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Reinforcement Learning for Cybersecurity
Published in Chong Li, Meikang Qiu, Reinforcement Learning for Cyber-Physical Systems, 2019
Mobile edge computing (MEC) provides data storage, computing and application services with edge devices such as Access Points (APs), laptops, base stations, switches and IP video cameras at the network edge. Being closer to customers than the cloud, mobile edge computing can provide the Internet of Things (IoT), cyber-physical systems, vehicular networks, smart grids and embedded AI with low latency, location awareness and mobility support. Mobile edge caching reduces the duplicated transmissions and backhaul traffic, improves the communication efficiency, and provides quality of services for caching users. From the security perspective, however, due to the limited computation, energy, communication and memory resources, the edge devices are protected by different types of security protocols, which are in general less secure compared with cloud servers and data centers. In addition, mobile edge caching systems consist of distributed edge devices that are controlled by selfish and autonomous people. The edge device owners might be curious about the data contents stored on their cache and sometimes even launch insider attacks to analyze and sell the privacy information of the customers. Therefore, MEC systems are more vulnerable to security threats such as wireless jamming, Distributed Denial of Service attacks (DDoS), spoofing attacks including rogue edge and rogue mobile devices, man-in-the-middle attacks, and smart attacks. Fig. 7.3 illustrates the possible attacks during the mobile offloading procedure and the cashing perspective.
Mobile Edge Computing for the 5G Internet of Things
Published in Yulei Wu, Haojun Huang, Cheng-Xiang Wang, Yi Pan, 5G-Enabled Internet of Things, 2019
Haojun Huang, Wang Miao, Geyong Min, Chunbo Luo
Mobile edge computing (MEC), which originated from a computing service platform, is a new network architecture designed to reduce the distance between the end users and computing resources, enabling unprecedented benefits for data processing, service provisioning and resource optimization. MEC has been widely adopted by industry companies, such as IBM and Nokia Siemens Networks, to provide services to mobile users [6]. The basic idea is to migrate the cloud computing resources from remote data centers to the edge of the mobile access network to improve utilization of computing and storage resources.
Automated driving technologies
Published in Tom Denton, Automated Driving and Driver Assistance Systems, 2019
Together with Nokia and Deutsche Telekom, Bosch is developing local cloud solutions for the automotive industry and working on the complete integration of vehicles via the cellular network all the way through to the Bosch IoT Cloud. The companies are employing Mobile Edge Computing (MEC), a cellular network technology that uses a local cloud to aggregate and process latency-critical information and distribute it to drivers. Unlike most clouds, this local cloud is situated directly at a mobile base station near the roadside and not on the internet.
Evolutionary PSO-based emergency monitoring geospatial edge service chain in the emergency communication network
Published in International Journal of Digital Earth, 2023
Sheng He, Xicheng Tan, Yanfei Zhong, Meng Huang, Zhiyuan Mei, You Wan, Huaming Wang
With the development of manufacturing, the performance of end devices has been greatly enhanced, whereas the price has dropped, making the application of edge computing grow considerably (Wu et al. 2022). Certain less computation-intensive tasks can be assigned directly to edge computing nodes to decrease latency remarkably (C. Chen et al. 2023; Ujjwal et al. 2019). In 2014, the European Telecommunications Standards Institute introduced the concept of mobile edge computing (MEC), which enables mobile users to obtain services from nearby BSs (Khan et al. 2019). In the beginning, MEC mainly focused on task offloading in stable cellular or wireless networks. Furthermore, many researchers are concerned with providing edge computing services in constrained environments in remote or disaster areas. Based on the UAV MEC architecture, Cheng et al. (2021) proposed a joint deep reinforcement learning framework to learn joint task unloading and energy allocation decisions. Z. Chen, Xiao, and Han (2020) established a multilevel edge network resource optimization model to optimize the functional selection of UAVs in MEC networks and solved the optimal unloading mode and resource utilization plan based on a Markov decision process algorithm. Such research is crucial for the rapid establishment of an edge computing environment based on emergency communication networks and the provision of computing services after disasters.
METAhaul framework of HPON for smart city access networks
Published in Journal of the Chinese Institute of Engineers, 2020
Kuo-Chang Feng, San-Liang Lee, Ching-Sheu Wang
Evolving toward smart cities, mobile communication facilities, sensor devices in smart buildings/transportation, and Mobile Edge Computing (MEC) servers will be interconnected (Santos et al. 2018). The traffic demand of all networking sectors will rise rapidly. With the mobile Customer Premises Equipment (CPE) being used as the default computing device for many consumers, a huge increase in wireless and mobile data traffic is expected to cope with the increasing time spent online and data-intensive applications. Therefore, mobile networks have to continuously evolve to meet the operator demands of coverage, capacity, and efficiency with emerging technologies. In particular, the demands of the diverse variety of usage scenarios in the fifth generation (5G) cellular networks become very challenging to meet, for all associate networks, from core and Radio Access Networks (RAN) to transport networks (Shafi et al. 2017; Lema et al. 2017). The smart city market is growing at a rapid pace driven by 5G applications, Internet of Things (IoT), and other broadband needs. The smart city industry stands at the threshold of making one of the biggest changes to the underlying network architecture.
A real-time logo detection system using data offloading on mobile devices
Published in Cyber-Physical Systems, 2018
Mobile-edge computing (MEC) is a new framework to provide co-locating computing and storage resources at the edge. In MEC systems, mobile end users offload resource-hunger tasks to the back-end server. Many works have been proposed to address computation offloading problem in MEC. In [7], Rudenko et al. found that significant power saving can be achieved by offloading certain tasks of realistic size. Gonzalo et al. in [8] proposed an adaptive offloading mechanism that leverages the execution history. They collect the consumed resources and the state of the device and use this information to perform an offloading. In [9], Huang et al. proposed a dynamic offloading algorithm based on Lyapunov optimisation to save energy and meet given application execution time requirement. Barbera et al. conducted various experiments in [10] and showed that wireless transmission affects the overall offloading performance in a large degree. Wu et al. considered network unavailability in mobile offloading in [11] and proposed a new offloading decision and application partition algorithms to minimise energy consumption and execution time. Wen et al. in [12] proposed a new offloading algorithm by configuring the clock frequency of the mobile devices to minimise the energy consumption. In [13], Xian et al. proposed a new offloading algorithm. By setting a timeout, their work can achieve good offloading performance without estimating the computation time in advance. These approaches only consider the single user computation offloading problem, which may be not practical in real applications.