关键词： Smart cities   street cleaning   garbage detection   deep learning   mobile edge computing  

摘要： During the process of smart city construction, city managers always spend a lot of energy and money for cleaning street garbage due to the random appearances of street garbage. Consequently, visual street cleanliness assessment is particularly important. However, the existing assessment approaches have some clear disadvantages, such as the collection of street garbage information is not automated and street cleanliness information is not real-time. To address these disadvantages, this paper proposes a novel urban street cleanliness assessment approach using mobile edge computing and deep learning. First, the high-resolution cameras installed on vehicles collect the street images. Mobile edge servers are used to store and extract street image information temporarily. Second, these processed street data is transmitted to the cloud data center for analysis through city networks. At the same time, Faster Region-Convolutional Neural Network (Faster R-CNN) is used to identify the street garbage categories and count the number of garbage. Finally, the results are incorporated into the street cleanliness calculation framework to ultimately visualize the street cleanliness levels, which provides convenience for city managers to arrange clean-up personnel effectively. The overall approach is illustrated and visualized using the street images of Jiangning District in Nanjing, China. The practical application shows the feasibility and usability of the approach.

关键词： Mobile edge computing   network coding   wireless relay   intelligent cache   cooperative communications  

摘要： As an important technology in next-generation networks, mobile edge computing (MEC) can bring high-speed intelligent network services to users. However, planning MEC file transmission represents a problem to be urgently solved. In addition, network coding has been an emerging technology in recent years, and certain forms of cooperation with wireless relays can improve the transmission efficiency of network data. In this paper, we focus on cooperation in MEC intelligent cache with network coding. First, we built a MEC intelligent cache analysis platform that includes multiple algorithms;it can predetermine which streaming media files should be stored in the limited storage space of the MEC device to provide users the most efficient intelligent network experience. The platform's decision algorithm includes an innovative composite recommendation algorithm that is more comprehensive than the traditional recommendation algorithm. The MEC file extracted from the core network is then simultaneously transmitted to the edge with the real-time normal communication file using an innovative network coding and wireless relay cooperation method;with that, the files stored in the MEC can be cached to the edge. The normal network communication process does not require additional slot costs. The simulation results show that the proposed cooperation scheme can effectively improve network data transmission, improve users' average access rate, and offer users better intelligent network services.

关键词： Vehicular heterogeneous network   mobile edge computing   multipath transmission   work-load allocation  

摘要： With the rapid development of intelligent transportation, various applications which have millisecond delay requirements appear in the vehicular heterogeneous network. Offloading these delay-sensitive applications into edge nodes is a trend and direction of development. However, with the increase in the number of vehicular applications, the conventional methods of distance-based edge node workload allocation make the workload allocation unbalanced, causing some edge nodes to be overloaded, resulting in response time of corresponding application being too long to meet low delay requirements. In this paper, we tackle the problem of edge node overload and propose a multipath transmission workload balancing optimization scheme, which uses multipath transmission in the edge computing architecture as the transport protocol support for the communications between the vehicles and the edge nodes and the real-time virtual machine (VM) migration happens between the edge nodes. First, the application is assigned to the edge node closest to each vehicle. When the workload of the edge node exceeds its capacity, the scheme iteratively selects the application with the longest response time. Second. if its response time exceeds the response time passed to the cloud computing center, it is assigned to the cloud computing center for processing;if not, it is reassigned to the standby edge to minimize its response time until all vehicle applications cannot find a better edge node. Then, computing resources are allocated to each edge node, and resources of different sizes are allocated to different types of VMs in each edge node through convex optimization. Finally, the extensive simulation results illustrate that the multipath transmission workload balancing optimization scheme can effectively reduce the average response time of the vehicular applications compared to existing schemes.

关键词： Correlated mobility   edge computing   host deployment   mobile edge computing   resource distribution   user mobility  

摘要： Mobile edge computing (MEC) is the next paradigm to support the enormous growth of diverse mobile applications that require high computational power, low latency, and high bandwidth. The user experience can be enhanced beyond the constrained resources limited by the user equipment by offloading computation-intensive tasks to the mobile edge (ME) hosts. However, ensuring service continuity of the offloaded tasks while users are moving from cell to cell is a critical factor to be kept in mind before selecting the locations suitable for ME host deployment. Therefore, in this paper, we present an optimization model to address the resource distribution problem of mobile users in an ME system so that the total number of tasks accomplished is maximized by considering the user mobility patterns in the network. Despite the fact that the correlation of human mobility during peak hours is present in the traces of urban cities, there is a lack of a correlated human mobility model in the existing literature. We first developed a synthetic correlated mobility model to reproduce the human traces of mobility during morning peak hours. We then developed a utilitarian resource distribution algorithm to solve the ME host deployment and the resource distribution problem. We anticipate that the findings of this paper will contribute to the development of a real-time network management and monitoring system in the ME system to detect changes in MEC resource requirements and optimize MEC resource orchestration based on the predicted user mobility and services' usage demands. Our proposed methods will: 1) become a critical part of the future network management system of 5G that employs MEC;2) contribute to the standardization of the MEC and 5G platforms;and 3) help network operators reduce the massive cost of deploying and operating a large number of distributed servers in the next-generation mobile networks.

关键词： Mobile edge computing   Offloading availability   Offloading decisions   Task scheduling   Multi-objective optimization  

摘要： Deploying cloud service at base stations (Cloudlets, considered as Mobile Edge Computing) or sharing resources between mobile devices (Mobile Cloudlets) are expected to be enablers of Edge Computing or Fog computing. Different Mobile Edge Computing systems have been proposed based on hierarchical cloud architecture. However, the dynamics and diversity of mobile cloudlets makes it challenging to offload tasks to optimal nodes (process locally, or offload to a device in Mobile Cloudlets or Cloudlets). In this paper, offloading availability is formulated by considering user mobility similarity and the residual energy of mobile devices. To guarantee offloading availability and minimize energy consumption as well as data size transmitted through the cellular access links, offloading decisions for multi-tasks (task scheduling problem) are formulated to be a multi-objective optimization problem and we develop a centralized algorithm TS-SMOSA such that efficient-energy offloading decisions are made and tasks can be allocated to optimal nodes. Finally, experimental results are promising and show near optimal solutions for all of our studied scenario.

关键词： 5G   mobile edge computing   ultra-reliable low latency communications   augmented reality   code-partitioning offloading  

摘要： Augmented reality (AR) is one of the emerging use cases relying on ultra-reliable and low-latency communications (uRLLC). The AR service is composed of multiple dependent computational-intensive components. Due to the limited capability of user equipment (UE), it is difficult to meet the stringent latency and reliability requirements of AR service merely by local processing. To solve the problem, it is viable to offload parts of the AR task to the network edge, i.e., mobile edge computing (MEC), which is expected to extend the computing capability of the UE. However, MEC also incurs extra communication latency and errors on the wireless channel;therefore, it is challenging to make an optimum offload decision. So far, a little of state-of-the-art work has considered both the latency and reliability of the MEC-enabled AR service. In this paper, we study the scenario multiple edge nodes cooperate to complete the AR task. The dependency of task components is modeled by a directed acyclic graph through code partitioning. We aim to minimize the service failure probability (SFP) of the MEC-enabled AR service considering reliability and latency. We design an integer particle swarm optimization (IPSO)-based algorithm. Although the solution of IPSO-based algorithm approaches the optimum of the problem, it is infeasible to use IPSO for real-time AR services in practice due to the relatively high computational complexity. Hence, we propose a heuristic algorithm, which achieves a performance close to that of the IPSO-based algorithm with much lower complexity. Compared with state-of-the-art work, the heuristic algorithm can significantly improve the probability to fulfill the targeted SFP in various network conditions. Due to the generic characteristics, the proposed heuristic algorithm is applicable for AR services, as well as for many other use cases in uRLLC.

关键词： Vehicular networks   mobile edge computing   reinforcement learning   resource allocation  

摘要： The latest research of network and computing contributes greatly to the development of vehicular networks. However, in existing works, these two important enabling technologies are studied separately. To reduce the delay, in this paper, we propose a multi-platform intelligent offloading and resource allocation algorithm which can dynamically organize the computing resources to improve the performance of the next-generation vehicular networks. Considering the task calculation problem, the K-nearest neighbor algorithm is used to select the task offloading platform (i.e., cloud computing, mobile edge computing, or local computing). For the computational resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation. The simulation results show that compared with the baseline algorithm that all tasks are offloaded to the local or mobile edge computing server, the resource allocation scheme achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.

关键词： Internet of Things   latency tolerable data   mobile edge computing   task offloading   data transfer  

摘要： With the emergence of in-depth research of emerging technologies or 5G mobile communication technology methods, the IoT applications have been further sublimated. In this paper, the new characteristics and new challenges appearing in the current mobile edge computing are sorted out, and the latest related models and work are summarized. The important optimization models and moving models and wireless block data in mobile edge computing are analyzed and discussed. On this basis, this paper mainly designs and verifies the following three aspects of mobile edge computing. A joint optimization model of task offloading and power allocation is established, and a centralized joint optimization algorithm for task unloading and power allocation is proposed. Based on the equalization delay and the impact of energy consumption on task unloading, the algorithm can use the idle resources that can be used to distribute and unload the computing tasks. The simulation experiments show that the algorithm can not only coordinate task offloading and power allocation effectively, but also improve the balance between system delay and energy consumption. Delay-tolerable data can be modeled as a partially observable Markov decision process in a software-defined transport and compute node selection process. Compared with the existing scheme, the proposed method can effectively reduce system overhead, shorten data calculation execution time, improve data calculation efficiency, and ensure that the delay can tolerate data transmission arrival rate under the condition of transmission delay.

关键词： Mobile edge computing   resource allocation   unmanned aerial vehicle communications   computation efficiency  

摘要： Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.

关键词： mobile edge computing   wireless power transfer   joint optimization   convex optimization   BFGS  

摘要： Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.