关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) systems (in which intensive computation and data storage tasks are performed locally, due to absence of communication infrastructure for connectivity to cloud) are currently being developed for disaster response applications and for tactical environments. MEC applications for these scenarios generate and process significant mission critical and personal data that require resilient and secure storage and sharing. In this paper we present the design, implementation and evaluation of R-Drive, a resilient data storage and sharing framework for disaster response and tactical MEC applications. R-Drive employs erasure coding and data encryption, ensuring resilient and secure data storage against device failure. R-Drive adaptively chooses erasure coding parameters to ensure highest data availability with minimal storage cost. R-Drive's distributed directory service provides a resilient and secure namespace for files with rigorous access control management. R-Drive leverages opportunistic networking, allowing data storage and sharing in mobile and loosely connected edge computing environments. We implemented R-Drive on Android, integrated it with existing MEC applications. Performance evaluation results show that R-Drive enables resilient and secure data storage and sharing. © 2022, CC BY.

关键词： Bicycles   Smart cities   Intelligent sensors   Roads   Real-time systems   Public transportation   Biomedical monitoring   Bicycle sharing system   crowd sensing   emergency network   mobile ad hoc network   mobile edge computing   smart city   software defined network  

摘要： As a typical example of an Intelligent Transport System (ITS) in a smart city, the bicycle sharing is developing so fast and changing the citizens' travel habits to a large extent in China. However, its disorder development brings a heavy burden for city, and it is considered as a junk of city in many citizens' opinions. In our previous work, we proposed the concept of Internet of Shared Bicycle (IoSB) to solve some problems in technique. In this paper, we devote to how to enable bicycle sharing playing a more important role in the construction of smart city, instead of being treated only as a type of traffic mode. We summarize some of the current research status of bicycle sharing in smart cities and put forward some of our own views. Then the Bicycle Sharing System (BSS) is be further studied and explored the possibility of being deployed as a terminal in the city in terms of technology. When a serious disaster comes, it serves as an auxiliary communication system for the city and becomes a measure of the city's emergency network. In particular, we describe a special role of bicycle sharing during public health events. In addition, the feasibility in some interesting cases with great potential in the future is also discussed.

关键词： Mobile edge computing  

摘要： Employing mobile elements is an efficient solution to the performance improvement of wireless sensor networks (WSNs). We propose an efficient data gathering mechanism for disconnected WSNs with rendezvous points (DGM-RPs). The mobile sink traverses the entire network and stops only at the rendezvous points (RPs) while gathers the data from sensors in every disconnected segment. In this paper, mobile sinks perform the task of edge computing and alleviate the load of upper cloud. We measure the shape of disconnected segments, layering them by use of the convex hull, and then design the travelling path of the mobile sink to minimize the travel latency to visit all disconnected segments. At least one RPs will be selected in a segment firstly, and then, on this basis, we consider the distribution density of sensor nodes and the location of the RPs already exist to adding new RPs, which make good use of the margin to reducing the energy consumption and prolong the network lifetime.

关键词： Mobile edge computing  

摘要： With the rapid development of unmanned aerial vehicles (UAVs) technology and the advent of the 5G era, the role of UAV-enabled mobile edge computing (MEC) system has attracted much attention, especially in the event of some emergencies. However, considering the limited battery life and computing capabilities of UAVs, it is challenging to provide energy-efficient services for mobile devices. To solve this challenge, we propose an energy-efficient dynamic task migration algorithm (EDTM) that minimizes the total energy consumption of the system while ensuring UAVs system load balance. Based on the improved ant colony algorithm and path elimination strategy, the proposed algorithm comprehensively considers task migration distance between UAVs, the load situation of UAVs, and environmental factors (e.g., wind speed and air density) and finally plans a reasonable task migration path. The simulation results show that the performance of the proposed EDTM is superior to the benchmark schemes.

关键词： Internet of Vehicles   IEEE-802   11p   Cellular networks   Mobile edge computing   Multi-access edge computing   Vehicular edge computing   Task offloading   Quality of service   Cache management  

摘要： In mobile environments, with the help of larger bandwidths and cloud computing solutions, any task can be offloaded from a mobile user equipment to be handled remotely. However, even though this process is accelerated with every cellular generation, with 5G being no exception, offloading to a faraway centralized cloud implies non-negligible delay. To tackle this issue concerning delay-sensitive applications, mobile edge computing, now denominated as multi-access edge computing (MEC), was brought to light. With cloud resources brought closer to the edge of the network, MEC greatly reduces task offloading delay, thereby striving to satisfy the constraints of real-time applications. As highly demanding mobile applications, vehicular networks are a target to be addressed in terms of performance, especially communication and computation delay. In this article, we establish the specificities of MEC when applied to the Internet of Vehicles (IoV), and survey recent papers studying implementations of MEC relevant to real-time vehicular considerations. We categorize these latest V2X architectures so as to unveil the mechanisms behind their improved performance: network availability and coverage, reliability and loss of network connectivity, large data handling and task offloading. This survey not only provides an initial understanding of the state-of-the-art advancements in the field of MEC-enabled vehicular networks, but also raises open issues and challenges that need to be addressed before enjoying the full benefits of this paradigm.

关键词： mobile edge computing   multi-UAV   collaborative cloud and edge computing   deep neural network   differential evolution  

摘要： This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

关键词： Deep Q learning   Mobile Edge computing   SDN   Edge caching   Service migration  

摘要： With the increase of mobile terminal equipment and network mass data, users have higher requirements for delay and service quality. To reduce user access latency and more effectively cache diverse content in the edge network, a low-latency edge caching method is proposed. The cache model based on base station cooperation is established and the delay in different transmission modes is considered. Finally, the problem of minimizing latency is transformed into a problem of maximizing cache reward, and a greedy algorithm based on the original dual interior point is used to obtain the strategy of the original problem. Meanwhile, in order to improve service quality and balance communication overhead and migration overhead, a migration method based on balanced communication overhead and migration overhead is proposed. The model that balances communication overhead and migration overhead is established, and the reinforcement learning method is used to obtain a migration scheme that maximizes accumulated revenue. Comparison results show that our caching method can enhance the cache reward and reduce delay. Meanwhile, the migration algorithm can increase service migration revenue and reduce communication overhead.

关键词： energy efficiency   mobile edge computing   UAV-enabled   wireless power transfer   trajectorys cheduling  

摘要： The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is *** to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

关键词： Mobile edge computing  

摘要： 5G mobile is a revolutionary technology capable of providing internet services at exceptional speeds. It has the potential to bring about a positive change in the lives of billions of people around the world by making the vision of smart cities, automated and connected cars, telehealth and telemedicine a living reality. However, to realise the full potential of the 5G vision, it is necessary to embrace the concept of Mobile Edge Computing to overcome the limitations of traditional cloud computing. © 2022 Institute of Telecommunications Professionals. All rights reserved.

关键词： Mobile edge computing   Cloud computing   Video coding   Rate control  

摘要： The current low-latency video coding rate control algorithm has similar video sequence structure, low rate control accuracy, long control time, etc., which cannot meet the expectations of modern workers. Based on this, this paper proposes a mobile edge cloud computing-based Low-latency video coding rate control algorithm, optimize the original control algorithm, improve its rate control accuracy, etc. By analyzing the relationship between mobile edge computing and cloud computing, combined with edge computing in the context of cloud computing, this paper proposes a mobile edge cloud computing method and designs the mobile edge cloud computing architecture. Low latency video sequences are obtained using mobile edge cloud computing, and a mobile edge cloud computing model is established to control the update rate parameters of delayed video files. According to the principle of rate control, based on joint rate-distortion theory and frame coding complexity calculation model, the low latency video coding rate control is realized. The experimental results show that the byte data volume of the low-latency video coding sequence of the proposed method is 5653 dB. The rate control accuracy can be as high as 99.64%, and the rate control time is 1.37 s. The proposed method has a high similarity of video sequence structure and high rate control accuracy and can effectively shorten the rate control time of low latency video coding.