关键词： Mobile edge computing   Computation offloading   Deep learning   Parallel task offloading   Mobile devices  

摘要： The growing demand for real-time computing applications on mobile devices is burdening their processing power and battery life. Mobile edge computing helps by allowing these tasks to be offloaded to nearby servers having more processing power. However, when it comes to multiple servers and tasks, choosing the optimal components for offloading becomes challenging. This is because we need to balance between reducing the amount of data transferred and keeping communication latency low. To address this problem, an energy- efficient parallel computation offloading mechanism through deep learning (EPCOD), is proposed. An algorithm using deep learning (DL) is developed and trained as a decision-making system. This system selects the best combination of application components taking into account various factors, such as energy consumption, network conditions, computational load, data transfer volume, and communication latency. A cost function that includes all these factors is developed to calculate the cost for each possible offloading policy combination. By analyzing a large dataset, we find the best policies. Additionally, we use a DL network to efficiently handle this computational task. Simulation results demonstrate that EPCOD effectively minimizes both latency and energy consumption, achieving a high accuracy of deep neural network of up to 73.5%.

关键词： mobile edge computing   heterogeneous network   task offloading   queueing theory   stochastic geometry  

摘要： Mobile edge computing architecture (MEC) can provide users with low latency services by integrating computing, storage and processing capabilities near users and data sources. As such, there has been intense interest in this topic, especially in single-server and homogeneous multi-server scenarios. The impact of network heterogeneity and load fluctuations is ignored, and the performance evaluation system relies too much on statistical mean indicators, ignoring the impact of real-time indicators. In this paper, we propose a new heterogeneous edge computing network architecture composed of multi-core servers with varying transmission power, computing capabilities and waiting queue length. Since it is necessary to evaluate and analyze the service performance of MEC to guarantee Quality of Service (QoS), we design some indicators by solving the probability distribution function of response time, such as average task offloading delay, immediate service probability and blocking probability. By analyzing the impact of bias factors and network parameters associated with MEC servers on network performance, we provide insights for MEC design, deployment and optimization.

关键词： Mobile edge computing  

摘要： With the rapid development of building information modeling (BIM) technology, its application in construction projects has become widespread, particularly in the collaborative work during the design, construction, and operation phases. However, traditional BIM models, due to their large data volume and complex structure, present significant challenges in data transmission and real-time processing during cross-regional collaboration. Meanwhile, mobile edge computing (MEC), as an emerging computational paradigm, reduces data transmission latency and enhances processing efficiency by deploying computing and storage resources at the network edge. The integration of a BIM lightweight engine and MEC offers a more efficient solution for cross-regional cost collaboration. While previous studies have explored the combination of BIM with cloud and edge computing, most have focused on single-region scenarios, with the lightweight methods for BIM data and MEC deployment for cross-regional collaboration still lacking efficiency and practicality. Furthermore, existing optimization algorithms often fail to fully address the unique demands of cross-regional collaboration, leading to suboptimal overall performance. To address these issues, a cross-regional cost collaborative optimization model based on the BIM lightweight engine and MEC was proposed in this study. The main content of this study includes a) the design of a BIM lightweight engine based on point cloud compression, which improves data transmission efficiency through efficient compression techniques;b) the investigation of the MEC server deployment strategy in cross-regional cost collaborative scenarios, optimizing the distribution and scheduling of computational resources;and c) the development of a cross-regional cost collaborative optimization method based on an improved genetic algorithm to enhance the intelligence and efficiency of resource scheduling. This research provides not only a new theoretical framework for

关键词： 360 degrees virtual reality   multiuser access control   proactive caching   mobile edge computing  

摘要： Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider's cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360 degrees VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server's computing capacity (the BS's bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%).

关键词： deep reinforcement learning   internet of vehicles   mobile edge computing   service placement  

摘要： Edge computing offers crucial computational and storage support to vehicles by providing various services within the framework of the Internet of Vehicles in intelligent transportation systems. Service placement (SP) becomes particularly challenging when edge resources are limited and vehicles exhibit high-mobility. Many current dynamic placement methods rely on real-time placement, often leading to increased costs, instability, and frequent changes. This paper proposes SACRF-SP, an adaptive dynamic service placement algorithm based on Soft Actor-Critic (SAC) and Random Forest (RF), for dynamic urban traffic scenarios. This algorithm utilizes the SAC method to identify optimal placement nodes and integrates an RF model to predict service request trends. A decision network is constructed to assess the necessity of redeployment. Extensive simulation experiments demonstrate that SACRF-SP significantly reduces latency, resource usage, and the frequency of redeployment.

关键词： UAV swarm   mobile edge computing   computational offloading   data compression   deep reinforcement learning  

摘要： The combination of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) effectively meets the demands of user equipments (UEs) for high-quality computing services, low energy consumption, and low latency. However, in complex environments such as disaster rescue scenarios, a single UAV is still constrained by limited transmission power and computing resources, making it difficult to efficiently complete computational tasks. To address this issue, we propose a UAV swarm-enabled MEC system that integrates data compression technology, in which the only swarm head UAV (USH) offloads the compressed computing tasks compressed by the UEs and partially distributes them to the swarm member UAV (USM) for collaborative processing. To minimize the total energy and time cost of the system, we utilize Markov Decision Process (MDP) for modeling and construct a deep deterministic policy gradient offloading algorithm with a prioritized experience replay mechanism (PER-DDPG) to jointly optimize compression ratio, task offloading rate, resource allocation and swarm positioning. Simulation results show that compared with deep Q-network (DQN) and deep deterministic policy gradient (DDPG) baseline algorithms, the proposed scheme performs excellently in terms of convergence and robustness, reducing system latency and energy consumption by about 32.7%.

关键词： Mobile edge computing   Resource reservation   Energy harvesting   Contract theory   Information asymmetry  

摘要： To address bottleneck of energy performance, this paper considers an energy harvesting (EH)-enabled mobile edge computing (MEC) system with contract users (CUs), random users (RUs) and an agent node (AN). AN can manage resource interactions between MEC server and users. The resource demands of CUs are known in advance, while RUs are mobile and arrive randomly. We propose a resource reservation framework based on contract theory with the goal of maximizing the utility of AN under information asymmetry and demand uncertainty. First, for symmetric information scenario, CUs are willing to share information with MEC server to obtain the relationship between the optimal resource reservation and the demands of CUs. Then, for asymmetric information scenario, a contract-based resource reservation scheme is proposed to motivate wireless access point to share the demand information of CUs and undertake partial over-reservation risk cost. Based on this, the theoretical expression for the optimal reserved resource is obtained by exploring the features of the feasible contracts under individual rationality and incentive compatibility constraints. We leverage simulation to validate the proposed contract-based resource reservation scheme and verify its effectiveness through simulation results.

关键词： Integrated access and backhaul   Mobile edge computing   Deep learning   Task offloading   Energy efficiency  

摘要： In the B5G era, the number of Internet of Things (IoT) devices is surging, leading to more diverse demands for intelligent applications, most of which are latency-sensitive and resource-intensive. This trend places a demand on communication networks for ultra-high bandwidth, low latency, and high reliability. In this context, UAV-assisted networks combine the advantages of drones and edge computing, providing computational and communication resources over wide areas, thereby enhancing the service capabilities of edge computing. This study explores the issues of task offloading and resource allocation problem in hierarchical edge computing within UAV-assisted Integrated Access and Backhaul Network (IABN) operating in the millimeter-wave band. For hierarchical mobile edge computing systems in integrated access and backhaul networks, we model the optimization problem of minimizing task offloading energy consumption, considering power and latency constraints, and decompose it into two more tractable subproblems. A method based on deep neural networks (DNN) and iterative order-preserving optimization is proposed to obtain the optimal solution for subproblem 1. Additionally, a Whale Optimization-based Power Allocation (WOPA) approach is proposed to solve subproblem 2. Finally, we compared the proposed algorithm's energy consumption and delay in various configuration scenarios against baseline algorithms. The results demonstrate that the proposed algorithm achieves minimal energy consumption with almost no overdue tasks.

关键词： Optimization   Resource management   Computational modeling   Games   Wireless communication   Servers   Real-time systems   Delays   Collaboration   Autonomous aerial vehicles   Collaborative intelligent transportation systems   mobile edge computing   Stackelberg game  

摘要： The rapid advancement of sixth-generation (6G) networks and artificial intelligence technologies is leading to the emergence of collaborative intelligent transportation systems (C-ITS), which is regarded as an essential trend in the future of transportation. Integrating Internet of Things (IoT) with C-ITS is an efficient solution to provide real-time data collection and status monitoring for vehicles and infrastructures to improve the intelligence and reliability of C-ITS. In order to address the challenges of limited battery energy and low computing power of IoT nodes, integrating wireless power transfer (WPT) with mobile edge computing (MEC) is considered as a promising solution to improve their lifetime and computational capability for IoT nodes. In this paper, we investigate a distributed dynamic computing offloading model for an MEC-enabled C-ITS, where multiple roadside units (RSUs) collaborate to provide offloading services to wireless devices (WDs). We formulate the task offloading and bandwidth resource allocation as a distributed Stackelberg game. The WDs act as leaders, aiming to maximize their computing rate by offloading tasks to RSU or performing local computing. The RSUs act as followers, optimizing their bandwidth allocation based on the WDs' offloading decisions, thereby improving the overall system computing rate. We prove the existence of a Stackelberg equilibrium (SE) and propose a multi-agent reinforcement learning algorithm to enable WDs to select offloading decisions and help RSUs optimize bandwidth allocation. Numerical simulation results demonstrate that the proposed scheme offers significant performance improvements over existing methods.

关键词： game theory   mobile edge computing   Nash bargaining   resource allocation  

摘要： While mobile edge computing (MEC) holds promise to enhance users' mobile experience, how to make appropriate offloading decisions under consideration of quality of service (QoS) and energy consumption requirements is challenging. In this research, we focus on obtaining an equilibrium solution that ensures not only the fairness of terminal users and utility maximization for the edge service providers but also QoS and energy consumption requirements. We formulate the cooperative game resource allocation optimization problem (CGRA) under the complete/incomplete information environment and propose the Nash Bargaining Cooperative Game (NBCG) policy to solve it. We perform experiments and compare it with six schemes. Experimental results show that the proposed NBCG can gain better offloading profits than the benchmarks and improve the percent of guaranteed tasks by as much as 23% under different input data sizes, as much as 18% under different time delay tolerances, and as much as 25% under different execution weights.