Editorial: Emerging Trends on Data Analytics at the Network Edge
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Editorial: Emerging Trends on Data Analytics at the Network Edge Deyu Zhang 1 & Mianxiong Dong 2 & Geyong Min 3
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Data analytics at the network edge has received extensive attentions from both the academia and industry due to its potential in various smart applications, such as augmented/ virtual reality, and real-time video surveillance. However, the computational intensiveness and complex full-stack system design have impeded its flourish in our daily life. This special issue, i.e., “Emerging Trends on Data Analytics at the Network Edge”, solicits and publishes original research papers on the algorithms, system design, and methodologies to improve the performance of data analytics performed on the network edge. After a rigorous peer review process, this special issue accepted only 11 papers from 27 submissions. The topics of the accepted paper range from content prefeching to hardware performance measurement. A brief review of the papers included in the special issue is provided as follows. The first paper, “Efficient Caching Strategy in Wireless Networks with Mobile Edge Computing” by Ying Chen, Shuang Chen, et al. investigates caching in a multi-content provider and multiple users scenario. Aiming at maximizing the caching utility, the authors establish a game model of caching resource competition and prove the existence of Nash equilibrium. The second paper, “A System for Real-time Intervention in Negative Emotional Contagion in a Smart Classroom Deployed under Edge Computing Service Infrastructure” by
This article is part of the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong * Deyu Zhang [email protected] Mianxiong Dong [email protected] Geyong Min [email protected] 1
Central South University, Changsha, China
2
Muroran Institute of Technology, Muroran, Japan
3
University of Exeter, Exeter EX4 4PY, UK
Jian Li, Daqian Shi, et al., designs an emotional contagion model for classroom scene to locate the source of negative emotional contagion. They deploy the emotion recognition system as an edge computing-based service to minimize the response time. The third paper, “Vehicles joint UAVs to Acquire and Analyze Data for Topology Discovery in Large-Scale IoT Systems” by Haojun Teng, Kaoru Ota, et al., proposes a unmanned aerial vehicles topology discovery scheme to discover the physical topology with low-cost and high accuracy. The authors use vehicles as mobile anchors and the cloud platform to analyze the received information to determine undiscovered physical topology. The forth paper, “Energy Efficient Mode Selection, Base Station Selection and Resource Allocation Algorithm in D2D Heterogeneous Networks” by Zhufang Kuang, Gongqiang Li, et al., studies the joint problem of DUEs mode selection, base station selection, channel allocation and power allocation in D2D communications, with the objective to optimize the energy effici
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