Guest editorial

The emerging applications, such as Augmented and Virtual Realities (AR/VR), Internet of Things (IoT), 4K/8K streaming, raise strong requirements to move computation from the cloud to the edges to be closer to users.There are tremendous possibilities for the network edge, which may include a variety of entities, such as small data centers, end devices, and resource-abundant network nodes.These together provide the network computation and intelligence to users.At the network edge, new computation technologies, such as big data analytics, modern machine learning technology, Artificial Intelligence (AI), blockchain, and security processing, have great potential to be embedded into the network to make it intelligent and trustworthy.On the other hand, Information-Centric Networking (ICN), data center network, Software-Defined Network (SDN), Network Function Virtualization (NFV), and network slicing have emerged as new networking paradigms for fast and efficient delivering and retrieving data.This trend triggers the convergence between emerging networking concepts and new computation technologies to reach the vision of an intelligent edge.To advance such convergence, 10 papers were finally selected for this special issue on edge computation and intelligence。 Computation plays a crucial role at the edge.The first paper, "EA-DFPSO: an intelligent energy-efficient scheduling algorithm for mobile edge networks" coauthored by Y. Lu, L. Liu, J. Gu, J. Panneerselvam, and B. Yuan, presents a novel task scheduling algorithm named Energy-Aware Double-Fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time.The second paper, "Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing" coauthored by Y. Li, T. Wang, Y. Wu, and W. Jia, investigates the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization, and proposes a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs' resource allocations under a given transmission duration.The third paper, "A LoRa-based protocol for connecting IoT edge computing nodes to provide small-data-based services" contributed by K. Nakamura, P. Manzoni, A. Redondi, E. Longo, M. Zennaro, J. Cano, and C. T. Calafate, presents a flexible protocol called LoRaCTP, which is based on LoRa technology that allows data chunks" to be transferred over large distances with very low energy expenditure.The fourth paper, "Multi-agent reinforcement learning for edge information sharing in vehicular networks" coauthored by R. Wang, X.

Abstract licence: CC BY-NC-ND

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