关键词： Mobile edge computing   Compute-intensive applications   User mobility   Computation offloading   Service migration  

摘要： With the increase in the use of compute-intensive applications, the demand to continuously boost the efficiency of data processing increases. Offloading the compute-intensive application tasks to the edge servers can effectively solve problems for resource-constrained mobile devices. However, the computation offloading may increase network load and transmission delay, which will influence the user experience. On the other hand, the unceasing distance change between the local device and edge server could also affect the service quality due to user mobility. This paper proposes the offloading and service migration methods for compute-intensive applications to deal with these issues. First, the fine-grained computation offloading algorithm based on a non-cooperative game is proposed. The overhead on both the local side and edge side is analyzed. Moreover, the service migration path selection based on the Markov decision process is proposed by considering user mobility, energy cost, migration cost, available storage, and bandwidth. The optimal service migration path is selected according to the Markov decision process, which can improve service quality. Experiment results show that our proposed offloading strategy performs better in reducing energy consumption by more than 10% and latency by more than 6.2%, compared with other baseline algorithms, and saving mobile device energy and reducing task response time, saving over 10% of time and energy consumption compared to similar algorithms. The proposed service migration scheme can reduce migration times and maintain a success rate of more than 90% while guaranteeing service continuity in a multi-user scenario.

关键词： Asynchronous reinforcement learning   Mobile edge computing   Deep reinforcement learning   Online computing offloading  

摘要： The processing of large volumes of data sets unprecedented demands on the computing power of devices, and it is evident that resource-constrained mobile devices struggle to satisfy the need. As a distributed computing paradigm, edge computing can release mobile devices from computation-intensive tasks, reducing the strain and improving processing efficiency. Traditional offloading methods are less adaptable and do not work in some harsh settings. We simplify the problem to binary offloading decisions in this research and offer a new Asynchronous Update Reinforcement Learning-based Offloading (ARLO) algorithm. The method employs a distributed learning strategy, with five sub-networks and a central public network. Each sub-network has the same structure, as they interact with their environment to learn and update the public network. The sub-network pulls the parameters of the central public network every once in a while. Each sub-network has an experienced pool that minimizes data correlation and is particularly successful in preventing situations where the model falls into a local optimum solution. The main reason for using asynchronous multithreading is that it allows multiple threads to learn the strategy simultaneously, making the learning process faster. At the same time, when the model is trained, five threads can run simultaneously and can handle tasks from different users. The results of simulations show that the algorithm is adaptive and can make optimized offloading decisions on time, even in a time-varying Internet environment, with a significant increase in computational efficiency compared to traditional methods and other reinforcement learning methods.

关键词： Task analysis   Wireless communication   Communication system security   Jamming   Security   Internet of Things   Computational modeling   Mobile edge computing   wireless power transfer   physical layer security   secrecy energy efficiency  

摘要： This paper investigates secure offloading for a wireless powered mobile edge computing (MEC) system. Specifically, an energy-constrained user is first charged by a power beacon (PB) via wireless power transfer (WPT) and then leverages the harvested energy to offload its computation tasks to a MEC server in the presence of multiple eavesdroppers. To enhance the security of computation offloading, we propose a novel physical layer security-assisted scheme, in which the PB can also play the role of a cooperative jammer by transmitting jamming signals to interfere with the eavesdroppers. The objective is to maximize secrecy energy efficiency (SEE) of the MEC system by jointly optimizing the transmit power for WPT, offloading, and jamming, the time allocated for WPT and offloading, and the computation task partition while satisfying the secrecy offloading rate constraint and the energy causality constraint. To deal with the highly non-convex problem, we develop an efficient two-layer algorithm by resorting to variable substitutions, successive convex approximation, and the Dinkelbach method. Simulation results demonstrate that our designed scheme yields a SEE performance enhancement as compared to benchmarks.

关键词： Training   Computational modeling   6G mobile communication   Mobile handsets   Collaborative work   Resource management   Optimization   6G   federated learning   heterogeneous network   mobile edge computing  

摘要： 6G is envisioned to achieve ubiquitous Artificial Intelligence (AI) in heterogeneous and massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to assist AI technology to be implemented at the network edge. FEEL enables training a machine learning model through communication among multiple edge devices based on data from a large number of users. However, the heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of FEEL over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process. In particular, we formulate a training efficiency maximization problem, where a novel analytical relation among the training loss, resource consumption and heterogeneity is inferred by convergence analysis. Then, we propose a searching algorithm named IFBA to obtain the optimal inexactness of local models and frequency band allocation for edge devices in order to mitigate the straggler effect caused by the heterogeneity and resource limitation of devices. As a result, the training efficiency can be improved by adapting the inexactness of local models and frequency band allocation for edge devices on-demand. Simulation results validate that AceFL has advantages in training efficiency improvement and heterogeneity adaption, e.g., accelerating the training by 5% - 15% under different heterogeneity.

关键词： Task analysis   Energy consumption   Delays   Computational modeling   Trajectory   Servers   Resource management   UAV communication   mobile edge computing   reinforcement learning   secure transmission  

摘要： UAV communication has received widespread attention in MEC systems due to its high flexibility and line-of-sight transmission. Users can reduce their local computing pressures and computation delay by offloading tasks to the UAV as an edge server. However, the coverage capability of a single UAV is very limited. Moreover, the data offloaded to the UAV will be easily eavesdropped. Thus, in this paper, we propose two secure transmission methods for multi-UAV-assisted mobile edge computing based on the single-agent and multi-agent reinforcement learning, respectively. In the proposed methods, we first utilize the spiral placement algorithm to optimize the deployment of UAVs, which covers all users with the minimum number of UAVs. Then, to reduce the information eavesdropping by a flying eavesdropper, we utilize the reinforcement learning to optimize the secure offloading to maximize the system utility by considering different types of users' tasks with diverse preferences for residual energy of computing equipment and processing delay. Simulation results indicate that compared with the single-agent method and the benchmark, the multi-agent method can optimize the offloading in a better manner and achieve larger system utility.

关键词： narrowband Internet of Things   mobile edge computing   deep reinforcement learning   compute offload   resource allocation  

摘要： Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient comput-ing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algo-rithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200 KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%,and when the execution task volume is 600 KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

关键词： Approximation algorithm   edge data caching   fairness   integer programming   mobile edge computing   optimization  

摘要： Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the network edge. Many studies have attempted to formulate edge data caching strategies for app vendors to optimize caching performance by caching the right data on the right edge servers. However, existing edge data caching approaches have unfortunately ignored fairness, which is an important issue from the app vendor's perspective. In general, an app vendor needs to cache data on edge servers to serve its users with insignificant latency differences at a minimum caching cost. In this paper, we make the first attempt to tackle the fair edge data caching (FEDC) problem. Specifically, we formulate the FEDC problem as a constraint optimization problem (COP) and prove its $\mathcal {NP}$NP-hardness. An optimal approach named FEDC-OPT is proposed to find optimal solutions to small-scale FEDC problems with integer programming technique. In addition, an approximate algorithm named FEDC-APX is proposed to find approximate solutions in large-scale FEDC problems. The performance of the proposed approaches is analyzed theoretically, and evaluated experimentally on a widely-used real-world data set against four representative approaches. The experimental results show that the proposed approaches can solve the FEDC problem efficiently and effectively.

关键词： Tactile Internet   Cloud   5G   Mobile edge computing   Latency  

摘要： Internet of Things (IoT) is one of the promising technologies, announced as one of the primary use cases of the fifth-generation cellular systems (5G). It has many applications that cover many fields, moving from indoor applications, e.g., smart homes, smart metering, and healthcare applications, to outdoor applications, including smart agriculture, smart city, and surveillance applications. This produces massive heterogeneous traffic that loads the IoT network and other integrated communication networks, e.g., 5G, which represents a significant challenge in designing IoT networks;especially, with dense deployment scenarios. To this end, this work considers developing a novel artificial intelligence (AI)-based framework for predicting traffic over IoT networks with dense deployment. This facilitates traffic management and avoids network congestion. The developed AI algorithm is a deep learning model based on the convolutional neural network, which is a lightweight algorithm to be implemented by a distributed edge computing node, e.g., a fog node, with limited computing capabilities. The considered IoT model deploys distributed edge computing to enable dense deployment, increase network availability, reliability, and energy efficiency, and reduce communication latency. The developed framework has been evaluated, and the results are introduced to validate the proposed prediction model.

关键词： Internet of Vehicles   Blockchain   Mobile edge computing   Authentication mechanism   Privacy protection  

摘要： Blockchain technology can provide excellent support for identity authentication and access control mechanisms. In particular, blockchain technology can ensure that large amounts of confidential data generated by the Internet of Vehicles devices are stored and transmitted in a safe and reliable environment, which is the key to making system services optimal. In addition, mobile edge computing is the best solution for IoV applications to deal with low latency and limited computing and storage capacity of vehicle-mounted devices. Mobile edge computing can help IoV systems achieve a variety of functions and features, the most important of which is the ability to process terminal data in real-time. Even though the amount of data generated by IoV devices is growing rapidly, the system is still characterized by low latency and high efficiency. Because the communication between IoV devices is carried out in an untrusted environment, it is particularly important to design a secure and effective identity authentication scheme. Therefore, this paper proposes an efficient, safe, and time-sensitive authentication mechanism for devices on the Internet of Vehicles, which applies to a large number of scenarios. The mechanism is based on the blockchain concept and mobile edge computing technology. Security analysis shows that the proposed scheme meets the security requirements of the Internet of Vehicles and is resistant to many known attacks. By comparing with existing advanced IoT authentication schemes, the performance evaluation of the mechanism shows that the scheme enhances security features while reducing computation and communication overhead.

关键词： Mobile edge computing   D2D underlay communication   Price elasticity model   Log-log model   Task offloading   Delay tolerance  

摘要： The development of 5G/6G aims to provide high mobile computing with ultra-reliable low latency. The implementation of latency-sensitive computing applications is causing the overall cost management of the network to be a challenging issue. A Device-to-Device (D2D) underlay mobile edge computation offloading system has been developed to achieve these goals. This work intends to create a mobile edge computing (MEC)-D2D underlay system with cost optimization using the price elasticity log-log model ((PELM)-M-2). In this system, the task computation of each device can be partially offloaded to the edge server and nearby mobile helper. This work focuses on minimizing the computation cost of the network with computation and communication constraints. The optimization problem P1 is formulated as a mixed-integer nonlinear programming problem (MINLP). The problem P1 is split up into two sub-problems denoted by P2 and P3, respectively. The solution to P2 optimizes the task division ratio to meet delay tolerance. The solution to P3 minimizes energy consumption by using the price elasticity log-log model ((PELM)-M-2). Numerical results show that the proposed PEL2M better than MEC system with binary offloading (MECBO), MEC system with partial offloading (MECPO), Mobile edge computing and device-to-device underlay system with partial offloading (MECD2DUPO), Brute-force algorithm and Hungarian algorithm in terms of reduction in energy consumption by 72.71, 73.24, 66, 70.15, and 66.30%, and the latency is reduced by 88.4, 16.89, 9.5, 11.02, and 9%, respectively. The proposed algorithm may be applied to solve various latency-constrained applications in medical fields, vehicular communication, etc.