关键词： Mobile edge computing   Computation offloading   Edge caching   Content   Latency  

摘要： Edge caching is an effective paradigm that can significantly reduce the computation task offloading latency for mobile edge computing (MEC) in vehicular networks, while also alleviating the backhaul transmission pressure for retrieving content data from the cloud server. However, most existing works fail to address how to handle heterogeneous tasks generated by vehicle terminals (VTs), especially in complex scenarios where both computation and content tasks are generated simultaneously. In this paper, we consider a mobility-aware vehicular network model where VTs simultaneously generate heterogeneous task requests, i.e., a computation task and a content task, and investigate joint optimization of caching for heterogeneous tasks data, computation offloading, and computing resource allocation. In order to optimize the latency for processing the heterogeneous tasks, an average execution latency minimization problem with sojourn time and caching capacity constraints is formulated. We decompose this problem into two tractable subproblems, i.e., caching optimization subproblem, and computation offloading and resource allocation optimization subproblem. We first develop a dynamic programming (DP) algorithm to obtain the optimal caching strategies for heterogeneous tasks data. We compare the obtained content retrieval latency with the local computing latency, and derive the optimal computation offloading and edge computing resource allocation solutions. On this basis, we propose a joint computation offloading and resource allocation (JCORA) algorithm to determine the computing resources allocated to each VT and corresponding computation offloading strategy. Numerical results indicate that the proposed algorithm, which integrates DP algorithm and JCORA algorithm, can achieve lower execution latency for heterogeneous tasks compared to the benchmark schemes. Additionally, for task loss scenarios where the sojourn time constraint cannot be met, the impact of VT mobility on the

关键词： UAV   mobile edge computing   trajectory optimization   resource scheduling   robust optimization  

摘要： Mobile edge computing (MEC) deployed in unmanned aerial vehicles (UAVs) has shown special strength by enhancing computational capacity and prolonging the battery lives of terrestrial user equipment (UE). Nevertheless, current research lacks studies of robust offloading scheme scheduling and trajectory planning using terrestrial random channels. The state-of-the-art joint task-offloading and trajectory-planning optimization techniques for UAV-mounted MEC are focused on scenarios where only air-ground channels exist rather than time-varying terrestrial channels. By contrast, this paper considers the scenario where both the time-varying/random terrestrial channels and the line-of-sight air-ground channels occur. Aiming at robust resource scheduling for energy-efficient UAV-assisted MEC, we formulate a novel joint optimization of UAV trajectory planning and task offloading, which, however, is highly nonconvex. As a countermeasure, the original optimization is recast as subproblems related to task offloading and trajectory planning and solved by a novel robust iterative optimization algorithm that combines the methods of weighted minimum mean square error, S-procedure, successive convex approximation, etc. Numerical results indicate that, compared to various baselines, the proposed algorithm can effectively reduce energy consumption and optimize the trajectory in the presence of a large number of input tasks. In addition, in terms of stability and effectiveness, the proposed robust iterative optimization algorithm can reduce energy consumption more stably in time-varying/random channels compared to non-robust schemes.

关键词： Gaussian global seagull algorithm   resource optimization method   UAV   5G   mobile edge computing   emergency support  

摘要： In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system's energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system's energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.

关键词： Task analysis   Servers   Games   Computational modeling   Resource management   Heuristic algorithms   Load modeling   Game theory   load balance   mean field game   mobile edge computing   multi-agent reinforcement learning  

摘要： Smart devices (SDs) used in the Industrial Internet of Things can generate computational tasks for processing the data generated during production. However, due to the limited processing power of SDs, it is necessary to transfer these computational tasks to more powerful devices for processing. To this end, we propose a Mobile Edge Computing (MEC) system based on a Software Defined Network (SDN) for SDs to offload their computational tasks. This MEC system includes multiple MEC servers to handle numerous SDs, which leads to load-balancing challenges among these servers. To tackle this problem, we develop a computational offloading model based on mean-field game theory and introduce a mean-field game-based load-balancing algorithm (MFGLB), which reduces processing latency and facilitates task scheduling through Multi-Agent Deep Reinforcement Learning. Each SD in the MEC system is considered a participant in the mean-field game, simplifying the complex stochastic game into a more manageable dual-agent game. We then prove the existence of Nash Equilibrium for this mean-field game. To evaluate the effectiveness of our MFGLB algorithm, we compare its performance with traditional load-balancing algorithms and a stochastic game-based load-balancing algorithm. Our experimental results demonstrate the superiority of MFGLB in reducing processing latency and addressing load imbalances.

关键词： mobile edge computing   cloud-edge-terminal collaboration network   computation offloading   deep reinforcement learning  

摘要： This paper investigates the application of Intelligent Reflecting Surfaces (RIS) in 5G wireless communications, focusing on enhancing the performance of Internet of Things (IoT) networks. The rapid growth of IoT applications has exacerbated challenges in traditional wireless communication, including signal interference and limited bandwidth. To overcome these issues, we propose an innovative optimization approach utilizing deep learning algorithms to dynamically adjust the configuration of RIS. This approach significantly improves signal quality and network throughput. Our experimental results demonstrate the effectiveness of the proposed solution in enhancing communication performance under various real-world scenarios. The findings provide valuable insights into the practical deployment of RIS in 5G IoT networks, offering a promising solution to address key performance bottlenecks.

关键词： Bilevel optimization   mobile edge computing   personalized service customization   resource allocation   task offloading  

摘要： This article addresses the pricing scheme in a wireless-powered cooperative mobile edge computing (WP-CoMEC) system, focusing on personalized service customization. Traditional pricing schemes in such systems often assume a passive mode, with the service provider leading, and the device owner following. However, with the rise of personalized requirements, this paper proposes a novel approach where the device owner becomes an active participant in the pricing scheme, leading to personally customized services. The proposed pricing model formulates a bilevel multi-objective optimization problem, considering task offloading, resource allocation, and energy harvesting. This comprehensive approach ensures a more holistic optimization process. To address the computational challenges posed by the bilevel pricing model, this article proposes a bilevel periodically interactive evolutionary algorithm (BL-PIEA), which efficiently handles mixed variables, complex objective conflicts, and the inner nested structure of the bilevel pricing model. The proposed BL-PIEA is tested on ten instances, and the results indicate that BL-PIEA can effectively solve the proposed pricing model, showcasing superior performance in terms of reduced run time and saved evaluation budgets compared to other algorithms. With the proposed bilevel pricing model solved by BL-PIEA, the service provider can make out better pricing schemes that satisfy the device owner's requirements, so as to achieve a good personalized service customization.

关键词： Mobile edge computing   Satellite network   Multi-objective joint optimization   Task offloading   Multi-agent DRL  

摘要： The Internet of Things (IoT) integrates a large number of heterogeneous terminals and systems, possessing ubiquitous sensing and computing capabilities. Satellite networks are the crucial supplement to terrestrial networks, particularly in remote areas where network infrastructures are sparingly distributed or unavailable. Combining edge computing with satellite networks provides on-orbit computing capabilities for IoT applications, reducing service delay and enhancing service quality. Due to the resource constraints of satellites, achieving collaborative services through task offloading among multiple satellites becomes essential. Both the privacy leakage risk arising from frequent data interactions and the load imbalance resulting from offloading preferences cannot be overlooked. The key challenge of task offloading is to safeguard the privacy of offloaded data and ensure the system's load balance while minimizing the delay and energy consumption. In this paper, the task offloading problem is formulated as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on multi-objective joint optimization using multi-agent deep reinforcement learning in a distributed architecture is proposed. The simulation results validate the efficacy of our model and algorithm, demonstrating that our proposed algorithm achieves better performance in minimizing comprehensive offloading costs.

关键词： Mobile edge computing   Cloud computing   Internet of things   Task offloading   African vultures optimization algorithm  

摘要： Mobile Cloud-Edge Collaboration (MCEC) views in the main of converting the site for user electronics. By naturally integrating mobile devices with cloud computing (CC) resources at the edge of the scheme, this mutual paradigm improves storage, processing, and communication capabilities. This cooperation increases the performance of user electronics, delivering users responsive and resource-efficient knowledge. Offloading in Mobile Cloud-Edge Collaboration (MCEC) is a strategic device that recovers computational efficiency and resource energy for mobile devices. By reasonably moving computation tasks from mobile devices to the edge or cloud servers, offloading declines the load on the limited processing and energy capabilities of mobile devices. This joint method influences the stable computing power and storage aptitude accessible in the cloud-edge structure, confirming that resource-intensive uses like complex data processing or machine learning (ML) tasks can be implemented professionally. Offloading not only increases the receptiveness and performance of mobile users but also contributes to energy conservation, extending the battery time of mobile devices. This study proposes an African Vultures Optimizer algorithm-based Offloading Strategy for Mobile Cloud-Edge Collaboration (AVOAOS-MCEC) approach for consumer electronics. The AVOAOS-MCEC technique is based on the nature of AVOA is a new nature-based system, which is inspired by the unusual behavior of African vultures in foraging and navigation. In addition, the AVOAOS-MCEC technique designs a task offloading process to reduce the total energy utilization with the fulfillment of capacity and delay requirements. The experimental validation of the AVOAOS-MCEC method is verified utilizing distinct measures. An extensive comparison study stated that the AVOAOS-MCEC technique outperforms the other models in terms of several performance measures.

关键词： mobile edge computing   computation offloading   unmanned aerial vehicle   post-disaster rescue   multi-agent reinforcement learning  

摘要： Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading. CER-MADDPG emphasizes collaboration between UAVs and uses historical UAV experiences to classify and obtain optimal strategies. It enables collaboration among edge devices through the design of the 'critic' network. Additionally, by defining good and bad experiences for UAVs, experiences are classified into two separate buffers, allowing UAVs to learn from them, seek benefits, avoid harm, and reduce system overhead. The performance of CER-MADDPG was verified through simulations in two aspects. First, the influence of key hyperparameters on performance was examined, and the optimal values were determined. Second, CER-MADDPG was compared with other baseline algorithms. The results show that compared with MADDPG and stochastic game-based resource allocation with prioritized experience replay, CER-MADDPG achieves the lowest system overhead and superior stability and scalability.

关键词： maximize profit   mobile edge computing   multi-agent algorithm   network slicing   online resource allocation   secure admission control  

摘要： The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end-users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL-based approach outperforms other state-of-the-art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively. We propose a multi-agent deep reinforcement learning mechanism for end-to-end network slicing in mobile edge computing. Utilizing the advantage actor-critic method, it predicts future user arrivals, ensures resource availability, and optimizes resource al