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： 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.

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： Computation offloading   Mobile cloud computing   Mobile edge computing   Cloudlet   Lagrangian decomposition   Offloading middleware  

摘要： In this paper, we focus on tasks offloading over two tiered mobile edge computing environment. We consider several users with energy constrained tasks that can be offloaded over edge clouds (cloudlets) or on a remote cloud with differentiated system and network resources capacities. We investigate offloading policy that decides which tasks should be offloaded and determine the offloading location on the cloudlets or on the cloud. The objective is to minimize the total energy consumed by the users. We formulate this problem as a Non-Linear Binary Integer Programming. Since the centralized optimal solution is NP-hard, we propose a distributed linear relaxation heuristic based on Lagrangian decomposition approach. To solve the subproblems, we also propose a greedy heuristic that computes the best cloudlet selection and bandwidth allocation following tasks' energy consumption. We compared our proposal against existing approaches under different system parameters (CPU resources), variable number of users and for six applications, each having specific traffic pattern, resource demands and time constraints. Numerical results show that our proposal outperforms existing approaches. In addition to the theoretical approach, we evaluate our offloading policy using real experiments. In this case, we setup a real testbed composed of client terminal, offloading server located either at the edge or at a remote Cloud. We also implemented our proposal as an offloading middleware on both the client and the offloading server. Using this testbed, we were able to evaluate our offloading decision policy for multi-users context with three real Android OS applications, with different traffic patterns and resource demands. We also discuss the performance of our proposal for each application and we analyze the multi-users effect.

关键词： Mobile edge computing   Computation offloading   Time consumption   Multi-objective   Energy consumption   Cost  

摘要： Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy- and cost-aware for workflow applications in MEC.

关键词： Authentication   IoT   Mobile edge computing   RF Fingerprinting  

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. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Bandwidth-aware   Mobile edge computing   Traffic sensing   Traffic state estimation   Vehicular networks  

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. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Mobile edge computing   Edge computing   Cloudlets   Fog computing   Micro clouds   Cloud computing  

摘要： In recent years, the Edge computing paradigm has gained considerable popularity in academic and industrial circles. It serves as a key enabler for many future technologies like 5G, Internet of Things (IoT), augmented reality and vehicle-to-vehicle communications by connecting cloud computing facilities and services to the end users. The Edge computing paradigm provides low latency, mobility, and location awareness support to delay-sensitive applications. Significant research has been carried out in the area of Edge computing, which is reviewed in terms of latest developments such as Mobile Edge Computing, Cloudlet, and Fog computing, resulting in providing researchers with more insight into the existing solutions and future applications. This article is meant to serve as a comprehensive survey of recent advancements in Edge computing highlighting the core applications. It also discusses the importance of Edge computing in real life scenarios where response time constitutes the fundamental requirement for many applications. The article concludes with identifying the requirements and discuss open research challenges in Edge computing. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Collaborative computation offloading   computation resource allocation   mobile edge computing   vehicular networks  

摘要： Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

关键词： Mobile edge computing   Workflow scheduling   Security modeling   Energy efficient   Genetic algorithm (GA)  

摘要： It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection. Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications. (C) 2019 Elsevier B.V. All rights reserved.