activity
20132021
most citedDeep Reinforcement Learning for Smart Home Energy Management

471 citations · 1.4k across the 55 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

cs.CR2020

Detecting Colluding Sybil Attackers in Robotic Networks using Backscatters

Yong Huang, Wei Wang, Tao Jiang +1

Due to the openness of wireless medium, robotic networks that consist of many miniaturized robots are susceptible to Sybil attackers, who can fabricate myriads of fictitious robots…

eess.SP2020

Peer-to-Peer Localization for Single-Antenna Devices

Xianan Zhang, Wei Wang, Xuedou Xiao +3

Some important indoor localization applications, such as localizing a lost kid in a shopping mall, call for a new peer-to-peer localization technique that can localize an individua…

cs.IT2020

Joint Energy Harvest and Information Transfer for Energy Beamforming in Backscatter Multiuser Networks

Wenyuan Ma, Wei Wang, Tao Jiang

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamfor…

eess.SY2020★ 4 cited

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

Liang Yu, Yi Sun, Zhanbo Xu +4

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic bur…

eess.SP2020★ 1 cited

Robot-assisted Backscatter Localization for IoT Applications

Shengkai Zhang, Wei Wang, Sheyang Tang +2

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Loca…

cs.CR2020

Authenticating On-Body IoT Devices: An Adversarial Learning Approach

Yong Huang, Wei Wang, Hao Wang +2

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and sa…