关键词： Mobile edge computing   Deep reinforcement learning   Directed acyclic graph   Deep neural network  

摘要： With the rapid development of innovative applications, lots of computation-intensive and delay-sensitive tasks are emerging. Task offloading, which is regarded as a key technology in the emerging mobile edge computing paradigm, aims at offloading the tasks from mobile devices (MDs) to edge servers or the remote cloud to reduce system delay and energy consumption of MDs. However, most existing task offloading studies either didn't consider the dependencies among tasks or simply designed heuristic schemes to solve dependent task offloading problems. Different from these studies, we propose a deep reinforcement learning (DRL) based task offloading scheme to jointly offload tasks with dependencies. Specifically, we model the dependencies among tasks by directed acyclic graphs and formulate the task offloading problem as minimizing the average cost of energy and time (CET) of users. To solve this NP-hard problem, we propose a deep Q-network learning-based framework that creatively utilizes deep neural networks to extract system features. Simulation results show that our proposed scheme outperforms the existing DRL scheme and heuristic scheme in reducing the average CET of all users and can obtain near-optimal solutions.

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： Satellites   Task analysis   Games   Internet of Vehicles   Heuristic algorithms   Computational modeling   Vehicle dynamics   6G   mobile edge computing   Nash equilibrium   Stackelberg game   Internet of Vehicles  

摘要： With the development of Internet of Vehicles (IOV) technology, minimizing network latency while performing computationally intensive tasks has become a challenge. As the most advanced data communication technology, 6G can significantly reduce the latency. Additionally, utilizing remote satellites for assistance can alleviate the computational pressure. In this paper, an Integrated Satellite-Terrestrial Internet of Vehicles (IST-IOV) environment was established with 6G networks. Firstly, we established the Edge Computation Service Provider (ECSP) to manage task allocation and data flow. Secondly, considering energy consumption, latency, and offloading process, we define utility values for vehicles and ECSP, construct a Stackelberg bi-level dynamic cooperative game. The existence of the unique Nash equilibrium in the game has also been proved by backward induction methods. Finally, based on the interaction between vehicles and ECSP, we proposed a computation offloading algorithm called Dynamic Cooperative Game Theory in IOV (DCGT-IOV). The DCGT-IOV algorithm can find the optimal offloading strategy for vehicles and the optimal pricing strategy for ECSP based on the gradient of the utility function. Simulation results demonstrate that the proposed algorithm achieves higher overall utility in various scenarios compared with other strategies and exhibits good convergence properties.

关键词： Jamming   Autonomous aerial vehicles   Task analysis   Heuristic algorithms   Optimization   Resource management   Computational modeling   UAV communication   mobile edge computing   anti-jamming   resource management   reinforcement learning  

摘要： In recent years, jointly optimizing unmanned aerial vehicle (UAV) hover point selection and resource management for UAV-assisted mobile edge computing (MEC) is a hot research topic. Unlike previous studies, this paper investigates the optimization problem of hover point selection and resource management under dynamic jamming attacks, where the objective is to maximize overall communication and computing efficiency while taking into account constraints on total UAV power and the availability of channels. Due to the non-convex problem and highly dynamic environments, we then propose an advanced deep reinforcement learning (DRL) algorithm to jointly optimize UAV hover point selection, task collection time ratio, transmission power, channel selection, and task offloading ratio to improve the efficiency of UAV-assisted MEC. Specifically, the algorithm optimizes UAV hover point selection to minimize the negative effect of jamming attacks, and then manages resources to improve UAV task processing capacity and reduce energy consumption while mitigating jamming. Simulation results demonstrate that our proposed learning-based algorithm significantly enhances the computing and offloading efficiency in complex and dynamic UAV-assisted MEC environments against jamming compared to other existing algorithms.

关键词： Mobile edge computing   Unmanned aerial vehicle   Task offloading   Federated reinforcement learning   Split learning  

摘要： The convergence of mobile edge computing (MEC) network with unmanned aerial vehicles (UAVs) presents an auspicious opportunity to revolutionize wireless communication and facilitate high-speed internet access in remote regions for mobile devices (MDs) as well as large scale artificial intelligence (AI) models. However, the substantial amount of data produced by the UAVs-assisted MEC network necessitates the integration of efficient distributed learning techniques in AI models. In recent times, distributed learning algorithms, including federated reinforcement learning (FRL) and split learning (SL), have been explored for the purpose of learning machine learning (ML) models that are distributed by sharing model parameters, as opposed to large raw data-sets as seen in traditional centralized learning algorithms. To implement the hybrid method, the model is first trained locally on each UAV-assisted MEC network using SL. Subsequently, the model parameters that have been encrypted are sent to a central server for federated averaging. Finally, after the model has been updated, it is distributed to each UAV-assisted MEC network for local fine-tuning. Our simulations indicate that the proposed split and federated reinforcement learning (SFRL) framework yields comparable high-test accuracy performance while consuming less energy compared to extant distributed learning algorithms. Furthermore, the SFRL algorithm efficiently realizes energy-efficient selection between the SL and FRL methods under different distributions. Numerical results shows that the proposed scheme improves the accuracy by 29.31% and reduced the energy consumption by around 67.34% and time delay by about 7.37%. as compared to the existing baseline schemes.

关键词： Internet of Things   Digital twins   Delays   Approximation algorithms   Sensors   Heuristic algorithms   Data models   Digital twin   mobile edge computing   query services   age of information   IoT applications   service delays   approximation algorithms   resource allocation   optimization  

摘要： The Mobile Edge Computing (MEC) paradigm gives impetus to the vigorous advancement of the Internet of Things (IoT), through provisioning low-latency computing services at network edges. The emerging digital twin technique has been explosively growing in the IoT community, which bridges the gap between physical objects and their digital representations in an MEC network, enabling real-time monitoring and analysis, simulations on the dynamics of systems, accurate predictions on behaviours of objects, and optimization on network resource allocation. In this paper, we consider AoI-aware query services in an MEC network empowered by digital twin technology for diverse IoT applications. We aim to maximize the weighted sum of the accumulative freshness of query results measured by the Age of Information (AoI) and the total query service delay of admitted requests. To this end, we first formulate a novel minimization problem that explores nontrivial trade-offs between the two conflicting optimization objectives: the freshness of query results and service delays, and we show the NP-hardness of the problem. Then, we propose an approximation algorithm with a provable approximation ratio for the problem, at the expense of bounded computing capacity violations. We also develop a heuristic for the problem without any capacity violations. We finally evaluate the performance of the proposed algorithms via simulations. The simulation results demonstrate that the proposed algorithms are promising, and outperform the comparison benchmarks.

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： UAV routing   Mobile Edge Computing   heterogeneous multiple UAVs   IoT ground devices   heuristic  

摘要： The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.

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： Age of information   transmission-computation trade-off   mobile edge computing   service preemption  

摘要： The freshness of information in real-time monitoring systems has received increasing attention, with Age of Information (AoI) emerging as a novel metric for measuring information freshness. In many applications, update packets need to be computed before being delivered to a destination. Mobile edge computing (MEC) is a promising approach for efficiently accomplishing the computing process, where the transmission process and computation process are coupled, jointly affecting freshness. In this paper, we aim to minimize the average peak AoI (PAoI) in an MEC system. We consider the generate-at-will source model and study when to generate a new update in two edge server setups: 1) computing preemption, where the packet in the computing process will be preempted by the newly arrived one, and 2) non-preemption, where the newly arrived packet will wait in the queue until the current one completes computing. We prove that the fixed threshold policy is optimal in a non-preemptive system for arbitrary transmission time and computation time distributions. In a preemptive system, we show that the transmission-aware threshold policy is optimal when the computing time follows an exponential distribution. Our numerical simulation results not only validate the theoretical findings but also demonstrate that: 1) in our problem, preemptive systems are not always superior to non-preemptive systems, even with exponential distribution, and 2) as the ratio of the mean transmission time to the mean computation time increases, the optimal threshold increases in preemptive systems but decreases in non-preemptive systems.