulation results show that the proposed approach effectively enhances system efficiency and sustainability.

The rapid growth of Industrial Internet of Things (IIoT) terminals has resulted in tasks exhibiting increased concurrency, heterogeneous resource demands, and dynamic priorities, significantly increasing the complexity of task scheduling in edge computing. Cloud-edge-end collaborative computing leverages cross-layer task offloading to alleviate edge node resource contention and improve task scheduling efficiency. However, existing methods generally neglect the joint optimization of task offloading, resource allocation, and priority adaptation, making it difficult to balance task execution and resource utilization under resource-constrained and competitive conditions. To address this, this paper proposes a two-stage dynamic-priority-aware joint task offloading and resource allocation method (DPTORA). In the first stage, an improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm integrated with a Priority-Gated Attention Module (PGAM) enhances the robustness and accuracy of offloading strategies under dynamic priorities; in the second stage, the resource allocation problem is formulated as a single-objective convex optimization task and solved globally using the Lagrangian dual method. Simulation results show that DPTORA significantly outperforms existing multi-agent reinforcement learning baselines in terms of task latency, energy consumption, and the task completion rate.

The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user’s task execution needs. A comparison with the latest algorithm also verifies the model’s performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.

In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

For the problems of unreasonable computation offloading and uneven resource allocation in Mobile Edge Computing (MEC), this paper proposes a task offloading and resource allocation strategy based on deep learning for MEC. Firstly, in the multiuser multiserver MEC environment, a new objective function is designed by combining calculation model and communication model in the system, which can shorten the completion time of all computing tasks and minimize the energy consumption of all terminal devices under delay constraints. Then, based on the multiagent reinforcement learning system, system benefits and resource consumption are designed as rewards and losses in deep reinforcement learning. Dueling-DQN algorithm is used to solve the system problem model for obtaining resource allocation method with the highest reward. Finally, the experimental results show that when the learning rate is 0.001 and discount factor is 0.90, the performance of proposed strategy is the best. Furthermore, the proportions of reducing energy consumption and shortening completion time are 52.18% and 34.72%, respectively, which are better than other comparison strategies in terms of calculation amount and energy saving.

Mobile Edge Computing (MEC) is a promising computing paradigm, which can provide mobile devices with powerful computing capability and low delay by offloading resource-intensive tasks to the MEC Server. However, due to the varying wireless channel conditions and different task delay requirements, the resource allocation decision made by MEC Server cannot always achieve the lowest system energy consumption. In this paper, we consider resource allocation problem in a multi-user MEC system. The objective is to achieve minimum system energy consumption under delay constraint by suitably allocating communication and computing resources. We solve the communication resources (subcarrier) allocation problem through regression algorithm, make use of three regression algorithms to predict subcarrier allocation. And then calculate the computing resources (CPU frequency) allocation and system energy consumption. Simulation results show that machine learning can achieve near-optimal system energy consumption, where the system energy consumption of the ANN(Artificial Neural Network), Xgboost (Extreme Gradient Boosting), and RF(Random Forest) algorithms is 1.30%, 7.49%, and 23.29% higher than the optimal system energy consumption respectively. In the view of prediction loss, ANN and Xgboost perform better than RF.

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), the diverse IoT applications with requirements of ultra-low latency and heterogeneous connectivity have been increasing sharply in recent years. In this work, we consider the problem of diverse task offloading and computation resource allocation in the dynamic and heterogeneous MEC-enable network, wherein heterogeneous characterized by the MEC servers with different computing capacities and multi-type applications with different computation requirements. Firstly, to drive the long-term system-wide effectiveness towards a near-optimum, joint task offloading and computation resource allocation is proposed by invoking the Lyapunov optimization theory, and we analyze the tradeoff between task offloading revenue and latency. Besides, a heuristic task offloading methodology based on a search tree is developed to get the optimal offloading strategy in a heterogeneous environment. Moreover, to improve processing efficiency and reduce unnecessary communication overhead, the Offloading Priority Selection Criterion (OPSC) is designed. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulations.

Joint task offloading and data caching in mobile edge computing networks

In modern mobile edge computing (MEC) systems, effectively integrating service caching, task offloading, and service delivery is crucial for ensuring high network quality and enhanced user experience. In this article, we introduce a novel joint optimization framework that employs Uncrewed aerial vehicles (UAVs) as dynamic edge computing nodes, collaborating with service collection units (SCUs) that aggregate service requests from Internet of Things (IoT) devices. In the proposed system, UAVs equipped with small-capacity edge servers can process computational tasks locally or forward them to a base station (BS) via the backhaul when necessary. Both user layers (UEs) and UAVs dynamically cache programs using an adaptive algorithm based on task frequency and program weight, which maximizes cache hit rates and expedites service execution. A joint optimization problem is formulated to minimize the overall end-to-end latency through coordinated task offloading, CPU allocation, caching, and UAV scheduling, subject to energy, storage, and mobility constraints. Our resource-aware task offloading and UAV trajectory planning further enable efficient service responsiveness and minimize reliance on the backhaul link. The overall objective is to reduce maximum task latency and backhaul congestion across the network. Extensive simulation results show that our framework significantly outperforms standard baselines, including random caching, most caching, and calculation delay and energy consumption multiobjective optimization problem (CDECMOP), achieving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.5\%$</tex-math> </inline-formula> lower energy consumption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$23.6\%$</tex-math> </inline-formula> lower latency across diverse network scenarios. These findings underscore the effectiveness of the proposed UE-UAV cooperative caching and computation strategy for scalable, energy-efficient, and low-latency UAV-assisted MEC systems.

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.

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, significantly outperforming benchmark strategies.

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

As the most important part of a smart city and long-standing challenging issue, a highly efficient smart logistic system has attracted a great deal of attention in recent years. In particular, unmanned aerial vehicles (UAVs) are ideal solutions for last-mile delivery scenarios in recent years due to their fast speed and easy deployment. However, because of the highly automatic delivery process, UAVs are still constrained by the limited payload, battery, and computing capacity for complex computational tasks. With the aid of the mobile edge computing (MEC) technology, UAVs can offload computational tasks to the MEC computational resources in various types of IoT environments. In spite of the task offloading which can enhance their task process capability, it also brings extra overhead, such as data transfer time and energy consumption. These extra overheads may significantly impact the efficiency and payload of UAV-based delivery systems. Therefore, taking the UAV last-mile delivery system with MEC as an example, this article investigates the energy-aware multi-UAV task computation management problem according to a realistic autonomous delivery network (ADNET). Specifically, we propose a computation management strategy, namely, the MEC-based task offloading and scheduling strategy (TOSS), to provide an integral approach covering both the static task offloading and scheduling algorithm, as well as the dynamic resource conflict resolution algorithm. Grounded on real-world scenarios, our experimental results show that TOSS can achieve a higher payload for UAVs by using minimum energy consumption and task makespan within the given constraints of the deadline compared to the state-of-the-art methods.