关键词： Wireless power transfer   Mobile edge computing   Nonlinear EH model   Partial computation offloading   Power allocation  

摘要： This paper investigates the wireless powered mobile edge computing (WP-MEC) system, which consists of an energy transmitter (ET) with multiple antennas, an MEC server and multi wireless devices (WDs) with single antenna. The ET transfers energy to WDs via energy beamforming. Each WD harvests energy to power its operations, i.e., computing tasks locally and offloading partial tasks to the MEC server. A practical nonlinear energy harvesting (EH) model is adopted to describe the relationship between energy transmitting and harvesting. In the context of the nonlinear EH model, energy constraints between harvesting and comsumption at WDs is analyzed and an optimization problem to maximize the weighted sum computation rate of the system is formulated. The transmitting power allocation among different energy beams at the ET and computation offloading at WDs are jointly optimized to make full use of energy and computing resources. Since the nonlinear EH model is nonconvex, it is hard to solve the formulated problem. By exploiting the successive convex approximation method, an iterative optimizing scheme called JOEO (Joint Offloading and Energy Optimization) is proposed. Simulation results in the final part indicate the convergence and superiority of the JOEO scheme over other benchmark schemes under different system parameters.

关键词： Federated Learning   Distributed machine learning   Mobile edge computing   Non-IID data  

摘要： This paper presents new asynchronous methods to the Federated Learning (FL), one of the next generation paradigms for Artificial Intelligence (AI) systems. We consider the two-fold challenges lay ahead. First, non-IID (non-Independent and Identically Distributed) data across devices cause unstable performance. Second, unreliable and slow environments not only slow the convergence but also cause staleness issues. To address these challenges, this study uses a bottom-up approach for analysis and algorithm design. We first reformulate FL by unifying both synchronous and asynchronous updating schemes with an asynchrony-related parameter. We theoretically analyze this new form and find practical strategies for optimization. The key findings include: 1) a two-stage training strategy to accelerate training and reduce communication overhead;2) strategies of choosing key hyperparameters optimally for these stages to maintain efficiency and robustness. With these theoretical guarantees, we propose FedSA (Federated Staleness -Aware), a novel asynchronous federated learning algorithm. We validate FedSA on different tasks with non-IID/IID and staleness settings. Our results indicate that, given a large proportion of stale devices, the proposed algorithm presents state-of-the-art performance by outperforming existing methods on both non-IID and IID cases. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Trust assessment   Community   Mobile edge computing   Vehicular networks  

摘要： In vehicular networks, trustworthy information sharing between vehicles is an important security issue. We find that existing trust systems in vehicular networks have the disadvantages of high assessment latency and high maintenance cost. In this paper, by introducing mobile edge computing (MEC), we propose a tree-searching based trust assessment method through communities, named TTAC method, for vehicular networks. The proposed TTAC method includes two parts. First, based on information interactions, TTAC gives a direct trust assessment method by utilizing Dempster-Shafer (D-S) evidence theory. Second, with the assistance of MEC base stations, TTAC designs a tree-searching based indirect trust calculation method by utilizing two neural networks through vehicles' communities. In experiments, we use a dataset of Shenzhen taxicab traffic and simulate information interactions among vehicles. The experimental results show that TTAC method can ensure fast calculation time with high assessment accuracy in a distributed manner. Especially, in terms of the accuracy of the indirect trust assessment, the mean square error (MSE) of TTAC method is lower than that of two popular trust assessment methods, compared with 3VSL by 41.4% and with MoleTrust by 71.4%.

关键词： Mobile handsets   Resource management   Servers   Delays   Computer architecture   Routing   Cloud computing   Mobile edge computing   resource allocation   deep reinforcement learning  

摘要： The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile devices with limited resources face more severe capacity constraints than ever before. As a new concept of network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) seems to be a promising solution to meet this emerging challenge. However, MEC also has some limitations, such as the high cost of infrastructure deployment and maintenance, as well as the severe pressure that the complex and mutative edge computing environment brings to MEC servers. At this point, how to allocate computing resources and network resources rationally to satisfy the requirements of mobile devices under the changeable MEC conditions has become a great aporia. To combat this issue, we propose a smart, Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network resources adaptively, reduce the average service time and balance the use of resources under varying MEC environment. Experimental results show that the proposed DRLRA performs better than the traditional OSPF algorithm in the mutative MEC conditions.

关键词： Smart cities   Blockchain   Edge computing   Security   Task analysis   Quality of service   Load management   Consortium blockchain   Internet of Vehicles   mobile edge computing   smart city  

摘要： In a smart city, Mobile Edge Computing (MEC) are generally deployed in static fashion in base stations (BSs). While moving vehicles with advanced on-board equipment can be regarded as dynamic computing resource transporters ignoring geographical limitations. Thus Internet of Vehicle (IoV) could assist the smart city to achieve flexible computing resource demand response (DR) via paid sharing the idle vehicle computing resources. Motivated by this, we propose a Peer-to-Peer (P2P) computing resource trading system to balance computing resource spatio-temporal dynamic demands in IoV-assisted smart city. On one hand, to guarantee transaction security and privacy-preserving in our system, we employ a consortium blockchain approach and demonstrate the process of secure computing resource trading without involving a centralized trusted third-party. On the other hand, to encourage individual smart vehicles to participate in our system, we construct a two-stage Stackelberg game jointly optimizing the utilities of buyers and sellers. And we also derive the optimal computing pricing and trading amount strategies in this proposed game. Finally, security analysis shows the security performance of our system and numerical simulations show that our strategies can encourage the collaboration between the buyer and smart vehicles.

关键词： Electric Vehicle   transportation management   V2V communication   mobile edge computing  

摘要： As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs' reservations, concerning the massive signalling exchange cost. The Global Controller ( GC) is positioned as cloud server to gather BSSs (service providers) status and EVs' reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs).

关键词： Cloud computing   mobile edge computing   QoS   monitoring   Bayesian classifier   K-nearest neighbor  

摘要： Mobile edge computing is a new computing paradigm that performs computing on the edge of a network. It provides services to users by deploying edge servers near mobile devices. Services may be unavailable or do not satisfy the needs of users due to changing edge environments. Quality of service (QoS) is commonly employed as a critical means to indicate qualitative status of services. It is particularly important to monitor QoS of services timely and effectively in the mobile edge environment. However, user mobility and dependencies among QoS values often cause the monitoring results to deviate from the real results in the mobile edge environment. Existing QoS monitoring approaches have not taken into account these problems. To address the problems, this article proposes ghBSRM-MEC (Gaussian hidden BayeSian Runtime Monitoring for Mobile Edge Computing), a novel mobility and dependence-aware QoS monitoring approach for the mobile edge environment. This approach assumes that the QoS attribute values of edge servers obey Gaussian distribution. It constructs a parent property for each property, thus reducing the dependence between properties. During the training stage, a Gaussian Hidden Bayesian classifier is constructed for each edge server. During the monitoring stage, combining with a KNN algorithm, the classifier is changed dynamically based on user mobility to realize QoS monitoring in the mobile edge environment. The experimental results validate the feasibility, effectiveness, and efficiency of ghBSRM-MEC.

关键词： Task analysis   Resource management   Servers   Optimization   Minimization   Energy consumption   Cloud computing   Energy consumption   deep Q network   latency   mobile edge computing   non-convex optimization   offloading   resource allocation  

摘要： We consider a multi-user multi-server mobile edge computing (MEC) network with time-varying fading channels and formulate an offloading decision and resource allocation problem. To solve this mixed-integer non-convex problem, we propose two hybrid approaches that learn offloading strategy with DQN (opt-DQN) or Q-table (opt-QL) at each user equipment (UE). The communication resources are allocated with an optimization algorithm at each computational access point (CAP). We also propose a pure DQN method that learns both the offloading strategy and resource allocation via Q-learning (QL). We analyze the convergence behavior of the QL-based algorithms from a game-theoretical perspective and demonstrate the performance of the proposed hybrid approaches for different network sizes. The simulation results show that the hybrid approaches reach lower costs than other baseline algorithms and the pure-DQN approach. Moreover, the performance of the pure-DQN approach degrades severely as the network size increases, while opt-DQN still performs the best, followed by opt-QL. These observations demonstrate that the hybrid approach that combines the advantages of both QL and convex optimization is a promising design for a multi-user MEC network, wherein complicated offloading and resource allocation strategies need to be determined in a timely and accurate fashion.

关键词： mobile edge computing   resource allocation   computing offloading   data caching   optimization  

摘要： Mobile terminal applications with high computing complexity and high time delay sensitivity are developing quite fast today, which aggravates the load of mobile cloud computing and storage and further leads to network congestion and service quality decline. Mobile edge computing (MEC) is a way of breaking through the limits of computing and storage resources of mobile cloud and alleviating the load of mobile cloud. Computing time costs and transmission time costs are considered to be the main issues for the mobile cloud when carrying out computing offloading and data caching. Therefore, an efficient resource management strategy, which could minimize the system delay, is proposed in this paper. The new scheme offloads reasonably computing tasks and caches the tasks' data from the mobile cloud to mobile edge computing-enabled base stations. An intelligence algorithm, genetic algorithm, is being used to solve the global optimization problem which would cause transmission delay and computing resources occupation, and to determine the computing offloading and data caching probability. The simulation of the system using MATLAB is conducted in 8 different scenarios with different parameters. The results show that our new scheme improves the system computing speed and optimizes the user experience in all scenarios, compared with the scheme without data caching and the scheme without computing offloading and data caching.

关键词： Computation offloading   Quality of service   Mobile edge computing   Deep reinforcement learning   Privacy preservation  

摘要： Owing to the insufficient processing ability of wireless devices (WDs), it is difficult for WDs to process these data within the deadline associated with the quality of service requirements. Offloading computation tasks (workloads) to emerging mobile edge computing servers with small or macro base stations is an effective and feasible solution. However, the offloaded data will be fully exposed and vulnerable to security threats. In this paper, we introduce a wireless communication and computation model of partial computation offloading and resource allocation considering the time-varying channel state, the bandwidth constraint, the stochastic arrival of workloads, and privacy preservation. To simultaneously optimize the computation and execution delays, the power consumption, and the bandwidth resources, we model the optimization problem as a Markov decision process (MDP) to minimize the weighted sum cost of the system. Owing to the difficult problems of lack of priori knowledge and the curse of dimensionality, we propose a decentralized optimization scheme on partial computation offloading and resource allocation based on deep reinforcement learning (DOCRRL). According to the time-varying channel state, the arrival rate of computation workloads, and the signal-to-interference-plus-noise ratio, the DOCRRL algorithm can learn the optimal policy for decision-making under stringent latency and risk constraints that prevent the curse of dimensionality from arising owing to the high-dimensional action space and state space. The numerical results reveal that DOCRRL can explore and learn the optimal decision-making policy without priori knowledge;it outperforms four baseline schemes in simulation environments.