Bin Hu 0022

dblp:00/6381-22 · DBLP profile ↗
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5ranked-venue papers
1as first author
4since 2021 · last 2025
0000-0003-0954-1551ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 5 · 1 first-author · 4 since 2021
YearPublicationVenuePosition
2025 Joint multidimensional features for LoRa reception in burst traffic
Bin Hu 0022, Zhimeng Yin 0001, Shuai Wang 0021, Shuai Wang 0008, Zhuqing Xu, Tian He 0001
Comput. Networks2
2024 NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways
Jiazhao Wang, Wenchao Jiang, Ruofeng Liu, Bin Hu 0022, Demin Gao, Shuai Wang 0008
NSDI4
2023 Time Synchronization Based on Cross-Technology Communication for IoT Networks
abstract
Time synchronization is a fundamental requirement for wireless communication systems to work properly. Most of the existing studies focus on time synchronization among homogeneous devices. This work investigates time synchronization with heterogeneous technologies (e.g., WiFi, ZigBee, and Bluetooth) which is important for the rising Internet of Thing (IoT) scenarios where heterogeneous devices coexist. Recent advances in cross-technology communication (CTC) break the wall between heterogeneous wireless devices. In this work, we propose a new time synchronization strategy based on the CTC technique and provide a technique called TimeBee, which takes the advantage of coordination from a WiFi device to assist ZigBee devices for time synchronization. An effective method is employed so that ZigBee nodes are coordinated for time synchronization based on the received timestamps from WiFi devices. The experimental results show that TimeBee achieves global time synchronization with low time errors.
Demin Gao, Yunhuai Liu, Bin Hu 0022, Lei Wang 0042, Weiwei Chen 0004, Yongrui Chen 0001, Tian He 0001
IEEE Internet Things J.3
2021 Leveraging Fine-Grained Self-correlation in Detecting Collided LoRa Transmissions
Bin Hu 0022, Xiaolei Zhou 0001, Shuai Wang 0008
WASA (3)2
2020 SCLoRa: Leveraging Multi-Dimensionality in Decoding Collided LoRa Transmissions
abstract
LoRa as a representative of Low-Power Wide Area Networks (LPWAN) technologies has emerged as an attractive communication platform for the Internet of Things. Since its dense deployment, signal collisions at base stations caused by concurrent transmissions degrade network performance. Existing approaches utilize the signal feature, e.g., frequency, to separate packets from collisions. They do not work well in burst traffic networks because the feature is not stable or fine-grained enough and the information for directed signal separation is not sufficient. In this paper, we leverage multidimensional information and propose a novel PHY layer approach called SCLoRa to decode collided LoRa transmissions. SCLoRa utilizes cumulative spectral coefficient, which integrates both frequency and power information, to separate symbols in the overlapped signal. The practical factors of channel fading, similar symbol boundary, and spectrum leakage are taken into account. The SCLoRa design requires neither hardware nor firmware changes in commodity devices – a feature allowing fast deployment on LoRa base stations. We implement and evaluate SCLoRa on USRP B210 base stations and commodity LoRa devices (i.e., SX1278). The experiment results in different scenarios with different radio parameters show that the throughput of SCLoRa is 3× than the state-of-the-art.
Bin Hu 0022, Zhimeng Yin 0001, Shuai Wang 0021, Zhuqing Xu, Tian He 0001
ICNP1