关键词： Mobile edge computing   computation offloading   code analysis   object-oriented application   software adaptation  

摘要： Mobile edge computing (MEC) offers a promising technology that deploys computing resources closer to mobile devices for improving performance. Most of the existing studies support on-demand remote execution of the computing tasks in applications through program transformation, but they commonly assume that mobile devices merely resort to a single server for computation offloading, which cannot make full use of the scattered and changeable computing resources. Thus, for object-oriented applications, we propose a novel approach, called FUNOff, to support the dynamic offloading of applications in MEC at the function granularity. First, we extract a call tree via code analysis and locate the function invocations that are suitable for offloading. Next, we refactor the code of related object functions according to a specific program structure. Finally, we make offloading decisions referring to the context at runtime and send function invocations to multiple remote servers for execution. We evaluate the proposed FUNOff on two real-world applications. The results show that, compared with other approaches, FUNOff better supports the computation offloading of object-oriented applications in MEC, which reduces the response time by 10.7%-58.2%.

关键词： cache   DDPG   mobile edge computing   UAV  

摘要： Mobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade-off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good condition. This article studies how to intelligently allocate the computing capability and wireless bandwidth among users for a cache-aided multi-terminal multi-CAP MEC network with non-ideal channel estimation, where there are N$$ N $$ mobile terminals and M$$ M $$ CAPs in the network. Each terminal has some tasks that need to be computed in a fast and efficient way. For such a system, we first design the system by jointly considering the computing capability and wireless bandwidth allocation, where the computing and communication delay is used as the performance of metric. To optimize the system performance, we then employ deep deterministic policy gradient to learn an effective strategy on the allocation of computing capability and wireless bandwidth, in order to decrease the system delay as much as possible. Simulations are finally conducted to show the superiority of the proposed studies in this article, especially about the advantages from cache.

关键词： Training   Recommender systems   Computational modeling   Servers   Data models   Data privacy   Federated learning   recommendation system   mobile edge computing   incentive mechanism   game theory  

摘要： With the rapid development of consumer electronics and communication technology, a large amount of data is generated from end users at the edge of the networks. Modern recommendation systems take full advantage of such data for training their various artificial intelligence (AI) models. However, traditional centralized model training has to transmit all the data to the cloud-based servers, which suffers from privacy leakage and resource shortage. Therefore, mobile edge computing (MEC) combined with federated learning (FL) is considered as a promising paradigm to address these issues. The smart devices can provide data and computing resources for the FL and transmit the local model parameters to the base station (BS) equipped with edge servers to aggregate into a global model. Nevertheless, due to the limited physical resources and the risk of privacy leakage, the users (the owners of the devices) would not like to participate in FL voluntarily. To address this issue, we take advantage of game theory to propose an incentive mechanism based on the two-stage Stackelberg game to inspire users to contribute computing resources for FL. We define two utility functions for the users and the BS, and formulate the utility maximization problem. Through theoretical analysis, we obtain the Nash equilibrium strategy of the users and the Stackelberg equilibrium of the utility maximization problem. Furthermore, we propose a game-based incentive mechanism algorithm (GIMA) to achieve the Stackelberg equilibrium. Finally, simulation results are provided to verify the performance of our GIMA algorithm. The experimental results show that our GIMA algorithm converges quickly, and can achieve higher utility value compared to other incentive methods.

关键词： Mobile edge computing   Real-time video analysis   Computation offloading  

摘要： Driven by the Quality of Experience (QoE) demands for video analysis applications within contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles (IoV), networks are confronted with numerous challenges, including, but not limited to, the requirements for ultra-high bandwidth, ultra-low latency, reliability, and stability. Mobile Edge Computing (MEC) as a technology aims to decentralize computational and storage resources closer to data sources and end-users, meeting the demands of computation-intensive and latency-sensitive video analysis applications and thereby effectively addressing QoE requirements. This has led to a new intersection, edge video offloading, which has attracted widespread attention. However, to date, there are but a few extant papers on this topic with limited relevance. To bridge this gap, we offer a comprehensive review of the state-of-the-art approaches in computational offloading techniques applied to video data, innovatively revisiting existing task offloading technologies from the perspective of mobility of edge server nodes, including solutions based on both fixed and dynamic MEC servers. Additionally, we explore the differences in video offloading techniques between scenarios involving a single MEC server and those involving multiple MEC servers. Meanwhile, open challenges are presented to direct future research in this field.

关键词： Autonomous aerial vehicles   NOMA   Semantic Web   Security   Blockchains   Resource management   Jamming   Mobile edge computing   resource allocation   unmanned aerial vehicle   blockchain   federated learning   WEB 3.0.  

摘要： The use of unmanned aerial vehicles (UAVs) in Internet-of-Things (IoT) has grown, but security for UAV communications still a challenge due to the distributed nature of line-of-sight communication networks. This paper analyses the security issues for UAV-assisted MEC systems, in which UAVs are used to support base stations (BSs) in computing offloaded workloads while also operate as jammers to inhibit malicious eavesdroppers. In this paper, a minimum secure computing capacity maximization issues is addressed by jointly optimizing communication link, computation resources, and UAVs trajectories. These issues are of a non-trivial nature and present a significant challenge to be resolved due to the presence of highly coupled variables. In order to effectively overcome these issues, we introduced the concept of WEB 3.0. Firstly, we introduced blockchain method to enhance security to the UAV assisted MEC system and then to train the data locally and to solve various wireless schemes problems, respectively we proposed a FL method. To contribute to the literature, we developed an effective resource allocation strategy that maximizes computational offloading using WEB 3.0, a Blockchain-based FRL method. Finally, numerical findings indicate that the suggested techniques improve the security computation capacity efficiency of the systems when compared to the standards.

关键词： Task analysis   Energy consumption   Servers   Optimization   Delay effects   Industries   Mobile handsets   Mobile edge computing   industry 50   task scheduling   offloading strategy   particle swarm optimization  

摘要： With the development of network and communication technology, artificial intelligence, distributed computing and beyond fifth-generation communications, Industry 5.0 is booming and obtains rapid growth. To improve the processing efficiency of intensive tasks, Mobile Edge Computing (MEC) technology can facilitate task offloading from mobile devices to edge servers. Traditional methods do not fully consider that applications are usually composed of dependency-aware tasks, which neglect the impact of task dependencies on offloading strategies and lead to low efficiency in task scheduling. This paper proposes a joint optimization of energy consumption and time delay for dependency-aware task offloading with mobile edge computing. First, in order to minimize the energy consumption and task processing of mobile device, a dependency-aware task offloading model is established. Secondly, the dependencies between tasks are analyzed to construct a Directed Acyclic Graph (DAG), and an algorithm based on topological ordering is introduced to obtain possible solutions for task scheduling. Furthermore, to minimize the total cost, an improved Particle Swarm Optimization (PSO) algorithm is used to obtain the optimal task offloading decision and MEC server selection optimization. Experimental results demonstrate that the proposed strategy can reduce the time cost and energy consumption compared to existing typical methods for tasks with different dependencies effectively.

关键词： Task analysis   Servers   Jamming   Delays   Security   Eavesdropping   Wireless communication   Mobile edge computing   service caching   secure computation offloading   cooperative jamming  

摘要： Mobile edge computing (MEC) enables the caching of various services close to users, thereby reducing service delay for emerging applications. However, realizing efficient and secure computation offloading is challenging due to the limited storage capacity of MEC servers and the offloading security issue arising from the open nature of wireless channels. In this letter, we investigate a MEC network with an eavesdropper, where the MEC server caches service programs required for task execution. Moreover, we propose a cooperative jamming-assisted scheme to enhance the security of computation offloading, in which a friendly jammer actively transmits signals to disrupt eavesdropping. Considering the limited caching capacity of the MEC server and the security concerns of computation offloading, we formulate a delay minimization problem by jointly optimizing service caching placement, transmit power, and devices' computation offloading decisions. To tackle this problem, we develop an efficient layered algorithm using successive convex approximation and the cross entropy-based technique. Numerical results demonstrate the effectiveness of our proposed scheme in reducing the overall delay in completing devices' computation tasks compared to baseline schemes.

关键词： Quality of service   Servers   Digital twins   Consumer electronics   Real-time systems   Optimization   Task analysis   Mobile edge computing   consumer electronics   diversified top-k service composition   digital twin  

摘要： Mobile Edge Computing (MEC) stands as an indispensable technology in the facilitation of 5G networks, enabling the deployment of widely-used services on edge servers situated in close proximity to consumer electronics. Within the MEC framework, a central role is attributed to edge service composition (ESC), pivotal in bolstering functionality and ameliorating user experiences. Presently, prevailing methods for ESC predominantly center on the prioritization of Quality of Service (QoS) optimization, presenting a solitary optimal composite service for consumer electronics invocation. Regrettably, this approach sidelines the significance of solution diversity within composite services, potentially resulting in service overload and the suboptimal utilization of edge resources. To surmount these challenges, this study integrates digital twin (DT) technology and diversified search mechanisms into the MEC domain, offering an innovative diversified top- ${k}$ service composition methodology known as DSC-DT. By harnessing the capabilities of DT technology, DSC-DT enables the emulation and assessment of diverse composite service solutions within a virtual space. Specifically, the proposed methodology models the procedure of service composition within a DT environment as an issue of subgraph isomorphism. This is succeeded by the configuration of the diversification process as an independent set predicament within an undirected graph, efficiently resolved through a greedy algorithmic paradigm. It is noteworthy that DSC-DT accommodates a gamut of query types, including normal queries, constraint queries, and optimal queries. The efficacy and efficiency of the proposed approach are corroborated through comprehensive experiments conducted upon authentic datasets.

关键词： Mobile edge computing   federated continual learning   data selection and aggregator placement   approximation and online algorithms  

摘要： Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly accurate machine learning model for new event predictions. The age of data, defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

关键词： Task analysis   Servers   Autonomous aerial vehicles   Relays   Computational modeling   Resource management   Optimization   Mobile edge computing   unmanned aerial vehicle   relaying   user mobility   3D deployment  

摘要： As one of the most popular consumer electronics, Unmanned Aerial Vehicle (UAV) has the potential to assist User Equipment (UE) in the Internet of Everything. Mobile edge computing (MEC) is also an emerging technique that can provide sufficient computing resources to IoE users. In this paper, we focused on the UAV-assisted MEC problem with mobile UEs, where the UAV serves as an MEC server to assist UEs in computing and acts as a relay to deliver tasks to a ground access point (AP). The objective is to minimize the average time for completing all tasks in the network while jointly optimizing resource allocation such as communication bandwidth, CPU frequency, task division ratio, and UAV's three-dimensional location deployment. First, we proposed an online MEC network adjustment scheme. Then, we decomposed the formulated non-convex problem into three low-complexity subproblems. Last, we proposed a successive convex approximation-based joint optimization algorithm to solve them. In addition, we presented heuristic conclusions in task allocation rules, UAV flight trajectory prediction, and UAV hovering altitude selection, which can further reduce task completion time and deepen the understanding of the working mode of mobile edge servers. Simulation results show that the algorithm can significantly reduce the completion time.