关键词： Wireless power transfer   Secure communication   Mobile edge computing   Resource allocation  

摘要： Unmanned aerial vehicles (UAVs) equipped with mobile edge computing (MEC) servers and featuring flexible deployment capabilities can help to reduce the computing pressure on ground user networks. However, the majority of ground users are hindered by their limited battery life, preventing them from working without interruption. To maximize the service lifetime of ground users, UAVs transmit energy to them first and then collect offload tasks afterwards. This approach allows users to work without interruption while transmitting UAVs with computing tasks which can then be processed with the help of MEC servers. This helps to reduce the pressure on ground user networks, ensuring that they remain reliable and efficient. Therefore, we propose an optimization problem that aims to maximize the minimum security offloading rate of the system. This problem involves multiple variables, so conventional methods are not suitable for solving it. Our proposed scheme utilizes block coordinate descent (BCD) and successive convex approximation (SCA) algorithms, which can better optimize the user offloading decision, energy transfer duration, and user transmit power. Numerical results demonstrate that our scheme is more effective than the two benchmark schemes in improving the system performance.

关键词： Mobile edge computing   Service caching   Bursty data traffic   Uncertain processing delays   Online learning  

摘要： Mobile edge computing (MEC) provides extremely low -latency services for mobile users, by attaching computing resources to 5G base stations in an MEC network. Network service providers can cache their services from remote data centers to base stations to serve mobile users within their proximity, thereby reducing service latencies. However, mobile users of network services usually have bursty requests that require immediate processing in the MEC network. The data traffic of such requests is bursty, since mobile users have various hidden features including locations, group tags, and mobility patterns. Furthermore, such bursty data traffic causes uncertain congestion at base stations and thus leads to uncertain processing delays. Considering the limited resources of base stations, network services may not be able to be placed into base stations permanently to handle the bursty data traffic. As such, network services need to be dynamically cached in the MEC network to fully address the bursty data traffic and uncertain processing delays. In this paper, we investigate the problem of dynamic service caching and task offloading in an MEC network, by adopting the online learning technique to harness the challenges brought by bursty data traffic and uncertain processing delays. We first propose an online learning algorithm for the problem with uncertain processing delays, by utilizing the multi -armed bandits technique, and analyze the regret bound of the proposed algorithm. We then propose another online learning algorithm for the problem with bursty data traffic and uncertain processing delays, which adaptively learns the bursty data traffic of requests, based on small samples of mobile users' hidden features. We also propose a novel architecture of Generative Adversarial Network (GAN) to accurately predict user demands using small samples of mobile users' hidden features. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the unce

关键词： Mobile Edge Computing   coverage constraint   deep reinforcement learning   task offloading   resource allocation   resource allocation  

摘要： With the increase of the number of Mobile User Equipments (MUEs), numerous tasks that with high requirements of resources are generated. However, the MUEs have limited computational resources, computing power and storage space. In this paper, a joint coverage constrained task offloading ffl oading and resource allocation method based on deep reinforcement learning is proposed. The aim is offloading ffl oading the tasks that cannot be processed locally to the edge servers to alleviate the conflict between the resource constraints of MUEs and the high performance task processing. The studied problem considers the dynamic variability and complexity of the system model, coverage, offloading ffl oading decisions, communication relationships and resource constraints. An entropy weight method is used to optimize the resource allocation process and balance the energy consumption and execution time. The results of the study show that the number of tasks and MUEs affects ff ects the execution time and energy consumption of the task offloading ffl oading and resource allocation processes in the interest of the service provider, and enhances the user experience.

关键词： maritime autonomous surface ships   large language model   ship-handling theory test   mobile edge computing   mobile cloud computing   68Txx  

摘要： Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs' knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests f

关键词： Task analysis   Wireless communication   Energy consumption   Communication system security   Resource management   Reliability   Servers   Mobile edge computing   user-centric networks   blockchain   task offloading   alternating direction method of multipliers  

摘要： In the traditional mobile edge computing (MEC) system, the availability of MEC services is greatly limited for the edge users of the cell due to serious signal attenuation and inter-cell interference. User-centric MEC (UC-MEC) can be seen as a promising solution to address this issue. In UC-MEC, each user is served by a dedicated access point (AP) cluster enabled with MEC capability instead of a single MEC server, however, at the expense of more energy consumption and greater privacy risks. To achieve efficient and reliable resource utilization with user-centric services, we propose an energy-efficient blockchain-enabled UC-MEC system where blockchain operations and resource optimization are jointly performed. Firstly, we design a resource-aware, reliable, replicated, redundant, and fault-tolerant (R-RAFT) consensus mechanism to implement secure and reliable resource trading. Then, an optimization framework based on alternating direction method of multipliers (ADMM) is proposed to minimize the total energy consumed by wireless transmission, consensus, and task computing, where AP clustering, computing resource allocation, and bandwidth allocation are jointly considered. Simulation results show the superiority of the proposed UC-MEC system over reference schemes, with at most 33.96% reduction in the total delay and 48.77% reduction in the total energy consumption.

关键词： Mobile edge computing   edge server planning   mobility management   graph neural network   learning-based framework   Mobile edge computing   edge server planning   mobility management   graph neural network   learning-based framework  

摘要： Mobile service continuity is essential important to ensure that user sessions and services will survive user mobility. The 5G enhances its mobility management by providing the flexibility and offering three types of Session and Service Continuity (SSC) modes to address various service continuity requirements. Multi-access edge computing (MEC) is a type of widely adopted network architecture that delivers network services from the boundary of the mobile network by provisioning a set of edge servers. Determining an optimum planning of MEC edge servers, which involves determining edge servers appropriate geographical positions and their serving areas, is a precondition for more efficient service provisioning and better usage of network resources. In this work, we investigate the MEC servers planning problem by considering the management cost for maintaining MEC service continuity. The problem is formulated as a graph partitioning problem to partition the RAN graph with minimum SSC management costs and balanced MEC servers workloads. Then, we adapt a generalizable approximate Graph Partitioning framework which leverages on Graph Neural Network (GNN) to embed the RAN network spacial feature and on Multilayer Perceptron (MLP) for graph partitioning. Based on the framework, we propose a MEC server planning algorithm named MECP-GAP. Finally, we evaluate MECP-GAP with extensive simulations and real network data. Comparing to several baselines, MECP-GAP achieves better performance with lower running time.

关键词： Medical services   Servers   Privacy   Computational modeling   Games   Data privacy   Data models   Federated learning   healthcare   mobile edge computing   incentive mechanism   power allocation   energy efficiency   privacy-preserving  

摘要： Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing (MEC)-assisted federated learning (FL) has emerged as a promising technology for providing fast, efficient, and reliable healthcare services while ensuring patient privacy. However, concerns about the privacy and security of sensitive information often make patients hesitant to share their data. Moreover, MEC servers face challenges accessing the necessary radio resources for data transmission. To address these issues, designing an effective incentive mechanism that encourages healthcare user participation in FL and facilitates resource provision from the base station (BS) is vital. This work proposes an efficient and privacy-preserving incentive scheme that considers the interaction among the BS, MEC servers, and MEC users in the MEC-assisted FL healthcare system. Utilizing the Stackelberg game model, we investigate the allocation of transmit power, determination of differential privacy (DP) budgets for MEC users, reward strategies, radio resource demands for MEC servers, and pricing for radio resources at the BS. Furthermore, we analyze the Stackelberg equilibrium and empirically validate the effectiveness of our proposed scheme using a real-world medical dataset.

关键词： Mobile edge computing  

摘要： Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems. © 2024 The Authors

关键词： Mobile edge computing   Vehicular fog computing   Urban intelligent video systems   Task placement   ICVs  

摘要： With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these "mobile" edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper.

关键词： Task analysis   Heuristic algorithms   Vehicle dynamics   Adaptation models   Space vehicles   Real-time systems   Mobile computing   Digital twin   generative adversarial network   mobile edge computing   swarm intelligence   task offloading   vehicular edge computing  

摘要： Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, Adaptive Swarm Intelligent Offloading Scheme Based on Digital-Twin-Assisted PRediction In VEC (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improveD genetIc simulatEd annealing-baSEd particLe swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.