SE-Loc: Security-Enhanced Indoor Localization with Semi-Supervised Deep Learning

Qianwen Ye, Xiaochen Fan, Hongxia Bie, Deepak Puthal, Tao Wu, Xudong Song, Gengfa Fang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Wireless indoor localization has become unavoidable for industrial indoor location-based services. Given the ubiquitous deployment of wireless access points (APs), WiFi fingerprinting of Received Signal Strength (RSS) has been widely adopted for indoor localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities are exploited, mobile users may confront indoor localization mismatches, faults and even localization system failures. In this paper, we propose SE-Loc, a semi-supervised learning-based technique to enhance security and resiliency of fingerprint-based localization. The architecture of SE-Loc consists of two parts: (1) a correlation-based AP selection for processing RSS fingerprints and fingerprint-image generation, and (2) a deep learning model based on a denoising autoencoder and convolutional neural networks for robust feature learning and location matching. Extensive experiments show that under potential AP attacks, SE-Loc demonstrates superior performance on indoor localization over the state-of-the-art methods. With up to 100 malicious attacking APs in the UJIIndoorLoc edge server, SE-Loc can still achieve the lowest error fluctuation of 1.7 m and the highest average localization accuracy of 8.9 m.

Original languageBritish English
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Network Science and Engineering
StateAccepted/In press - 2022


  • Computational Intelligence
  • Deep learning
  • Feature extraction
  • Interference
  • Internet of Things
  • Internet of Things
  • Location awareness
  • Security
  • Semi-supervised Deep Learning
  • Wireless fidelity
  • Wireless Indoor Localization


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