Y
Ye Wang
Researcher at Harbin Institute of Technology
Publications - 97
Citations - 982
Ye Wang is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Cognitive radio & Throughput. The author has an hindex of 11, co-authored 96 publications receiving 629 citations. Previous affiliations of Ye Wang include University of Ontario Institute of Technology & Harbin Institute of Technology Shenzhen Graduate School.
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Journal ArticleDOI
Energy-theft detection issues for advanced metering infrastructure in smart grid
TL;DR: In this paper, an attack tree based threat model is presented to illustrate the energy-theft behaviors in AMI and summarize the current AMI energytheft detection schemes into three categories, i.e., classification-based, state estimation-based and game theory-based ones.
Journal ArticleDOI
Network Utility Maximization Resource Allocation for NOMA in Satellite-Based Internet of Things
TL;DR: Taking into account the condition of successive interference cancellation decoding, a practical solution under the Karush–Kuhn–Tucker (KKT) conditions is proposed, and an optimal solution is introduced by using the particle swarm optimization (PSO) algorithm for the joint resource allocation problem.
Journal ArticleDOI
Physical-Layer Authentication for Internet of Things via WFRFT-Based Gaussian Tag Embedding
TL;DR: This article proposes a Gaussian-tag-embedded physical-layer authentication (GTEA) scheme by using a weighted fractional Fourier transform (WFRFT) and shows that with the deliberately designed Gaussian tag, the GTEA scheme is robust against spoofing and replaying attacks.
Proceedings ArticleDOI
Deep Learning Based Plant Disease Detection for Smart Agriculture
TL;DR: A Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources and a lightweight DNN approach that can run on Internet of Things (IoT) devices with constrained resources are proposed.
Journal ArticleDOI
Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT
TL;DR: The proposed deep learning-based long-term power allocation (DL-PA) scheme can efficiently derive a more accurate decoding order than the conventional solution and improve the performance of the S-IoT NOMA downlink system, in terms of long- term network utility, average arriving rate, and queuing delay.