关键词： differential privacy   Hilbert curve   location-based service   mobile edge computing   privacy aware   quadtree  

摘要： In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D,epsilon) - LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%.

关键词： Mobile edge computing  

摘要： By allowing a mobile device to offload computation intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, which optimizes the amount of data for offloading to minimize the total expected energy consumption of the mobile device. Although the formulated optimization problem is non-convex, we prove that the objective function of the problem is piecewise convex, and accordingly develop an optimal solution for the problem. Numerical results verify the correctness of our findings and the effectiveness of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： —Consider a mobile edge computing system in which users wish to obtain the result of a linear inference operation on locally measured input data. Unlike the offloaded input data, the model weight matrix is distributed across wireless Edge Nodes (ENs). ENs have non-deterministic computing times, and they can transmit any shared computed output back to the users cooperatively. This letter investigates the potential advantages obtained by coding model information prior to ENs’ storage. Through an information-theoretic analysis, it is concluded that, while generally limiting cooperation opportunities, coding is instrumental in reducing the overall computation-plus-communication latency. Copyright © 2019, The Authors. All rights reserved.

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

摘要： The combination of mobile edge computing (MEC) and wireless energy transfer (WET) has been recognized as a prominent solution to liberate massive wireless devices from limited battery capacities and compute-intensive workloads in the Internet of Things (IoT) environment. This new paradigm is called wireless powered mobile edge computing (WP-MEC). In this paper, an online computation rate maximization (OCRM) algorithm for multi-user WP-MEC systems is proposed by jointly managing the radio, computational resources, allocating time for energy transfer and data transmission. In each time block, the optimal bandwidth allocation and data transmit powers for offloading are achieved by Bisection method with linear time complexity. In addition, the closed-form solutions of optimal time allocation and CPU-cycle frequencies of wireless devices are obtained. The theoretical performance analysis shows that the time average sum computation rate and task queue backlog obey an [O(1/V), O(V)] tradeoff. Numerical results verify that, our proposed OCRM algorithm outperforms the baseline schemes without joint optimization, which dramatically improves the computation rate while making the system stable.

关键词： Mobile edge computing   computation offloading   resource allocation   deep reinforcement learning  

摘要： To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia *** paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making *** wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading *** mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its *** state spaces are high-dimensional in our work and value function is unrealistic to ***,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches *** results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.

关键词： Mobile edge computing   dynamic content caching   popularity prediction   location awareness  

摘要： Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an H-infinity filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.

关键词： Mobile edge computing   dynamic pricing strategy   autonomous vehicle   Markov chain  

摘要： The idle computing resources of parked vehicles could be utilized to improve performance by assisting task executions in mobile edge computing (MEC) systems. As a result, the owner of a vehicle could be compensated, resulting in a win-win situation. A dynamic pricing strategy is proposed to minimize the average cost of the MEC system under the constraints on quality of service by adjusting the price constantly based on the current system state. To do so, a cost minimization problem is solved to obtain the optimal dynamic pricing strategy efficiently. Finally, the optimization results are validated with extensive simulations.

关键词： Mobile edge computing   Medical healthcare   QoS   Window-based rate control algorithm   BSA  

摘要： Emerging trends in mobile edge computing for developing the efficient healthcare application such as, remote monitoring of the patients with central electronics clouds (e-Clouds) and their increasing voluminous multimedia have caught the attention of everyone in industry and academia. So, clear visualization, big sensing level, and better quality of service (QoS) is the foremost priority. This paper proposes the window-based Rate Control Algorithm (w-RCA) to optimize the medical quality of service (m-QoS) in the mobile edge computing based healthcare by considering the network parameters for instance, peak-to-mean ratio (PMR), standard deviation (***), delay and jitter during 8 min medical video stream named "Navigation to the Uterine Horn, transection of the horn and re-anastomosis' transmission over 5 G networks. The performance of the proposed w-RCA is evaluated and compared with the conventional battery smoothing algorithm (BSA) and Baseline by using MPEG-4 encoder for optimizing m-QoS at the source or the server side. The experimental results demonstrate that the w-RCA outperforms the BSA and Baseline by optimizing QoS in remote healthcare application i.e., Telesurgery. Besides, it is observed and analyzed that w-RCA produces better and effective results at small buffer and window sizes unlike BSA and Baseline by adopting large buffer size during QoS optimization.

关键词： Internet of Things   live migration   mobile edge computing   network overhead   user mobility   virtual machine  

摘要： With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms (M-All and M-Edge), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight-based algorithm (M-Weight) and a mobility prediction-based heuristic algorithm (M-Predict) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility-induced VM migration in mobile edge computing environments.

关键词： Internet of Things   Mobile edge computing   Cloud computing   Consensus  

摘要： Mobile Edge Computing (MEC) is an emerging technology that enables computing directly at the edge of the cloud computing network. Therefore, it is important that MEC is applied with reliable transmission. The problem of reaching consensus in the distributed system is one of the most important issues in designing a reliable transmission network. However, all previous protocols for the consensus problem are not suitable for an MEC paradigm. It is the first time an optimal protocol of reaching consensus is proposed for MEC paradigm. The protocol makes all fault-free nodes communicate with each other and collect the exchanged messages to decide a common value. Based on the common value, the protocol ensures all fault-free nodes reach consensus without the influence of unreliable transmission. Finally, we proved theoretically that the proposed protocol can tolerate the maximum number of faulty components and using only two rounds of message exchanges.