关键词： Mobile edge computing   MANET   AOMDV   energy threshold   link lifetime   energy consumption  

摘要： During mobile edge computing, due to the movement of nodes and the exhaustion of node energy, link failure occurs thus reducing the network lifetime in the mobile ad-hoc network. When the route fails, because the single-path protocols need to restart the route discovery process, the delay of the network is greatly increased. Therefore, the multi-path routing protocol is proposed, saving the cost of route discovery. In this paper, we propose an ad hoc on-demand multi-path distance vector (AOMDV) routing protocol based on link lifetime and energy consumption prediction (named LLECP-AOMDV) for mobile edge computing. In the route discovery phase, the energy grading strategy is adopted. When the node energy is lower than the threshold, it no longer participates in the route discovery. In the routing selected phase, the path is selected based on the lifetime of the route link and the minimum energy consumption of the route. According to energy consumption, packet delivery rate, end-to-end delay performance indicators, we evaluate the comparison results. The result shows that under most network performance indicators and parameters, the proposed LLECP-AOMDV is superior to the other three protocols, which improves the network lifetime, reduces the node's energy consumption and the average end-to-end delay. The protocol is very useful for mobile edge computing.

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.

关键词： Servers   Planning   Resource management   Edge computing   Optimization   Virtual machining   Task analysis   Mobile edge computing   edge server planning   uncertain programming   learning-based framework   resource optimization  

摘要： Mobile Edge Computing (MEC) architecture is composed of geographically distributed edge servers, in which computing capabilities are provisioned at the boundary of the network, which is in close proximity to the end users to provide network services with low latency. The planning of MEC edge servers at appropriate locations is the fundamental first step towards the deployment of the MEC system. In the literature, edge servers planning is based on deterministic resource requirements. This assumption largely neglects the pragmatic complexities imposed by the real dynamic world, in which base station (BS) resource demands are stochastic variables with arbitrary pattern. In view of this fact, we formulate the MEC planning problem as a joint optimization problem of MEC edge servers placement and resource allocation with uncertain BS demands through an uncertain programming formulation. Due to the complexity of this joint-uncertain problem, a learning based framework is utilized to practically solve this problem, and the relevance of applying this mechanism in practical usage with sampled arbitrary BS demands data is also discussed. Finally, we conducted intensive real-data driven simulations to evaluate the performance of our proposed mechanism. The results show the effectiveness of our approach with arbitrary BS demands.

关键词： Delays   Task analysis   Servers   Base stations   Particle swarm optimization   Prediction algorithms   Virtual machining   Mobile edge computing   service migration   mobility   particle swarm algorithm  

摘要： With the development of Mobile Edge Computing (MEC), it has become a key technology to realize the vision of the Internet of Things. In MEC, users can upload tasks to edge nodes for faster processing speed and lower local energy consumption. However, as the mobility of users and the limited resources of the edge nodes, some edge nodes cannot provide high-quality services. In this case, we study service migration strategy in the MEC system to migrate services from the initial nodes to other edge nodes that can provide services to meet the needs of users. By making service migration decision and allocating computation resource, our work minimizes the delay and the energy consumption caused by finishing tasks. Specifically, we set up an efficient service migration model and formulate the service migration problem as a non-linear 0-1 programming problem. To solve this problem, we design a Particle Swarm based Service Migration scheme (PSSM) which includes Queuing Delay Prediction algorithm (QDP), Delay-aware Computation Resource Allocation algorithm (DCRA), and Modified Quantum Particle Swarm algorithm (MQPS). For evaluating the performance of the proposed PSSM, we conduct simulation in a practical scenario. The results demonstrate that our scheme not only can effectively reduce delay and energy consumption, but also improve the processing capability of servers.

关键词： Mobile edge computing   computation offloading   VANETs   link correlation   cost optimization  

摘要： Mobile edge computing (MEC) is a suitable solution to improve computational capabilities in vehicular environments, and the computation offloading plays a critical role in MEC. After the computation task generates, in traditional offloading strategies, vehicles offload data only when they have entered the communication range of the MEC server, which greatly increases the time cost and cannot satisfy the delay requirement of certain applications in many cases. To deploy a large number of MEC servers for full coverage of roads is not a realistic solution due to its high construction cost. In this paper, we make full use of the multi-hop vehicular ad hoc networks (VANETs) to assist in computation offloading of vehicles. In the real-time traffic environment, based on the link correlation theory in VANETs, the reliability model of multi-hop routing path is built. Meeting the delay constraints, the optimal offloading strategy with the lowest relaying and computing cost is executed by the binary search algorithm. The simulation results show that the proposed multi-hop VANETs-assisted offloading strategy (MHVA) shows better performance in offloading delay and cost than existing typical strategies under various environments.

关键词： edge computing   WSN   mobile edge computing   intelligent agriculture  

摘要： Agricultural informatisation has been an extraordinary aspect in the field of agriculture due to advancement in communication technologies. Many studies have been conducted to assist production, such as Wireless Sensor Network (WSN) and Cloud Computing. However, few studies have been focused on the severe requirements of delay and energy consumption of the mobile node. In order to improve network performance, edge computing is introduced into our system architecture for intelligent agriculture. Mobile Edge Computing (MEC) is a distributed computing architecture to offload some tasks to the edge of core network. We adopt a hierarchical structure made of three layers: physical perception layer, information service layer and intelligent application layer. In addition, we further confirm an offloading model for intelligent agriculture. In this article, the state-of-the-art development in intelligent agriculture and current approach of its key technology are discussed. Their potential opportunities and challenges in proposed architecture are presented as well.

关键词： Internet of Vehicles   mobile edge computing   task offloading   reinforcement learning   privacy security  

摘要： With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the on-board terminal can't achieve efficient computing. Therefore, this article proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.

关键词： Task analysis   Servers   Energy consumption   Edge computing   Genetic algorithms   Heuristic algorithms   Resource management   Probability preferred   workload   offloading   genetic algorithm   mobile edge computing  

摘要： Mobile edge computing (MEC) can provide computation and storage capabilities via edge servers which are closer to user devices (UDs). The MEC offloading system can be viewed as a system where each UD is covered by single or multiple edge servers. Existing works prefer a posterior design when task offloads, which can lead to increased workloads. To investigate the task offloading of edge computing in multi-coverage scenario and to reduce the workload during task offloading, a probability preferred priori offloading mechanism with joint optimization of offloading proportion and transmission power is presented in this paper. We first set up an expectation value which is determined by the offloading probability of heterogeneous edge servers, and then we form a utility function to balance the delay performance and energy consumption. Next, a distributed PRiori Offloading Mechanism with joint Offloading proportion and Transmission (PROMOT) power algorithm based on Genetic Algorithm (GA) is proposed to maximize the utility of UD. Finally, simulation results verify the superiority of our proposed scheme as compared with other popular methods.

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： Mobile edge computing   content distribution   context-aware   in-network caching   network congestion  

摘要： While the mobile networks are evolving to a content-centric paradigm, the surging traffic demands keep stressing the ever-increasing network capacities. In-network caching and particularly edge caching offer an effective means to alleviate the traffic pressure by saving bandwidth resources and balancing traffic loads. In this paper, we consider an integrated content distribution model with universal in-network caching in an end-to-end scope. Based on this model, we formulate two key problems to enable collaborative content distribution across domains, i.e., a request screening problem at the mobile edge and a request routing problem at the integrated edge-core. We propose effective solutions for the two problems, which include a joint device caching and matching algorithm for request screening, and a joint source selection and flow routing algorithm for request routing. The simulation results demonstrate that the proposed solutions achieve significant performance gain over traditional approaches in relieving network congestion by exploiting network dynamics and user contexts.