关键词： Task analysis   Interchannel interference   System performance   Probability   Power system reliability   Energy consumption   Wireless networks   Mobile edge computing   cochannel interference   outage probability   task offloading  

摘要： In this paper, we investigate a mobile edge computing (MEC) network, where the user has some computational tasks to be assisted by multiple computational access points (CAPs) through offloading. We consider practical communication scenarios with limited spectrum resources, and the cochannel interference arising from the aggressive reuse of frequency severely degrades the system offloading performance. To enhance the system performance, we provide three CAP selection criteria to choose one best CAP among multiple ones. Specifically, criterion I maximizes the computational capability at the CAP, criterion II minimizes the interfering power, while criterion III maximizes the instantaneous channel gain of data link. In time-varying channel environments, the CAP selection may be outdated, which deteriorates the system performance. For the three criteria, we evaluate the system outage probability in the outdated channel state information (CSI) by taking into account the latency, energy consumption and data rate, and provide the analytical and asymptotic expressions of outage probability, from which we obtain some critical insights on the system design. Simulation results are finally demonstrated to verify the proposed studies. In particular, criterion III under the perfect CSI can achieve the system whole diversity order coming from multiple CAPs.

关键词： Link selection   Mobile edge computing   Quality of service   Vehicular networks  

摘要： Due to the roadside unit can not fully achieve seamless coverage and the dynamic change of network topology in vehicular networks, problems such as the interruption of vehicle service, poor stability of communication link, and high network delay will be caused. To solve these problems, this paper proposes a codirectional multilane adaptive message return (CMAMR) strategy based on edge computing. Firstly, we establish a CMAMR communication model. Then, according to the movement trajectory of the receiving node, we propose a single-hop and a multi-hop message return link strategy. The former combines the single-hop link quality and node location information to construct a node selection function to assist the head node in selecting an optimal forwarding node to form a single-hop message return link. The latter assists the RSU to select the optimal multi-hop message return link in the same lane by establishing a link adaptive quality evaluation function, thus improving the user's quality of server. The simulation results show that the link network delay of the CMAMR strategy is 14.6%, 42.86%, and 38.47% lower than those of the random with forwarding progress (RFP), most forward with fixed radius (MFR), and traditional vehicle-to-infrastructure and vehicle-to-vehicle cooperative communication (TVCC) message return strategies, respectively;correspondingly, the link reliability is at least 9.13% higher for the CMAMR strategy.

关键词： Aerial-ground cooperative vehicular network   mobile edge computing   wireless power transmission   unmanned aerial vehicle   convex optimization  

摘要： Connected and autonomous vehicles (CAVs) are recently- envisioned to provide a tremendous social impact, while they put forward a much higher requirement for both vehicular communication and computation capacities to process resource-intensive applications. In this paper, we study unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) for a platoon of wireless power transmission (WPT)-enabled vehicles. Our objective is to maximize the system-wide computation capacity under both communication and computation resource constraints. We incorporate the coupled effects of the platooning vehicles and the flying UAV, air-to-ground (A2G) and groundto-air (G2A) communications, onboard computing and energy harvesting into a joint scheduling optimization model of communication and computation resources. To tackle the resulting optimization problem, we propose a successive convex programming method based on a second-order convex approximation, in which feasible search directions are obtained by solving a sequence of quadratic programming subproblems and used to generate feasible points that can approach a local optimum. We also theoretically prove the feasibility and convergence of the proposed method. Moreover, simulation results are provided to validate the effectiveness of our proposed method and demonstrate its superior performance over other conventional schemes.

关键词： Edge computing   fog computing   mobile edge computing   cloudlet   cyberphysical systems   trustworthiness   safety   security   predictability   dependability   critical systems  

摘要： Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking-with corresponding multiple interpretations of "edge computing" (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS incl

关键词： Task analysis   Subspace constraints   Relays   Servers   Delays   Computer architecture   Ad hoc networks   Mobile edge computing   buffer-aided relaying   task offloading   multi-convex optimization  

摘要： This letter studies hierarchical Mobile Edge Computing (MEC) with multiple sources, multiple buffer-aided relays, and one Higher-level computing Node (HN). The relays may or may not have computation resources and therefore, they randomly decide to process the received tasks or to offload them to the HN. We consider the delay in the transmission and computation buffers, and propose a novel Fairness and Efficiency Cost Function (FECF) as the weighted sum of the maximum Average Response Time (ART) and the overall system ART. We formulate a novel problem to minimize the FECF while keeping the system queues stable, by jointly optimizing the offloading probabilities at the relays and the capacities of the virtual machines at the HN. We prove that the problem is a multi-convex optimization problem and propose an effective solution method by alternating optimization. Simulation results demonstrate the effectiveness of the different variants of the proposed scheme in the scenarios with server-less and server-empowered relays and provide important insights regarding the fairness and efficiency in terms of the ART.

关键词： Mobile edge computing  

摘要： The rapid growth of the Internet of Vehicles applications leads to unreasonable resource allocation, task computing delay, and large energy consumption. To solve this problem, this paper proposes a computing resource allocation strategy based on mobile edge computing in the Internet of Vehicles environment. Firstly, we analyze the process of mobile edge computing network and resource allocation. Then, it is improved by introducing the Halton sequence into the traditional genetic algorithm, and the difference between populations is reduced by canceling the randomness to generate an initial population with smaller individual differences. Furthermore, by optimizing the parameter setting method of traditional genetic algorithm, the crossover probability and mutation probability that change dynamically with fitness value are given, which improves the accuracy of the algorithm. Finally, the simulation results show that under the premise of the same number of tasks, the average delay and total cost of the proposed strategy are also the smallest. When the number of tasks is 45, the average delay of the proposed method is 0.32 s and the total cost is 0.34, which are better than the comparison method. Simulation results show that the improved algorithm has more advantages in delay and overhead.

关键词： Industrial internet of things   Mobile edge computing   Probabilistic forwarding   Network coding   Wireless communication  

摘要： Mobile edge computing (MEC) is considered as a key enabler for the industrial internet of things (IIoT) to cope with the ever-increasing communication and computing demands of nodes. In consideration of the limited resource of the IIoT nodes, it is necessary to design cost-effective multi-hop data transmission schemes for mobile edge IIoTs. However, most of the traditional schemes have to spend enormous cost to meet the reliability requirement, which cannot support timely information processing of MEC-based IIoT. In this article, the probabilistic cooperative coded forwarding (PCCF) scheme for multi-hop data transmission in mobile edge IIoTs is proposed to address the above problem. First, the data packets are encoded at a source IIoT node using the systematic sparse network coding (SSNC) mechanism, then the source broadcasts the coded packets to its one-hop neighbors. To minimize the required number of redundant coded packets, the sparsity of coded packets is optimized. Second, the nodes which received the packets will become volunteer relay nodes and forward the coded packets using the cooperative coded forwarding (CCF) mechanism. The volunteer relays first forward the received coded packets with a forwarding probability, and then re-encode a pair of received coded packets and broadcast the re-encoded packet with a re-encoding probability. To guarantee the broadcast performance while minimizing the transmission number at relay nodes, the feasible forwarding and re-encoding probability are provided. Third, the receiver nodes will try to decode the received coded and re-encoded packets and recover data packets without sending acknowledgments. Finally, through a series of experiments, we verify the accuracy of analytical approximations and also find out the optimal sparsity of coded packets and the existence of minimum transmission numbers. These provide insights for further optimization of multi-hop data transmission in mobile edge IIoTs.

关键词： Mobile edge computing  

摘要： Mobile-edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min-max latency optimization problem by jointly designing multi-user detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ transmit power and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original non-convex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation. © 2022, CC BY.

关键词： Mobile edge computing  

摘要： Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable intelligence. We consider a compute platform consisting of a limited number of highly-resourced UAVs that act as mobile edge computing (MEC) servers to process the workload on premises. We propose a novel distributed solution to the collaborative processing problem that adaptively positions the MEC UAVs in response to the changing workload that arises both from the sensing UAVs' mobility and the task generation. Our solution consists of two key building blocks: (1) an efficient workload estimation process by which the UAVs estimate the task field-a continuous approximation of the number of tasks to be processed at each location in the airspace, and (2) a distributed optimization method by which the UAVs partition the task field so as to maximize the system throughput. We evaluate our proposed solution using realistic models of surveillance UAV mobility and show that our method achieves up to 28% improvement in throughput over a nonadaptive baseline approach. © 2022, CC BY.

关键词： Computation offloading   Mobile edge computing   Task and resource scheduling   Attribute reduction  

摘要： The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.