关键词： Mobile edge computing   D2D communications   collaborative content distribution   device caching   device matching   and channel allocation  

摘要： While mobile networks are evolving rapidly, the battle between ever-growing traffic demands and out-paced network capacities will continue and require more efficient solutions. Emerging techniques such as mobile edge computing and device-to-device (D2D) communications can help relieve traffic at the mobile edge and accommodate surging traffic demands from various content-centric services. In this work, we focus on exploiting device caching and user collaboration to offload content distribution traffic. Specifically, we investigate the request offloading problem, which aims to appropriately select caching devices and maximize the content requests that can be fulfilled through D2D communications. Given the constraints of individual transmission and caching capacities, the number of available D2D channels, and information privacy with social-awareness, we can decouple the request offloading problem into two subproblems, i.e., the device caching and matching problem, and the D2D channel allocation problem. As we prove that both problems are NP-hard, we propose efficient algorithms that iteratively make a best local decision in each step. Simulation results show that the proposed algorithms perform fairly closely to optimal solutions in small-scale instances and outperform the reference schemes under various situations.

关键词： Array signal processing   mobile edge computing   IoT   smart city  

摘要： To support the application of IoT and smart city, high data-rate wireless transmission is required. To meet the demand of high data-rate, the techniques of multiple antennas and mobile edge computing (MEC) networks have been proposed in order to enhance the data transmission rate significantly. However, there still exist lots of challenges array signal processing assisted MEC networks. In this paper, we propose an intelligent framework of offloading strategy for MEC networks assisted by array signal processing, where one user with multiple antennas has some computational tasks. These tasks can be computed by the user itself which however has limited computational capability, or computed by the near-by computational access points (CAPs) which has a powerful computational capability at the cost of wireless transmission. We consider the system cost by jointly taking into account the computational price, the energy consumption and the latency. By minimizing the system cost, we propose an intelligent offloading strategy based on ant colony optimization (ACO) algorithm, where the ants randomly visit the CAPs in order to obtain the final results. To further enhance the MEC network performance, the array signal processing is utilized at the user, where either the maximum ratio transmission (MRT) or selection combining (SC) is used to assist the data transmission from the user to CAPs. Simulation results with MRT and SC are finally demonstrated to verify the effectiveness of the proposed ACO-based offloading strategy and array signal processing schemes.

关键词： Delay effects   Servers   Phase change materials   Global Positioning System   Autonomous vehicles   Earth   Calibration   Automatic driving   high precision positioning   one-way time delay   the transformation of a coordinate system   mobile edge computing  

摘要： Connected autonomous driving is considered as a potential way to improve traffic safety. However, when positioning information is delivered through network, the network time delay could result in additional positioning errors, which has been proved by the experiment done in this paper in XuChang automatic driving center that the influence of one-way time delay will lead to meter-scale positioning error. In this paper, we propose a positioning calibration mechanism for connected autonomous vehicles called PCM. PCM firstly estimates the network time delay and then saves the calibrated location information in the MEC server to reduce the positioning error caused by network time delay. Experiments in FangShan and XuChang automatic driving centers verify the performance of PCM, and the result shows that PCM can reduce the positioning error to centimeter-level. For example, under 100ms time delay, the average positioning error without PCM is 119.67cm, while the average positioning error with PCM is 3.03cm. Experimental results show that PCM can effectively reduce the positioning error caused by a one-way time delay to centimeter-level, which is strictly required for positioning autonomous vehicles in automatic driving.

关键词： Intelligent data offloading   mobile edge computing   Internet of Things   risk-based behavior modeling   cognitive decision making   probabilistic uncertainty  

摘要： Data offloading to Mobile Edge Computing (MEC) servers is an attractive choice for resource-constrained Internet of Things (IoT) devices, towards reducing their computational effort. In this paper, we investigate the potential of partial data offloading to MEC servers, under the perspective of users & x2019;cognitive IoT devices presenting loss averse and gain seeking behavior. Due to the sharing nature of the access environment and the MEC server & x2019;s computational characteristics, we treat the MEC server option as a common pool of resources with uncertain payoff returned to the users, while the local computation capability is treated as a safe option for each user. Following the properties of Prospect Theory, users & x2019;prospect-theoretic utilities are formulated exploiting the local computing and offloading overhead options under probabilistic uncertainty. Such a modeling allows for the infusion of human awareness, inherent cognitive biases and behavioral characteristics into the devices & x2019;operation, their data offloading decisions and the edge computing environment that the devices are interacting with. Accordingly, each user & x2019;s optimal offloaded data to the MEC server is obtained as the outcome of a non-cooperative game, with users attempting to maximize their own utilities. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven under the probabilistic nature of the respective payoff functions, while a distributed algorithm that convergences to the PNE is designed. Numerical results are provided that demonstrate the operation and superiority of the proposed framework under different IoT scenarios and behaviors, considering both homogeneous and heterogeneous users.

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： 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： 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： Cloud computing   Servers   Delays   Bibliometrics   Edge computing   Licenses   Mobile edge computing   cloud servers   networks   edge device   smart cities   latency   energy  

摘要： The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

关键词： Cloud computing   Load management   Resource management   Servers   Heuristic algorithms   Edge computing   Mobile handsets   Mobile edge computing   cloudlet placement   affinity propagation algorithm   load balancing  

摘要： Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved Affinity Propagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move.

关键词： Hybrid energy harvesting   device-to-device communication   mobile edge computing   resource allocation  

摘要： Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper. By employing magnetic induction-based wireless reverse charging technology, mobile user can supplement extra energy from nearby users when the energy harvested from ambient radio frequency sources is about to be exhausted. Moreover, mobile user can not only perform local computation, but also offload computing tasks to nearby users for auxiliary computation through D2D communication links or mobile edge computing (MEC) server under base station (BS) for edge computation. Due to the limited computing resources of MEC server, when the computing capability of the MEC server reaches the maximum value, an adjacent user under another nearby BS can be considered as a relay node. The computing tasks of the remaining users under the previous BS can be transferred to the MEC server with sufficient resources under another nearby BS by establishing D2D relay links. The objective of the resource allocation strategy is to maximize the energy efficiency under the constraints of computation delay and energy harvesting. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is not easy to obtain the optimal solution at low computational complexity. A suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm can achieve higher energy efficiency than the standard particle swarm optimization algorithm.