关键词： Fog computing   Edge computing   Cloud computing   Internet of things (IoT)   Cloudlet   Mobile edge computing   Multi-access edge computing   Mist computing  

摘要： With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

关键词： Mobile edge computing   Wireless energy transfer   Multi-user offloading   ADMM  

摘要： In this paper, a wireless powered mobile edge computing (WP-MEC) system is considered, in which a hybrid access point integrated with MEC servers can charge N wireless devices (WDs) by broadcasting radio-frequency signals, and the time division multiple access (TDMA) protocol is used for task offloading of WDs. The goal of this paper is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. To solve this optimization problem, an alternating direction multiplier method (ADMM) based distributed optimization method is proposed. The proposed method can decompose the optimization problem into N sub-problems, which are solved by N WDs. Experimental results show that the proposed method outperforms the benchmarks and greatly increases the weighted sum computation rate while keeping the energy consumption at a low level under the premise of time complexity O(N).

关键词： Cloud computing   Task analysis   Wireless communication   Servers   Mobile handsets   Energy harvesting   Wireless sensor networks   Mobile edge computing   computation offloading   energy harvesting   multi-user mult-task scheduling  

摘要： Mobile Edge Cloud Computing (MECC) has becoming an attractive solution for augmenting the computing and storage capacity of Mobile Devices (MDs) by exploiting the available resources at the network edge. In this work, we consider computation offloading at the mobile edge cloud that is composed of a set of Wireless Devices (WDs), and each WD has an energy harvesting equipment to collect renewable energy from the environment. Moreover, multiple MDs intend to offload their tasks to the mobile edge cloud simultaneously. We first formulate the multi-user multi-task computation offloading problem for green MECC, and use Lyaponuv Optimization Approach to determine the energy harvesting policy: how much energy to be harvested at each WD;and the task offloading schedule: the set of computation offloading requests to be admitted into the mobile edge cloud, the set of WDs assigned to each admitted offloading request, and how much workload to be processed at the assigned WDs. We then prove that the task offloading scheduling problem is NP-hard, and introduce centralized and distributed Greedy Maximal Scheduling algorithms to resolve the problem efficiently. Performance bounds of the proposed schemes are also discussed. Extensive evaluations are conducted to test the performance of the proposed algorithms.

关键词： Machine learning   mobile edge computing   stochastic gradient descent  

摘要： Consider a device that is connected to an edge processor via a communication channel. The device holds local data that is to be offloaded to the edge processor so as to train a machine learning model, e.g., for regression or classification. Transmission of the data to the learning processor, as well as training based on stochastic gradient descent (SGD), must be both completed within a time limit. Assuming that communication and computation can be pipelined, this letter investigates the optimal choice for the packet payload size, given the overhead of each data packet transmission and the ratio between the computation and the communication rates. This amounts to a tradeoff between bias and variance, since communicating the entire data set first reduces the bias of the training process but it may not leave sufficient time for learning. Analytical bounds on the expected optimality gap are derived so as to enable an effective optimization, which is validated in numerical results.

关键词： Mobile edge computing   UAV trajectory   bit allocation   power allocation   resource partitioning   TD-UAV scheduling  

摘要： In this paper, we employ the unmanned aerial vehicle (UAV) as a flying base station (BS) to process the application tasks migrated from the terminal devices (TDs) for saving TDs' energy consumption. To tackle the huge volume of data at TDs, we propose a resource partitioning strategy scheme where one portion of bits at TD is computed locally and the remaining portion is transmitted to UAV for computing. Our goal is to minimize the total energy consumption of TDs by jointly optimizing bit allocation, resource partitioning, power allocation at TDs/UAV, TD-UAV scheduling, and UAV trajectory with One-by-One access scheme. Due to the non-convexity of the original problem with mixed integer variables, we decompose the problem into two sub-problems. Specifically, in the first sub-problem, the TD-UAV scheduling is obtained by solving dual problem with given UAV trajectory. In the second sub-problem, the UAV trajectory is obtained by using successive convex optimization techniques with given TD-UAV scheduling. Then, an iterative algorithm is proposed to optimize the TD-UAV scheduling and UAV trajectory alternately. Numerical results are provided to demonstrate the superiority of our proposed scheme over the benchmarks.

关键词： Unmanned aerial vehicle-enabled communication   mobile edge computing   resource allocation   user association   location optimization  

摘要： In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

关键词： Servers   Heuristic algorithms   Approximation algorithms   Edge computing   Computer architecture   Computational modeling   Optimization   Mobile edge computing   optimization modeling   placement   approximation algorithm  

摘要： The Mobile Edge Network (MEN) has emerged as the basic infrastructure to support fifth-generation networks, mobile edge computing and fog computing. The characteristics of mobility must be addressed to guarantee the quality of service in MENs. As one of the critical problems in MENs, flexible deployment plays a part in exploiting edge networks. Despite the abundance of recently proposed strategies, most concentrate on the change in user demands and inevitably ignore the influence for the user mobility, which is common in future networks. We propose Provision for Mobile Edge Computing (PMEC), a prototype that takes advantage of storage devices with flexible placement. In PMEC, we accommodate various considerations and select different storage devices to cache, deploying the cache with the relationship of a two-tiered structure in the MEN. Thus, we construct a flexible overlay network with the objective of minimizing the cost in a two-tiered edge network. Based on the analysis of the problem, we solve the two-tiered placement from bottom to top using the dynamic minimal spanning tree (MST) algorithm and design two algorithms for each tier including the basic algorithm and the improved algorithms. The simulation is conducted on realistic data to demonstrate the performance of our algorithms.

关键词： Mobile edge computing  

摘要： This paper studies a novel user cooperation model in a wireless powered mobile edge computing system where two wireless users harvest wireless power transferred by one energy node and can offload part of their computation tasks to an edge server (ES) for remote execution. In particular, we consider that the direct communication link between one user to the ES is blocked, such that the other user acts as a relay to forward its offloading data to the server. Meanwhile, instead of forwarding all the received task data, we also allow the helping user to compute part of the received task locally to reduce the potentially high energy and time cost on task offloading to the ES. Our aim is to maximize the amount of data that can be processed within a given time frame of the two users by jointly optimizing the amount of task data computed at each device (users and ES), the system time allocation, the transmit power and CPU frequency of the users. We propose an efficient method to find the optimal solution and show that the proposed user cooperation can effectively enhance the computation performance of the system compared to other representative benchmark methods under different scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： mobile edge computing   IoT   RF Fingerprinting   authentication  

摘要： In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning algorithms for recognition are performed by the remote cloud. By this means, the authentication rate can be improved by taking advantage of the machine-learning training methods and computing resource support of the cloud. Extensive simulations are performed under the IoT application scenario. The results show that the novel method can achieve higher recognition rate than that of traditional RFFID method by using wavelet feature effectively, which demonstrates the efficiency of our proposed method.

关键词： traffic sensing   bandwidth-aware   vehicular networks   mobile edge computing   traffic state estimation  

摘要： Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, we formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, we first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, we propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an l2-norm minimization problem. Last but not the least, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.