关键词： NOMA   Power system reliability   Probability   Resource management   Task analysis   Uplink   Edge computing   Mobile edge computing   non-orthogonal multiple access   physical layer security   secrecy outage probability   partial offloading  

摘要： Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.

关键词： Cognitive agent   Mobile edge computing   Caching strategy   Resource collaboration  

摘要： The service of mobile network develops rapidly nowadays, which generates various computing and resource-intensive applications, such as Internet of vehicles and virtual reality. Mobile edge computing (MEC) is close to data source and users, so terminals can execute tasks at the edge of network. In this way, the heavy load on core network can be relieved and tasks can be executed effectively. However, the demands of users vary from each other and users move all the time. It is difficult for the existing way of service supply to meet demands of all users. Cognitive Agent (CA) is put forward in this paper to help users cache and execute tasks on MEC in advance. In detail, CA is used to build personalized model combined with users' behavior data. At the same time, it uses Long short-term memory neural network to forecast the moving trajectory of terminal equipment and the service types to be requested, uses the prediction result to generate caching strategy, cache business and shorten the delay of task execution. Besides, to further reduce the stress on MEC, we propose the collaboration of computing, communicating and caching resource with neighboring users' equipment. To verify the effectiveness of CA, we build a model that assesses the performance of the system. Finally, we design a simulation experiment to execute resource request and resource collaboration. The result of the experiments show that CA can improve the efficiency of communication network, relieve the stress on network and improve the quality of services to users. (C) 2019 Elsevier B.V. All rights reserved.

关键词： End-edge-cloud orchestration   network computing   transparent computing   mobile edge computing   fog computing   cloudlet  

摘要： Sending data to the cloud for analysiswas a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.

关键词： Mobile edge computing   sequential task offloading   ultra-reliable low-latency task offloading  

摘要： In this letter, a collision-free sequential task offloading scheme to multiple mobile-edge computing servers is proposed. The problem is formulated as a multi-objective optimization of latency and offloading failure probability. An exact solution technique is developed to obtain a benchmark optimal solution for the problem. A more computationally efficient heuristic algorithm is additionally developed, whose sub-optimal solution yields a performance close to optimal. Simulation results illustrate that the proposed offloading scheme can effectively reduce both latency and offloading failure probability.

关键词： Mobile edge computing   computation efficiency   resource allocation   orthogonal frequency division multiple access  

摘要： Computation-efficient resource allocation strategies are of crucial importance in mobile edge computing networks. However, few works have focused on this issue. In this letter, weighted sum computation efficiency (CE) maximization problems are formulated in a mobile edge computing (MEC) network with orthogonal frequency division multiple access (OFDMA). Both partial offloading mode and binary offloading mode are considered. The closed-form expressions for the optimal subchannel and power allocation schemes are derived. In order to address the intractable non-convex weighted sum-of ratio problems, an efficiently iterative algorithm is proposed. Simulation results demonstrate that the CE achieved by our proposed resource allocation scheme is better than that obtained by the benchmark schemes.

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： Internet of vehicles   mobile edge computing   deep reinforcement learning   resource allocation   delay tolerance  

摘要： The advancement of 5G technology has brought the prosperous development of Internet of Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive to the delay. As a promising computing paradigm, mobile edge computing (MEC) can be applied to IoV scenarios. However, due to the limited resources of a single MEC server, it is difficult to cope with the suddenly increased computation loads caused by emergencies, or the intensive resource requests from busy regions. Therefore, we propose a novel regional intelligent management vehicular system with dual MEC planes, in which MEC servers in the same region cooperate with each other to achieve resource sharing. We classify computing tasks into different types according to their delay tolerances and focus on the optimization problem of resource allocation for different type tasks. And then, we design a resource allocation algorithm based on deep reinforcement learning, which can adapt to the changeable MEC environment to process high-dimensional data. Simulation results confirm that our proposed scheme is feasible and effective.

关键词： Mobile devices   edge computing   mobile edge computing   edge cloud computing   virtual machines   access points  

摘要： In recent years, computation offloading has become an effective way to overcome the constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive mobile application tasks to remote cloud-based data centers. Smart cities can benefit from offloading to edge points in the framework of the so-called cyber-physical-social systems (CPSS), as for example in traffic violation tracking cameras. We assume that there are mobile edge computing networks (MECNs) in more than one region, and they consist of multiple access points, multi-edge servers, and N MDs, where each MD has M independent real-time massive tasks. The MDs can connect to a MECN through the access points or the mobile network. Each task be can processed locally by theMDitself or remotely. There are three offloading options: nearest edge server, adjacent edge server, and remote cloud. We propose a reinforcement-learning-based state-action-reward-state-action (RL-SARSA) algorithm to resolve the resource management problem in the edge server, and make the optimal offloading decision for minimizing system cost, including energy consumption and computing time delay. We call this method OD-SARSA (offloading decision-based SARSA). We compared our proposed method with reinforcement learning based Q learning (RL-QL), and it is concluded that the performance of the former is superior to that of the latter.

关键词： Relays   NOMA   Uplink   Base stations   Lead   Performance evaluation   Energy harvesting   Power beacon   NOMA   outage probability   mobile edge computing  

摘要： As a promising technique, power beacon provides ability of wireless energy transfer to relaying devices in Internet of Things (IoT) to serve far devices with respect to low latency and high spectrum efficiency manner. Mobile edge computing (MEC) enables smart devices to offload parts of their computation-workloads to the cellular base stations associated with edge servers. This paper introduces a promising multiple access technique, namely non-orthogonal multiple access (NOMA) to provide communication service between the base station associated with MEC and far IoT devices. In this paper, we indicate that the multiple power beacons (PBs) benefits to uplink of NOMA-MEC system with the wireless powered IoT. More antennas equipped at smart device and more PBs exhibit reasonable outage performance for the considered system. The simulation results reveal in two folds: (1) our proposed scheme can simultaneously achieve higher spectrum efficiency and ensure prolong lifetime of IoT devices;(2) the mobility of relay leads to select PB appropriately and hence the suitable scheme is realized as comparing two schemes related to ability of energy harvesting and acceptable outage behavior.

关键词： Access control   Data security   Edge computing   Cloud computing   Data centers   Servers   Mobile edge computing   access control   data security   data confidentiality   data integrity   metagraph theory  

摘要： Mobile edge computing, with characteristics of position awareness, mobile support, low latency, decentralization, and distribution, has received widespread attention from industry and academia, and has been applied to application areas such as intelligent transportation, smart city, and real-time big data analysis. However, it also brings the new security threats, especially data security threats during data access that leads to unauthorized/unauthorized access, alteration and disclosure of data, affecting the confidentiality and integrity of the data. Therefore, access control, as an important method to ensure the security of user data during data access, began to be applied to mobile edge computing. However, the existing access control has the disadvantages of coarse-grain, poor flexibility and accuracy, lack of internal attack considerations, etc., which cannot meet the needs of data security in practical applications of mobile edge computing. In this paper, a data security enhanced Fine-Grained Access Control mechanism (FGAC) is proposed to ensure data security during data access in mobile edge computing. In FGAC, a dynamic fine-grained trusted user grouping scheme based on attributes and metagraphs theory was first designed. Secondly, the scheme was combined with the traditional role-based access control mechanism to assign roles to users based on user group credibility. And then, based on attribute matching the user authentication further verifies whether the user is allowed to perform the access operations to achieve fine-grained data protection. Experimental results show that FGAC can effectively identify malicious users and make group adjustments, while achieving fine-grained access control and assure the data security during the data access process in mobile edge computing.