关键词： Processor scheduling   Task analysis   Wireless communication   Dynamic scheduling   Heuristic algorithms   Resource management   Computational modeling   Mobile edge computing   computation offloading   reinforcement learning   resource allocation  

摘要： Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs' tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches.

关键词： Training   Servers   Computational modeling   Data models   Transfer learning   Multi-access edge computing   Training data   Client selection   federated transfer learning   intrusion detection   mobile edge computing   reinforcement learning  

摘要： In this letter, we propose an efficient federated transfer learning (FTL) framework with client selection for intrusion detection (ID) in mobile edge computing (MEC). Specifically, we leverage federated learning (FL) to preserve privacy by training model locally, and utilize transfer learning (TL) to improve training efficiency by knowledge transfer. For FL, unreliable and low-quality clients should not be selected to participate in the training. Therefore, we integrate FTL with a reinforcement learning (RL)-based client selection scheme to achieve the highest ID accuracy within a budget limit on the number of participating clients. Experimental results show that the FTL significantly improves ID accuracy and communication efficiency as compared with the FL. Furthermore, the FTL framework with RL-based client selection can achieve the highest accuracy within budget, which improves performance while saving cost.

关键词： Mobile edge computing   computation offloading   NOMA   deep reinforcement learning  

摘要： Mobile edge computing (MEC) is a promising solution to enable resource-limited mobile devices to offload computation-intensive tasks to nearby edge servers. In this paper, dynamic computation offloading in a non-orthogonal multiple access (NOMA) based multi-user network is investigated by considering stochastic task arrivals and wireless channels. Specifically, a cooperative multi-agent deep reinforcement learning (CMADRL) based framework is proposed to learn decentralized offloading policies to allocate task offloading and local execution powers at each user, which minimizes long-term average network computation cost in terms of power consumption and buffering delay. By leveraging a centralized training and distributed execution strategy, the proposed framework can not only learn efficient decentralized policies, but also relieves users' computational pressure and works well in coordinating the interference of NOMA-based networks. To reduce the training complexity, it is further improved to train one parameter shared policy network that can be exploited by all users with slightly sacrificed performance. Numerical simulations demonstrate that the proposed CMADRL based framework can learn efficient dynamic offloading policies at each user, which significantly outperforms the conventional independent Q-learning based framework as well as some greedy strategies with lower network computation cost.

关键词： Mobile edge computing  

摘要： The development and popularization of mobile Internet and wireless communication technology have spawned a large number of computation-intensive and delay-intensive applications. Limited computing resources and existing technologies cannot meet the performance requirements of new applications. Mobile edge computing technology can use wireless communication technology to offload data to be stored and computing tasks to the nearby assistant or edge server with idle resources. Based on the data offloading of distributed wireless sensor device to device communication, the architecture is designed and the basic framework of distributed mobile edge computing is constructed. To solve the problem of high mobile cloud computing technology, the offloading model of optimized mobile edge computing was proposed, and the stability and convergence of the proposed algorithm were proved. Finally, the system performance of the proposed algorithm is verified by simulation. The results show that the proposed algorithm can converge within a finite number of steps. Compared with other benchmark schemes, the proposed algorithm has better performance in reducing system energy consumption, reducing moving edge response delay and system total delay.

关键词： Mobile edge computing   Computation offloading   Energy harvesting  

摘要： Mobile edge computing (MEC) has emerged for meeting the ever-increasing computation demands from mobile applications. Mobile users endowing with computation and energy harvesting (EH) capabilities, called EH devices, are desired in MEC systems. With computation capability, EH devices can switch between local computing and mobile-edge computing for better QoEs. With EH technologies, mobile users could survive perpetually for supporting long-term task processing. However, the heterogeneous computation resource among EH devices and edge servers, limited wireless resource as well as time-constrained harvestable energy challenge the computation offloading. This paper investigates a green MEC with EH devices and develops an effective EH computation offloading scheme towards minimizing delay for green MEC. We formulate and analyze the EH computation offloading problem with game theory. Then, an EH computation offloading game scheme, including the Lyapunov drift-based energy harvesting and computation offloading best response algorithms, is designed to find out optimal energy harvesting and computation offloading solutions to address the problem. The simulation results have been conducted to demonstrate the efficiency of the proposal.

关键词： Mobile edge computing  

摘要： This paper considers a practical mobile edge computing (MEC) system, where edge server does not preinstall the program required to perform user offloaded computing tasks. A partial program offloading (PPO) scheme is proposed, which can divide a user program into two parts, where the first part is executed by the user itself and the second part is transferred to an edge server for remote execution. However, the execution of the latter part requires the results of the previous part (called intermediate result) as the input. We aim to minimize the overall time consumption of a multi-server MEC system to complete all user offloaded tasks. It is modeled as a mixed integer nonlinear programming (MINLP) problem which considers user-and-server association, program partitioning, and communication resource allocation in a joint manner. An effective algorithm is developed to solve the problem by exploiting its structural features. First, the task completion time of a single server is minimized given the computing workload and available resource. Then, the working time of the edge servers are balanced by updating user-and-server association and communication resource allocation. Numerical results show that significant performance improvement can be achieved by the proposed scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G   6G   Smart Internet of Vehicle   Mobile edge computing   Simultaneous wireless information and power transfer   Full-duplex   Multi-input and multi-output  

摘要： Smart Internet of Vehicles (IoV) as a promising application in Internet of Things emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes upload and download data through an anchor node which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output and full-duplex technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes.

关键词： Internet of things   Mobile edge computing   Computation offloading   Differential evolution   Ant colony optimization   Particle swarm optimization  

摘要： Internet of Things (IoT) tasks are offloaded to servers located at the edge network for improving the power consumption of IoT devices and the execution times of tasks. However, deploying edge servers could be difficult or even impossible in hostile terrain or emergency areas where the network is down. Therefore, edge servers are mounted on unmanned aerial vehicles (UAVs) to support task offloading in such scenarios. However, the challenge is that the UAV has limited energy, and IoT tasks are delay-sensitive. In this paper, a UAV-based offloading strategy is proposed where first, the IoT devices are dynamically clustered considering the limited energy of UAVs, and task delays, and second, the UAV hovers over each cluster head to process the offloaded tasks. The optimization problem of dynamically determining the optimal number of clusters, specifying the member tasks of each cluster, is modeled as a mixed-integer, nonlinear constraint optimization. A discrete differential evolution (DDE) algorithm with new mutation and crossover operators is proposed for the formulated optimization problem, and compared with the particle swarm optimization (PSO) and genetic algorithm (GA) meta-heuristics. Further, the ant colony optimization (ACO) algorithm is employed to identify the shortest path over the cluster heads for the UAV to traverse. The simulation results validate the effectiveness of the proposed offloading strategy in terms of tasks delays and UAV energy consumption.

关键词： Mobile edge computing  

摘要： Computation offloading is a hot research topic in mobile edge computing (MEC). Computation offloading among multiedge nodes in heterogeneous networks can help reduce offloading cost. In addition, the unmanned aerial vehicles (UAVs) play a key role in MEC, where UAVs in the air communicate with ground base stations to improve the network performance. However, limited channel resources can lead to the increase of transmission delay and the decline of communication quality. Effective channel selection mechanisms can help address those issues by improving transmission rate and ensuring communication quality. In this paper, we study channel selection during communication between multiple UAVs and base stations in an MEC system with heterogeneous networks. To maximize the transmission rate of each UAV user, we formulate a channel selection problem and model it as a noncooperative game. Then, we prove the existence of Nash equilibrium (NE). In addition, we design a multiple UAV-enabled transmission channel selection (UTCS) algorithm to obtain the equilibrium strategy profile of all the UAV users. Experimental results validate that UTCS algorithm can converge after a finite number of iterations and it outperforms random transmission algorithm (RTA) and sequential transmission algorithm (STA).

关键词： Mobile edge computing   Vehicular edge computing   Task offloading   Resource allocation   Unlicensed channels  

摘要： Recently, the emerging computation-intensive in-vehicle applications and the exponentially growing data have posed a serious challenge to resource-limited in-vehicle devices. By integrating the mobile edge computing (MEC) into the vehicle networks, vehicular edge computing (VEC) is envisioned as a promising solution. In this paper, we divide the VEC system into small time slots, which effectively simulates dense small-scale computing scenarios in the rapidly varying channel environment over time. Furthermore, to alleviate the shortage of spectrum resources, vehicles can offload part of computing tasks to the edge server not only through licensed channels but also through unlicensed channels. However, dynamic topology changes and complex communication characteristics of the vehicle networks lead to the strategies of task offloading and resource allocation being difficult to obtain. By decoupling the non-convex optimizing problem and splitting it into multiple sub-problems, we design a joint power, spectrum and computing resource allocation strategy with low complexity, in order to minimize the total energy consumption of all vehicles for processing tasks. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme that employing licensed and unlicensed access in VEC networks can reduce the energy consumption of vehicles for processing tasks in comparison with baseline schemes.