Smartphones Based Crowdsourcing for Indoor Localization

By Chenshu Wu, Zheng Yang, Yunhao Liu. IEEE Transactions on Mobile Computing, 2015

365 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/319dc95989b676aa88eac6f6556a0e305a1b46d0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Shashi Raj Pandey
2. Choong Seon Hong
3. Zheng Yang
4. Yunhao Liu
5. Nguyen H. Tran
6. Xinglin Zhang
7. Feng Yin
8. Medhi Bennis

Citations in the corpus

• FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “Crowdsourcing of virtual maps, such as RSS map or magnetic map, becomes trendy for big multi-storey buildings [35]–[37].”
• A Crowdsourcing Framework for On-Device Federated Learning (2019, background): “, for indoor localization, traffic monitoring, navigation [3], [4], [5], [6]).”
• Incentives for Mobile Crowd Sensing: A Survey (2016, methodology): “Pioneer sensing systems include NoiseTube [6] for noise monitoring, SignalGuru [7] and VTrack [8] for traffic monitoring, CityExplorer [9], SmartTrace [10], Sensorly [11] for 3G/WiFi discovery, and LiFS [12] and TrMCD [13] for indoor localization.”