关键词： framework   mobile cloud computing   mobile edge computing   offloading  

摘要： With mobile devices' limited resources, computing the increasing amount of data locally puts strain on their batteries, processors, storage and bandwidth making it necessary to offload computeheavy tasks to a close proximity edge cloud server. A research field known as Mobile Cloud Computing (MCC) has emerged to address the shortcomings of mobile devices by means of offloading. However, cost of operation and maintenance of cloud servers alongside the mobile nature of devices that could potentially cause connectivity issues are some of the most prominent challenges that face an efficient offloading process.

关键词： Mobile edge computing   unmanned aerial vehicle ( UAV)   unloading decision   joint optimization problem   USS algorithm  

摘要： Due to the birth of various new Internet of Things devices, the rapid increase of users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Therefore, we consider an unmanned aerial vehicle (UAV)-assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an UAV. A UAV, as a relay node, can provide users with extensive communications and certain computing capabilities. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources to minimize overall energy consumption and costs of computing andmaximum delay. To solve the joint optimization problem, we propose an efficient USS algorithm, which includes a UAV position optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user tasks compared with four other unloading decisions, such as traditional cloud computing, which uses only the edge server.

关键词： Wireless communication   Real-time systems   Wireless sensor networks   Servers   Energy consumption   Uplink   Renewable energy sources   SWIPT   mobile edge computing   real-time energy trading   wireless powered MEC system  

摘要： In this brief, we consider a wireless powered mobile edge computing (MEC) system, where the base station (BS) is integrated with a MEC server to support computation offloading. Meanwhile, the BS is equipped with renewable energy resource and can transfer energy to the wireless devices (WDs) and trade energy with the grid. The objective is to minimize the total energy cost of the operator under the offloading delay constraint by jointly optimizing the BS's spot-market energy demand and transmit power as well as users' offloading data size and transmit power. Both the perfect and imperfect channel state information (CSI) are considered. For the former, we propose a low computational-complexity algorithm for solving the formulated problem. For the latter, the non-convex problem is transformed into a convex one by using the successive convex approximation technique, and then an iterative algorithm is proposed. Simulation results verify the effectiveness of our proposed algorithms.

关键词： Blockchains   Vehicular ad hoc networks   Task analysis   Reliability   Computational modeling   Security   Markov processes   Vehicular ad hoc networks   mobile edge computing   blockchain   Markov decision process  

摘要： Vehicular ad hoc networks (VANETs) have become an important branch of future 6G smart wireless communications. As an emerging key technology, multi-access edge computing (MEC) provides low-latency, high-speed, and high-capacity network services for the VANETs. In this paper, we propose a novel framework for blockchain-based, hierarchical multi-access edge computing for the future VANET ecosystem (BMEC-FV). In the underlying VANET environment, we propose a trust model to ensure the security of the communication link between vehicles. Multiple MEC servers calculate the trust between vehicles through computing offloading. Meanwhile, the blockchain system plays an important role to manage the entire BMEC-FV architecture. We aim to optimize the throughput and the quality of services (QoS) for MEC users in the lower layer of the system architecture. In this framework, the main challenge is how to effectively reach consensus among blockchain nodes while ensuring the performance of MEC systems and blockchains. The blocksize of blockchain nodes, the number of consensus nodes, reliable features of each vehicle, and the number of producing blocks for each block producer are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Since it is difficult for this to be solved by traditional methods, we propose a novel deep compressed neural network scheme. Simulation results illustrate the superiority of the BMEC-FV ecosystem.

关键词： Optimization   Radio frequency   Millimeter wave communication   Resource management   Wireless communication   Simulation   Array signal processing   Millimeter-wave communication   mobile edge computing   reconfigurable intelligent surface   computation efficiency   hybrid beamforming  

摘要： In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

关键词： Dynamic searchable encryption   Mobile edge computing   Oblivious RAM  

摘要： In recent years, edge-cloud computing is regarded as a promising solution to meet the requirements of mobile computing and Internet-of-Things (IoT). However, due to the limited storage resources of edge equipment, there is a security threat when users outsource their sensitive data to the cloud computing center. The users usually adopt a data-encryption approach, Oblivious RAM (ORAM), which enables a user to read/write her outsourced private data without access pattern leakage. Not all users like the fully functional ORAM all the time since the ORAM protocol is usually highly interactive or occupies large edge storage space. We show that forward-private/backward-private (FP/BP) ORAMs are good alternatives for secure storage outsourcing. We introduce the FP/BP-ORAM definitions and present LL-ORAM, the first FP/BP-ORAM that achieves near-zero edge storage, single-round-trip read/write, and worst-case sublinear access time. For any outsourced record, LL-ORAM provides both an oblivious-access interface and a nonoblivious-access interface. FP-ORAM concerns more data-write privacy than data-read privacy. BP-ORAM concerns more data-read privacy. The constructions involve a tree data structure named LL-tree, which supports fast computation in the cloud with an access-pattern-reduced leakage profile. The security analysis shows that LL-ORAM meets the proposed forward and backward security model. The experimental results demonstrate that LL-ORAM is round-efficient and can be deployed on edge-cloud computing systems. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Channel estimation   Signal processing algorithms   Convolutional neural networks   Encoding   Deep learning   Training   Frequency-domain analysis   V2X   mobile edge computing   channel estimation   deep learning   edge intelligence  

摘要： The fifth generation New Radio vehicle-to-everything (5G NR-V2X) has higher requirements for delay and reliability, which brings challenges to the physical layer signal processing. Mobile edge computing (MEC) can provide larger capacity data storage and more efficient computation through localization of on-board unit (OBU) and next generation Node B (gNB) services. This paper combines the channel estimation of New Radio vehicle-to-infrastructure (NR-V2I) communication baseband signal processing with MEC and designs an intelligent channel estimation framework. In the MEC server, this paper proposes a channel estimation algorithm based on deep learning. This algorithm uses a one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) for time-domain state prediction. Additional velocity coding vector and multipath coding vector track changes in the environment, and accurately train channel data in different mobile environments. System simulation and analysis show that the proposed algorithm improves the channel estimation accuracy, reduces the bit error rate, and enhances the robustness compared with the representative channel estimation algorithms for NR-V2I.

关键词： Mobile edge computing  

摘要： With the rapid development of Internet of Things technology and interactive applications, the number of terminal devices in the network is increasing, and the development of interactive applications is hindered by network delay. To solve the network delay, bandwidth, and workload requirements in the new era, edge computing came into being. Edge computing aims to implement computing, storage, communication, and other services at the edge of the network by sinking cloud services from the core network to the edge of the network. Current research studies pay less attention to the impact of edge server location on the system performance, and edge server deployment is one of the key technologies for mobile edge computing. Therefore, we take 5G macrocellular/microcellular cluster as the edge server deployment scenario, propose an equivalent bandwidth-based deployment strategy, establish a mathematical model for edge server deployment, and contract a task experience function as an evaluation index from two aspects: task time and energy overhead. Based on the analysis of the experimental results, it is verified that the deployment strategy based on equivalent bandwidth is superior to other deployment strategies in terms of terminal device task overhead.

关键词： mobile edge computing   low latency   deep reinforcement learning   partial offloading   resource allocation  

摘要： In the dense data communication environment of 5G wireless networks, with the dramatic increase in the amount of request computation tasks generated by intelligent wireless mobile nodes, its computation ability cannot meet the requirements of low latency and high reliability. Mobile edge computing (MEC) can utilize its servers with mighty computation power and closer to tackle the computation tasks offloaded by the wireless node (WN). The physical location of the MEC server is closer to WN, thereby meeting the requirements of low latency and high reliability. In this paper, we implement an MEC framework with multiple WNs and multiple MEC servers, which consider the randomness and divisibility of arrival request tasks from WN, the time-varying channel state between WN and MEC server, and different priorities of tasks. In the proposed MEC system, we present a decentralized multi-agent deep reinforcement learning-based partial task offloading and resource allocation algorithm (DeMADRL) to minimize the long-term weighted cost including delay cost and bandwidth cost. DeMADRL is a model-free scheme based on Double Deep Q-Learning (DDQN) and can obtain the optimal computation offloading and bandwidth allocation decision-making policy by training the neural networks. The comprehensive simulation results show that the proposed DeMADRL optimization scheme has a nice convergence and outperforms the other three baseline algorithms.

关键词： Task analysis   Delays   Cloud computing   Energy consumption   Computational modeling   Heuristic algorithms   Game theory   Computation offloading   mobile edge computing   game theory   task migration   I2I  

摘要： Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.