L
Linghe Kong
Researcher at Shanghai Jiao Tong University
Publications - 243
Citations - 5296
Linghe Kong is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 31, co-authored 188 publications receiving 3473 citations. Previous affiliations of Linghe Kong include McGill University & Singapore University of Technology and Design.
Papers
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Journal ArticleDOI
Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism
TL;DR: This work proposes a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously, and designs a data authority management method to regulate the access to sensor data.
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Millimeter Wave Communication: A Comprehensive Survey
TL;DR: A taxonomy based on the layered model is presented and an extensive review on mmWave communications to point out the inadequacy of existing work and identify the future work.
Journal ArticleDOI
CDC : Compressive Data Collection for Wireless Sensor Networks
TL;DR: This paper adopts a power-law decaying data model verified by real data sets and proposes a random projection-based estimation algorithm for this data model, which requires fewer compressed measurements and greatly reduces the energy consumption.
Journal ArticleDOI
Millimeter-Wave Wireless Communications for IoT-Cloud Supported Autonomous Vehicles: Overview, Design, and Challenges
TL;DR: This article proposes the novel design of a vehicular mmWave system combining the advantages of the Internet of Things and cloud computing, which supports vehicles sharing multi-gigabit data about the surrounding environment and recognizing objects via the cloud in real time.
Proceedings ArticleDOI
Data loss and reconstruction in sensor networks
TL;DR: An environmental space time improved compressive sensing (ESTICS) algorithm to optimize the missing data estimation and shows that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy.