关键词： internet of things   IoT   power grid   renewable energy   optimisation strategy   mobile edge computing   MEC  

摘要： With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

关键词： Mobile edge computing   offloading decision   resource allocation   charge to UEs  

摘要： Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

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： V2X   data fusion   GPS   global positioning system   OBU   on board unit   RSU   road side unit   particle filter   positioning   vehicular network   MEC   mobile edge computing  

摘要： Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While global positioning system (GPS) is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using particle filter (PF), which is flexible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: controller area network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localisation with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.

关键词： Edge intelligence   mobile edge computing   IoT   image distortion   latency and reliability   semi-definite relaxation  

摘要： The emerging time-critical Internet-of-Things (IoT) use cases, e.g., augmented reality (AR), virtual reality (VR), autonomous vehicle etc., on the one hand, involve computation intensive computer vision (CV) components in the services, on the other hand, the computation task should be completed within the stringent latency constraint, otherwise, the service reliability will deteriorate. The state-of-the-art work has shown that it is promising to tackle this challenge by offloading the computation tasks to the edge servers (ESs) using mobile edge computing (MEC). However, offloading tasks from local IoT devices to remote ESs could cause communication errors, thereby resulting in transmission failure even service timeout. The existing work mainly requires perfect transmission during task offloading at physical layer or transport layer. In fact, CV algorithms for, e.g., image classification and recognition, are able to tolerate certain level distortion of the input image to maintain the required inference accuracy. In this paper, we focus on the service reliability at application layer and study how feasible it is to improve the service reliability of the time-critical CV services in MEC system by allowing imperfect transmission. The service reliability is modeled by the transmission failure probability, service timeout probability and inference accuracy. The optimization goal is to maximize the service reliability, subject to the latency constraint. Due to the non-convexity, we solve this problem by the semi-definite relaxation based algorithm for a multi-user scenario. We evaluate the algorithm considering the practical scenarios, i.e., object detection with SSD and YOLOv2. The proposed algorithm achieves the performance closed to the exhaustive method but at a much lower complexity.

关键词： overclocking   intelligent network   mobile edge computing   computation offloading   resource allocation  

摘要： With the rapid growth of user equipment(UE), the amount of data transmitted over networks has become enormous, exerting immense pressure on backbone networks and central cloud ***, corresponding applications requiring high energy consumption and low latency have multiplied the requirements for UE. Mobile edge computing(MEC) has been proposed to support the offloading of UE tasks to edge clouds for execution. The implementation of MEC requires fast data transmission between UE and edge servers, and the emerging 5G network appears to render this technology possible. In this paper,considering a large number of UE, a fixed MEC server, and an advanced intelligent network, we suggest an intelligent overclocking mechanism for the MEC server that operates for an intelligently calculated period to allow it to leverage more computing power without introducing additional hardware resources for a certain period of time. We jointly manage task offloading, server resource allocation, and overclocking to minimize the system-wide computation overhead and other risks. The proposed optimization problem is a mixedinteger nonlinear programming problem that is divided into three subproblems: offloading decision, resource allocation, and overclocking decision. We solve these subproblems using non-convex techniques and provide an iterative algorithm to obtain a heuristic solution for the original problem. Finally, simulation results show that the overclocked MEC server has lower system-wide computation overhead, faster task processing, and more offloaded UE as compared with the case without overclocking.

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.

关键词： Task analysis   Mobile handsets   Device-to-device communication   Processor scheduling   Servers   Scheduling   Peer-to-peer computing   Software defined network   device-to-device   peer-to-peer   mobile edge computing   task scheduling   resource allocation   minimum task duration  

摘要： The low computational capacity of mobile devices has become the main performance bottleneck for emerging computing-intensive and delay-sensitive applications. In this paper, we propose a peer-to-peer (P2P) enhanced task scheduling framework to minimize the average task duration in device-to-device (D2D) enabled mobile edge computing (MEC) network by jointly optimizing the task scheduling decision and the computational resource allocation. Our proposed framework can work in different modes in different application scenarios. In the MEC mode, an iterative algorithm is presented based on alternating optimization technology and sorting algorithm, which can derive a near-optimal solution of the original mixed-integer non-linear programming (MINLP) optimization problem. In the P2P mode, we can reformulate the original problem to a typical one-to-one assignment problem, which is solved by a task scheduling algorithm based on the Hungarian method. In more challenging hybrid mode, we make some simplifications based on reformulation linearization technology. Then, the near-optimal solution is obtained by our proposed scheduling algorithm based on the alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. Numerical results show that our proposed framework has shorter average task duration under different scheduling modes compared with the other scheduling schemes.

关键词： Mobile edge computing   age of information   task offloading   resource allocation   Lyapunov optimization  

摘要： To improve the operational efficiency of smart city, smart devices extract informative status updates from sampled image and video data to intelligently monitor the surroundings. Mobile edge computing (MEC) is considered as an emerging technology to provide energy-constrained devices with enhanced computation capability by offloading tasks to nearby servers. In such circumstance, the freshness of obtained status updates is critical to system performance, which can be characterized by the concept of age of information (AoI). Due to resource contention among multiple devices, the problem of how to maintain the timeliness of task executing is not trivial. In this paper, we are interested in minimizing the age of obtained status updates by jointly optimizing task generation, computation offloading as well as communication and computational resource allocation under the average energy constraint at each device. To tackle the time couplings of task generation and computation offloading decisions, we leverage the Lyapunov optimization technique to convert the long-term stochastic optimization problem into a per-time slot deterministic optimization problem. In each time slot, an online algorithm is proposed to determine the task offloading and computation offloading strategy. Moreover, we theoretically prove that the proposed algorithm can be arbitrarily close to the optimal performance with the gap of O(1/V). Simulation results show that our proposed scheme achieves better performance when compared with existing schemes.

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.