J
Jianxin Zhao
Researcher at University of Cambridge
Publications - 21
Citations - 500
Jianxin Zhao is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Edge device. The author has an hindex of 8, co-authored 13 publications receiving 414 citations. Previous affiliations of Jianxin Zhao include Beijing University of Posts and Telecommunications & Beijing Institute of Technology.
Papers
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Journal ArticleDOI
A Survey of Incentive Mechanisms for Participatory Sensing
Hui Gao,Chi Harold Liu,Wendong Wang,Jianxin Zhao,Zheng Song,Xin Su,Jon Crowcroft,Kin K. Leung +7 more
TL;DR: This paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature.
Journal ArticleDOI
Building accountability into the Internet of Things: the IoT Databox model
Andy Crabtree,Tom Lodge,James Colley,Chris Greenhalgh,Kevin Glover,Hamed Haddadi,Yousef Amar,Richard Mortier,Qi Li,John P. Moore,Liang Wang,Poonam Yadav,Jianxin Zhao,Anthony Brown,Lachlan Urquhart,Derek McAuley +15 more
TL;DR: The IoT Databox model is proposed as an in principle means of enabling accountability and providing individuals with the mechanisms needed to build trust into the IoT.
Proceedings ArticleDOI
Personal Data Management with the Databox: What's Inside the Box?
Richard Mortier,Jianxin Zhao,Jon Crowcroft,Liang Wang,Qi Li,Hamed Haddadi,Yousef Amar,Andy Crabtree,James Colley,Tom Lodge,Tosh Brown,Derek McAuley,Chris Greenhalgh +12 more
TL;DR: This paper elaborates on the proposed Databox, a collection of physical and cloud-hosted software components that provide for an individual data subject to manage, log and audit access to their data by other parties, describing the software architecture it is developing, and the current status of a prototype implementation.
Proceedings ArticleDOI
Privacy-Preserving Machine Learning Based Data Analytics on Edge Devices
TL;DR: It is argued that to avoid those costs, reduce latency in data processing, and minimise the raw data revealed to service providers, many future AI and ML services could be deployed on users' devices at the Internet edge rather than putting everything on the cloud.
Journal ArticleDOI
Energy-Efficient Event Detection by Participatory Sensing Under Budget Constraints
TL;DR: This paper introduces a novel distributed and energy-efficient event detection framework under task budget constraint, and presents two novel centralized detection algorithms that make use of the Minimum Cut theory and support vector machine (SVM)-based pattern recognition techniques.