关键词： Task analysis   Servers   Channel allocation   Resource management   Stability analysis   Energy consumption   Delays   Mobile edge computing   computation offloading   network utility optimization   resource management   channel allocation  

摘要： To accommodate ever-increasing computational workloads while satisfying the requirements of delay-sensitive tasks, mobile edge computing (MEC) is proposed to offload the tasks to nearby edge servers. Nevertheless, it can introduce new technical issues in terms of transmission and computation overheads affected by the underlying offloading decisions. In this paper, we investigate the computation offloading problem in a hierarchical network architecture, where tasks can be offloaded to nearby micro-BS and further forwarded to macro-BS equipped with an MEC server. Specifically, we propose a scheme of joint channel allocation and resource management, named JCRM, to make offloading decisions and maximize the long-term network utility with considering stochastic task arrival/dispatch and dynamic changes in available resources. As the formulated utility maximization problem is a mixed-integer non-linear stochastic programming problem that is directly intractable, we, therefore, leverage the Lyapunov optimization technique to decouple the original problem into three separate sub-problems. Based on the solutions to those sub-problems, our proposed scheme can make optimal offloading-downloading decisions with maximizing the overall task offloading rate. Finally, we verify the long-term network stability and near-optimal performance of JCRM via both theoretical analysis and extensive simulations.

关键词： Device-to-device communications   Game theory   Security   Mobile Edge Computing  

摘要： The unique distributed architecture of device-to-device (D2D) networks makes its devices and users more vulnerable to security and privacy threats, especially from malware. To protect the D2D network from the malware, we designed and implemented a novel routing protocol, Cluster-head Centered Fast Secure Routing. The protocol requires all of the member nodes to pass data packets through the Secure Routes selected by the cluster-head that optimizes routing decisions to reduces the possibility of the nodes receiving packets with malware. After our analysis, the strategy is the optimal coping strategy of thedefenderwhenever theattackerusing the Nash game strategy, the Stackelberg game strategy, considering the case of both zero-sum and the non-zero sum. Moreover, to ensure the security of the cluster-head, we have set up the Mobile Edge Computing servers at the edge of the D2D networks. Simulation results demonstrate that the proposed routing protocol can balance network security and Quality of Service, thus confirming that our proposed routing algorithm has practical significance.

关键词： edge cloud   fog computing   fuzzy logic   IoT app   mobile edge computing   mobile resource augmentation framework  

摘要： Mobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource-intensive mobile applications and internet of things (IoT)-based mobile applications. To solve this resource constraint problem, a web service-based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource-intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource-intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.

关键词： Task analysis   Servers   Energy consumption   NOMA   Optimization   Time-frequency analysis   Programming   Non-orthogonal multiple access   mobile edge computing   system computation energy efficiency  

摘要： Energy-efficient communication is one of the most pertaining issues in realizing mobile edge computing (MEC) networks. How to effectively use non-orthogonal multiple access (NOMA) to realize energy efficient MEC networks has been of high interest and calls for in-depth study. In particular, there has been yet no work to study the energy efficient communication scheme from system perspective in this area. In this work, we focus on the design of a system-centric computation energy efficiency (EE) scheme in a NOMA-based MEC network. The system computation EE is maximized by jointly optimizing the computing frequencies of the MEC server and the local users, the offloading time, the transmit powers of local users, as well as the execution time for the local users and the MEC server. Employing the fractional programming theory and introducing auxiliary variables, we develop an efficient Dinkelbach-based iterative scheme to tackle the formulated non-linear fractional programming problem. For obtaining instrumental insights, we further derive closed-form expressions for the partial solutions based on Karush-Kuhn-Tucker conditions. Simulation results are provided to verify the quick convergence of the proposed iterative algorithm, to obtain insights, as well as to demonstrate the superiority of the proposed resource allocation scheme in terms of the system computation EE by comparing it with benchmark schemes.

关键词： Mobile edge computing   Matching theory   Resource allocation   Task offloading scheduling  

摘要： Mobile edge computing (MEC) has emerged as an effective paradigm that delivers cloud services and functions to edge devices, with the objective to further enhance quality of service (QoS) of terminal users by offloading their computation-intensive tasks. In this article, a multi-user and multi-server MEC system is considered and each user can choose one MEC server to execute its computation task. We try to minimize the system delay (i.e., the maximum server delay). The problem is decomposed into task offloading problem and transmit power allocation problem which are solved by matching theory and a heuristic idea, respectively. The experimental results show that the proposed algorithm can not only obtain less delay, but also generate less energy consumption compared with the decomposed computation offloading and resource allocation algorithm, the shortest distance based scheduling algorithm and the random scheduling algorithm, especially when the data amount of tasks is the same but the workload is random. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Task analysis   Quality of service   Energy consumption   Servers   Delays   Energy harvesting   Microcell networks   Dynamic computation offloading   energy harvesting   small cell networks   mobile edge computing   POSG  

摘要： The combination of energy harvesting small cell networks (EH-SCNs) and mobile edge computing (MEC) has been considered as an effective means to improve the performance of mobile networks and provide users with a higher quality of service (QoS). In this letter, we investigate the decentralized computation offloading problem in heterogeneous EH-SCNs with MEC, where heterogeneous small cell base stations (SBSs) are rational individuals with interests to maximize their own benefits while considering their QoS requirements. Different from existing works, we address the challenge that heterogeneous SBSs may unwilling to expose their own information about the system state and offloading decisions. We formulate the problem as a partially observable stochastic game (POSG), in which SBSs can make optimal offloading decisions with imperfect state information. We analyze the local equilibrium, and propose a stochastic offloading algorithm to obtain the approximate optimal solution. Numerical results validate the effectiveness of the proposed scheme.

关键词： Mobile edge computing   computation offloading   resources allocation   power control  

摘要： In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality in such circumstances. In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12×) and reducing end device energy consumption (by up to 58%). © 2020, CC BY-NC-ND.

关键词： Task offloading   Mobile edge computing   Approximation algorithm  

摘要： With development well underway, 5G is envisioned as an enabler of lighting fast mobile services, such as virtual reality, augmented reality, live video analytics, and etc. In particular, multi-cell Mobile Edge Clouds (MEC) with 5G base stations endowed with computing capability are able to promote the Quality of Services (QoS) of mobile users by executing tasks in the edge cloud. Due to the varying 5G network conditions and limited computation capacity of each base station in the multi-cell MEC, as well as the stringent QoS requirements, a fundamental and challenging problem is how to offload user tasks to the edge cloud, such that the energy consumption of mobile devices is minimized. In this paper, we first formulate the offline and online location-aware mobile task offloading problems in a multi-cell MEC. For the offline location-aware mobile task offloading problem, we then develop an exact solution and an approximation algorithm with an approximation ratio. For the online problem, we thirdly propose a novel deep reinforcement learning-based offloading algorithm for mobile users to obtain the optimal offloading policy. We finally conduct extensive experiments by simulations to evaluate the proposed algorithms against existing benchmarks. The experimental results show that the proposed algorithms are promising and outperform the benchmark algorithms by significantly reducing energy cost of mobile devices and delays experienced by mobile users.

关键词： Mobile edge computing  

摘要： In this paper, a joint task, spectrum, and transmit power allocation problem is investigated for a wireless network in which the base stations (BSs) are equipped with mobile edge computing (MEC) servers to jointly provide computational and communication services to users. Each user can request one computational task from three types of computational tasks. Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users. This problem is formulated as an optimization problem whose goal is to minimize the maximal computational and transmission delay among all users. A multi-stack reinforcement learning (RL) algorithm is developed to solve this problem. Using the proposed algorithm, each BS can record the historical resource allocation schemes and users’ information in its multiple stacks to avoid learning the same resource allocation scheme and users’ states, thus improving the convergence speed and learning efficiency. Simulation results illustrate that the proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm. Copyright © 2020, The Authors. All rights reserved.