关键词： Cloud computing   E-health   Mobile Edge Computing   Object retrieval   Personal Health Records  

摘要： In this paper, a QR Code Identity Tag System designed for Turkish healthcare and served through the cloud is presented. The system designed is a distributed information management system as the medical data objects are geographically distributed over the system. The data objects are geographically distributed as they are placed on servers closest to the geographical location where they are most frequently required. This enables that the data is always available and the access is provided in minimum time. Additionally, to improve the performance the system employs Mobile Edge Computing technology in the form of cloudlets. The simulation results show that system performance improves as the number of cloudlets used increases and popularity threshold decreases.

关键词： Service recommendation   QoS prediction   fuzzy clustering   features learning   mobile edge computing  

摘要： In recent years, deep neural networks have achieved exciting results in a variety of tasks, and many fields try to introduce neural network techniques. In mobile edge computing, there are not many attempts that build neural network models in service recommendation or QoS (quality-of-service) prediction. The method proposed in this article is an attempt to employ neural network technique for QoS prediction. Compared to the pure use of QoS records, the exploration for context information in QoS prediction also still needs a lot of efforts. But an increasing number of features are highly likely to result in overfitting problem, especially in the case that the data size is small. To solve those problems, in this article, we propose several new techniques, including denoising auto-encoder with fuzzy clustering (DAFC) and recombination embedding network, focusing on how to use context information and how to alleviate overfitting problem. DAFC uses the denoising auto-encoder, which helps the fuzzy clustering algorithm overcome the defect that the performance is easy to be impacted by the number of clusters. Extensive experiments under different data densities show that these two network structures indeed improve the performance and reduce the overfitting problem.

关键词： cloud computing   mobile computing   stochastic processes   wireless channels   minimisation   resource allocation   power aware computing   network servers   telecommunication power management   computer network performance evaluation   multitier MEC offloading strategy   dynamic channel characteristics   cloud computing   mobile edge computing   edge servers   computation task   computing capacity   three‐   layer network architecture   mobile devices   edge cloudlets   helper cloudlet   computing data   joint communication   computation resource allocation problem   energy consumption   closed‐   form solutions   optimal offloading data size   optimisation problem   cloud execution   data transmission rate   stochastic channel condition   mobile execution   dynamic voltage and frequency scaling technique   network stability improvement   computation cost minimisation  

摘要： Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three-layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed-form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

关键词： Mobile edge computing   convex optimization   D2D communication   relay selection   resource allocation  

摘要： In order to improve resource utilization and network capacity, we propose the Device-to-Device (D2D) enabled Mobile Edge Computing (MEC) system, where multiple Smart Devices (SDs) transmit the offloading data to the MEC server with the help of wireless access point (WAP) selected from multiple WAPs. The SD uses the chosen WAP as the communication relay between the MEC server and itself. Aimed to minimize the total energy consumption of the system and satisfy the SDs demand on delay, we jointly optimize relay selection and resource allocation in D2D-enabled MEC system. The problem is formulated as an integer-mixed non-convex optimization problem which is a NP-hard problem. We thus propose a two-phase optimization algorithm that jointly optimizes relay selection policy and resource allocation strategy. In first phase, the original problem is converted into a convex optimization problem by using convex optimization techniques, and the optimal relay selection policy can be achieved by solving the relay selection problem. After obtaining the relay selection policy, the original problem is transformed into a resource allocation problem solved by leveraging the Lagrange Method in the second phase. Furthermore, the proposed algorithm is a low-complexity algorithm which is associated with the root finding method. The optimal relay selection policy and resource allocation strategy can be found in polynomial time. The extensive simulation results are provided to indicate that the D2D-enabled MEC system achieves remarkable results in energy saving. Compared with other baseline methods, our proposed algorithm can not only achieve the optimal solution with less time cost, but also improve the energy efficiency and network capacity.

关键词： Federated learning   Mobile edge computing   Deep neural network   Model partition   Differential privacy  

摘要： Federated learning is a recently proposed paradigm that presents significant advantages in privacy-preserving machine learning services. It enables the deep learning applications on mobile devices, where a deep neural network (DNN) is trained in a decentralized manner among thousands of edge clients. However, directly apply the federated learning algorithm to the mobile edge computing environment will incur unacceptable computation costs in mobile edge devices. Moreover, among the training process, frequent model parameters exchanging between participants and the central server will increase the leakage possibility of the users' sensitive training data. Aiming at reducing the heavy computation cost of DNN training on edge devices while providing strong privacy guarantees, we propose a mobile edge computing enabled federated learning framework, called FedMEC, which integrating model partition technique and differential privacy simultaneously. In FedMEC, the most complex computations can be outsourced to the edge servers by splitting a DNN model into two parts. Furthermore, we apply the differentially private data perturbation method to prevent the privacy leakage from the local model parameters, in which the updates from an edge device to the edge server is perturbed by the Laplace noise. To validate the proposed FedMEC, we conduct a series of experiments on an image classification task under the settings of federated learning. The results demonstrate the effectiveness and practicality of our FedMEC scheme.

关键词： Mobile edge computing   computation offloading   demand response   energy storage control   renewable usage  

摘要： Mobile Edge Computing (MEC) networks have recently developed to integrate renewable sources for providing sustainable and low-cost computation/energy services. However, one challenging problem is how to guarantee the stable and effective services for numerous devices under highly intermittent renewable generations and variable computation/energy demands. In this paper, we consider to co-deploy the edge servers and energy storage at micro Base Station (BS) in each edge sub-network. In the considered scenario, we first determine the number of edge servers and energy storage units at different sub-edge networks in a long-term period. Then, we jointly design the offloading and demand response policies for all micro BSs in a centralized manner. Considering the variabilities of renewable energy and computation/energy demands, the system cost is minimized while meeting the energy demand-supply balance and renewable portfolio standards. Furthermore, a distributed optimization problem is formulated to find the optimal offloading parameters, the amount of charging/discharging energy and utilized renewable energy of the micro BS without handing over all operational information. Numerical results show that the proposed deployment and policies can provide effective offloading and energy saving strategies for the proposed mobile edge network.

关键词： mobile edge computing   wireless power transmission   convex optimization   augmented Lagrangian method  

摘要： With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC-WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs' CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.

关键词： Mobile edge computing   colocation   bandwidth trading   auction  

摘要： Inspired by the shared infrastructure of colocation data centers and the growth of Mobile Edge Computing (MEC), colocation MEC businesses have thrived to offer an economical and low latency solution for MEC based tenants. In colocation MEC, a colocation service provider leases spaces at the base stations (BSs) to tenants for housing their servers. Although the tenants fully manage their own servers, they still need to purchase bandwidth from the network provider to via bandwidth purchasing market to serve their users. As a result, tenants should consider the trade-off between delay and energy consumption, which are related to bandwidth and server computing speed, respectively. In this paper, an auction framework is used to model the interaction between the network provider and tenants in the bandwidth market. The network provider determines the bandwidth amount and corresponding payment by tenants based on their submitted bids, in which the valuation of the bandwidth amount in each bid is based on the coupling between bandwidth and server computing speed. The networking provider also seeks winners in the social welfare maximization problem, which is NP-hard. To solve this problem, we propose a solution based on the randomized auction mechanism, which is proved to be approximately truthful, individually rational, and computationally efficient. Simulation results verify the effectiveness of our proposed randomized auction scheme in bandwidth trading.

关键词： Mobile Cloud Computing   Mobile Edge Computing   Computation Offloading   Stochastic Offloading   Markov   Semi-Markov   MDP   SMDP   HMM   HHMM  

摘要： Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive code or at least a part of it will be transferred to the nearby resource-rich servers. Different approaches have been proposed to help make better decisions in respect of whether, where, when, and how much to offload and to improve the efficiency of the offloading process in the literature. On the other hand, the dynamic behavior of mobile devices running on-demand applications faces the offloading to the new challenges, which could be described as stochastic behaviors. Therefore, various stochastic offloading models have been proposed in the literature. However, to the best of the author's knowledge, despite the existence of plenty of related offloading studies in the literature, there is not any systematic, comprehensive, and detailed survey paper focusing on stochastic-based offloading mechanisms. In this paper, we propose a survey paper concerning the stochastic-based offloading approaches in various computation environments such as Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Fog Computing (FC) in which to identify new mechanisms, a classical taxonomy is presented. The proposed taxonomy is classified into three main fields: Markov chain, Markov process, and Hidden Markov Models. Then, open issues and future unexplored or inadequately explored research challenges are discussed, and the survey is finally concluded.

关键词： Mobile edge computing   Internet of Vehicles   task scheduling   deep deterministic policy gradient  

摘要： Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.