J
Jiong Jin
Researcher at Swinburne University of Technology
Publications - 147
Citations - 4425
Jiong Jin is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 27, co-authored 125 publications receiving 3039 citations. Previous affiliations of Jiong Jin include Nanyang Technological University & University of Melbourne.
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An Information Framework for Creating a Smart City Through Internet of Things
TL;DR: A framework for the realization of smart cities through the Internet of Things (IoT), which encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system.
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PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid
TL;DR: This work proposes an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby SMs and accurately derive aggregate statistics as the fine-grained Fog level aggregation.
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Collective Behaviors of Mobile Robots Beyond the Nearest Neighbor Rules With Switching Topology
TL;DR: A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots.
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Robust Motion Control of a Linear Motor Positioner Using Fast Nonsingular Terminal Sliding Mode
TL;DR: In this article, the authors proposed a fast nonsingular terminal sliding mode (FNTSM) controller for linear motor (LM)-based direct drive to provide high speed and high precision performance.
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A comprehensive survey of anomaly detection techniques for high dimensional big data
TL;DR: This survey aims to document the state of anomaly detection in high dimensional big data by representing the unique challenges using a triangular model of vertices: the problem, techniques/algorithms, and tools (big data applications/frameworks).