Mobile Networks and Applications

, Volume 24, Issue 6, pp 2002–2013 | Cite as

Indoor Target Intrusion Detection via Iterative Transfer Learning Based Cognitive Sensing

  • Mu Zhou
  • Yaoping LiEmail author
  • Zhian Deng
  • Yongliang Sun
  • Yanmeng Wang
  • Zengshan Tian


The traditional localization technology, which requires the target carrying device and participating in localization process, transmits the signal to be received by the device to estimate the target locations, but it perceives the changes in the environment weakly as well as limits the application of localization services. Based on this, we propose a new indoor target intrusion detection approach based on iterative transfer learning without special device. In concrete terms, this approach relies on iterative transfer learning to use the signal received by Monitor Points (MPs) to determine whether there is a target intrusion in the environment, infer the area where the target is located, and consequently achieve autonomous cognitive sensing of environmental change. Specifically, first of all, we use the Received Signal Strength (RSS) data collected offline and their corresponding silence and intrusion labels to construct a source domain. Second, the cross-validation is applied to perform preliminary calibration on the RSS data collected online to obtain their corresponding pseudo-labels, and then these pseudo-labels are utilized to construct the target domain. Finally, the labels of target domain are obtained through the iterative intra-class transfer learning between the source and target domains. Furthermore, the experimental results show that the proposed approach can not only achieve high intrusion detection accuracy with a small number of RSS data, but also perform well in the cognitive sensing of the change of MPs.


WLAN Intrusion detection Cross-validation Pseudo-label Iterative transfer learning 



This work is supported in part by the National Natural Science Foundation of China (61771083, 61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299),Postgraduate Scientific Research and Innovation Project of Chongqing (CYS18240,CYS17221), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental Science and Frontier Technology Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083), and University Outstanding Achievement Transformation Project of Chongqing (KJZH17117).

Author’s Contributions

The authors have contributed jointly to all parts on the preparation of this manuscript, and all authors read and approved the final manuscript.

Compliance with Ethical Standards

Competing interests

The authors declare that they have no competing interests.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mu Zhou
    • 1
  • Yaoping Li
    • 1
    Email author
  • Zhian Deng
    • 2
  • Yongliang Sun
    • 3
  • Yanmeng Wang
    • 1
  • Zengshan Tian
    • 1
  1. 1.School of Communication and Information EngineeringChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  3. 3.School of Computer Science and TechnologyNanjing Tech UniversityNanjingChina

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