772 research outputs found
A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art
Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research
Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities
Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems
Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems
The ever-increasing growth in the number of connected smart devices and
various Internet of Things (IoT) verticals is leading to a crucial challenge of
handling massive amount of raw data generated from distributed IoT systems and
providing real-time feedback to the end-users. Although existing
cloud-computing paradigm has an enormous amount of virtual computing power and
storage capacity, it is not suitable for latency-sensitive applications and
distributed systems due to the involved latency and its centralized mode of
operation. To this end, edge/fog computing has recently emerged as the next
generation of computing systems for extending cloud-computing functions to the
edges of the network. Despite several benefits of edge computing such as
geo-distribution, mobility support and location awareness, various
communication and computing related challenges need to be addressed in
realizing edge computing technologies for future IoT systems. In this regard,
this paper provides a holistic view on the current issues and effective
solutions by classifying the emerging technologies in regard to the joint
coordination of radio and computing resources, system optimization and
intelligent resource management. Furthermore, an optimization framework for
edge-IoT systems is proposed to enhance various performance metrics such as
throughput, delay, resource utilization and energy consumption. Finally, a
Machine Learning (ML) based case study is presented along with some numerical
results to illustrate the significance of edge computing.Comment: 16 pages, 4 figures, 2 tables, submitted to IEEE Wireless
Communications Magazin
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