Big data and smart computing in network systems

Jiming Chen, Kaoru Ota, Lu Wang, Jianping He
2019 Peer-to-Peer Networking and Applications  
In recent years, big data and smart computing are emerging research fields that have drawn much attention from computer science, communication and control area, information technology as well as from social sciences and other disciplines. With the increasing deployment of new monitoring and sensing devices, and the advanced measurement infrastructures, a large amount of data is collected in network systems. Network systems have become data-driven, which call for big data, smart computing
more » ... and solutions (e.g., predictive data mining, robust data analytics, artificial intelligence, distributed and high-performance computing, efficient data management, privacy-preserving data publishing, etc.). With the growing volume, speed and types of big data from the network systems, smart computing is imperative to guarantee critical functionalities in network systems, such as real-time widearea situational awareness, dynamic data management, efficiency optimization and control, robust network performance, etc. The focus of this special issue, "Big data and smart computing in network systems", is on the improvement of network systems operations and applications with emphasis on big data and smart computing technologies. We solicit and publish original research papers on the theories, algorithms, and methodologies that highlight emerging data processing technologies for big data and smart computing. This special issue received 25 submissions and only 13 of them were finally accepted. The accepted papers address a variety of topics, including techniques, models and algorithms for big data in network systems, and networked infrastructure for smart computing, and cloud and grid computing, and data processing approaches for P2P communication networks, etc. A brief overview of the papers included in the special issue is provided below. Yu et al. [1] consider the problem that traditional compressive sensing methods are open-loop and the coordinator cannot obtain the recovery quality, which will impact the diagnosis results based on the recovered data. A novel close-loop structure is designed for wireless ECG monitoring, which can maintain the recovery quality and the energy efficiency at a high level. Moreover,
doi:10.1007/s12083-019-00784-6 fatcat:s4lqf6ngjzd7fkshiff5jzxu2i