关键词： Cellular V2X communication   mobile edge computing   physical-layer security   secrecy throughput   imperfect channel state information   access threshold  

摘要： In a cellular vehicle-to-everything (C-V2X) enabled computation-offloading network, vehicular users may deliver computation tasks to a cellular base station (BS) through vehicle-to-infrastructure (V2I) transmission links in order to accommodate computation-intensive applications, where the BS is usually equipped with a mobile edge computing (MEC) server. However, due to the broadcast nature of wireless communications and high-mobility of vehicles, the computation-offloading information may suffer from an eavesdropping threat. In practice, the interference generated by device-to-device-based V2V (D2D-V) links can be utilized for protecting the offloading information against eavesdropping. This observation motivates a security provisioning for C-V2X computation-offloading network. Specifically, a dynamic threshold-based access scheme is required to maintain the interference under control for different channel conditions. Unfortunately, the previous studies that are based on the assumption of perfect channel state information (CSI) can not be applied for the dynamic vehicular networks. In this paper, we propose a dynamic threshold-based access scheme for security provisioning of C-V2X computation-offloading network by considering an imperfect CSI. In this scheme, an optimized access threshold is set to update adaptively in terms of channel estimation error for balancing both the security and reliability of the offloading link. Furthermore, the proposed scheme can maximize the secrecy throughput under a connection outage constraint of the D2D-V links, with the total area spectral efficiency optimized under the security performance criterion for the offloading link. Numerical results are provided for validating the proposed theoretical analysis. A useful design insight is provided for attaining an optimal configuration of C-V2X computation-offloading network.

关键词： Wireless fidelity   Computational modeling   Edge computing   Data models   Cloud computing   Adaptation models   Tools   Mobile edge computing   data offloading   traffic prediction   security   deep learning  

摘要： Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.

关键词： Task analysis   Internet of Things   Servers   Security   Delays   Energy consumption   Cloud computing   Caching   energy   IoT   mobile edge computing   security  

摘要： The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies.

关键词： Mobile edge computing   mobile cloud computing   computation offloading   resource allocation  

摘要： Computation offloading enables energy-limited devices to expand the scope of the computational tasks that they can complete within specified latency requirements. However, when multiple devices seek to offload, effective allocation of resources becomes crucial. In this paper, we develop an energy-optimal signalling structure for a K-user offloading system, and an efficient algorithm for allocating the communication resources provided by that structure. The signalling structure is designed to exploit the differences between the users' latencies and the reduction in interference that arises when a device completes its offloading. That results in a time-slotted signalling structure, which is then optimized for a multiple access scheme that exploits the full capabilities of the channel (FullMA). The optimized signalling structure enables us to substantially reduce the dimension of the resource allocation problem, and to develop efficient algorithms to tackle that problem for both the binary and partial offloading cases. Our numerical experiments illustrate that the proposed time-slotted FullMA signalling structure significantly reduces the energy consumption of the devices compared to some existingmethods that employ orthogonal multiple access schemes, such as TDMA, and to those with FullMA, but sub-optimal single-time-slot signalling structures.

关键词： Backscattering Algorithm   Mobile edge computing   Prirority based task Scheduling Algorithm   Work load sharing cloud environment  

摘要： Recently, intense use of mobile devices is increased and day by day new technology in communication is increasing so we have to accommodate more and more devices on the cloud server also we have to provide advanced algorithm for efficient exchange of resources between cloud and client server. Mobile devices users are saturated with already proposed traditional methods such as grid-computing. Our aim in this project is to reduce the load on mobile devices and reduces the work sharing in cloud environment. This paper supports the use of more optimum algorithm for mobile Edge-Clouds. In using mobile edge computing technology, we have a cellular operator that allows efficient deployment services for specific customers or classes of customers. This technology also reduces the signal load of the core network, and can host applications and provide services in a cheaper way. Data Sharing will increase traffic on mobile edge cloud to reduce it is our major aim of the project. Our idea is to secure and reliable data sharing in mobile edge cloud *** achieve this we are going to implement an Algorithm of BackScatter and Priority based task Sharing Algorithm. © 2020 Hampstead Psychological Associates. All rights reserved.

关键词： Servers   Resource management   Blockchain   Task analysis   Edge computing   Wireless communication   Simulation   Mobile edge computing   auction   cross-server resource allocation   blockchain   incentive mechanism  

摘要： Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.

关键词： Computation offloading   Internet of Things   mobile edge computing   task scheduling   vehicular networks  

摘要： Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multi-user, multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations.

关键词： Mobile handsets   Task analysis   Servers   Edge computing   Resource management   Optimization   Cloud computing   Assisted mechanism   computation offloading scheme   mobile edge computing   resource allocation  

摘要： Mobile edge computing (MEC) is a promising paradigm for providing computing and storage capabilities in close proximity to mobile devices. To solve the scenario in which massive mobile devices have tasks to be processed at the same time, this paper proposes an assisted mechanism for the MEC system. When the primary MEC server is unable to meet the delay requirements of the mobile devices within its coverage area, a portion of the tasks can be offloaded to secondary MEC servers to obtain extra resources for processing. This MEC framework effectively reduces the computing and communication burden of the primary MEC server and improves the resource utilization of the secondary MEC servers. To maximize the system offloading utility in terms of latency, we formulated an optimization problem that jointly optimizes the task assignment, computing resource allocation and offloading decision of all mobile devices. Since the formulated problem is a mixed integer nonlinear problem, we use the decomposition method to convert the optimization problem into several subproblems. In addition, a heuristic algorithm based on the priorities of mobile devices and the MEC servers is proposed to obtain the suboptimal device offloading strategy. The numerical results show that the assisted mechanism can effectively reduce system latency and improve system reliability. In addition, the performance of our proposed algorithm is close to the optimal solution.

关键词： Caching   reinforcement learning   fuzzy logic   mobile edge computing  

摘要： Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

关键词： Wireless communication   Edge computing   Computer architecture   Wireless sensor networks   Internet of Things   Resource management   Wireless power transfer   Wireless power transfer   mobile edge computing   Industrial Internet of Things   network architecture   resource management  

摘要： The convergence of wireless power transfer (WPT) and mobile edge computing (MEC) has fostered the rise of wireless powered MEC, which is taken as a crucial technology for the sustainability of the Industrial Internet of Things (IIoT) systems. This article provides an overview of wireless powered MEC enabled IIoT systems, including use cases, network requirements, system architecture, and resource management. First, we highlight four key requirements to drive operational efficiency from the perspective of being transformed IIoT projects. Then an integration architecture of wireless powered MEC for IIoT is proposed. Designed with joint consideration of energy, communication, and computing resources, the architecture is enabled by an efficient system schedule mechanism to achieve age-aware data update, green and sustainable energy supply, as well as hierarchical and resilient computation. Finally, a case study is provided to verify the feasibility of the proposed architecture and demonstrate the efficiency of the proposed resource management approach.