@inproceedings{2015_1C_apec,
  title={APEC: Auto planner for efficient configuration of indoor positioning system},
  author={Jin, Ming and Jia, Ruoxi and Spanos, Costas},
  booktitle={Int. Conf. Mobile Ubiquitous Comput. Syst. Services Technol.(UBICOMM)},
  pages={100--107},
  year={2015},
  url_pdf={apec.pdf},
  url_link={https://www.iaria.org/conferences2015/AwardsUBICOMM15.html},
  note={<font style="color:#FF0000">(Invitation for IARIA Journals)</font>},
  abstract={Fingerprints-based methods have been prevailing in indoor positioning systems, whereas they have certain drawbacks that fingerprints collection in the offline phase requires considerable manpower and time. Auto Planner for Efficient Configuration (APEC) systematically exploits router setups and fingerprints allocations over space by taking into account user preferences and budget constraints. The task of configuration is formulated as an optimization problem, whose objective is the expected loss based on the Hierarchical Bayesian Signal Model (HBSM) and theoretical results on the misclassification rates. To reduce the computational complexity of large-scale problems, two heuristics are employed, i.e., the coordinate descent and the router-fingerprints decoupling, which are validated by simulation analysis. Experiments with three mobile devices (Android, iPad, iPhone) in two setups (7 or 9 access points) verify that the expected loss is a reliable predictor of the actual loss of the system (objective consistency), and that APEC outperforms the random and uniform approaches (solution superiority). Since APEC focuses on the system configuration in the planning stage, it can be combined with other fingerprinting processes in the online phase to improve the utility of the system},
  keywords={Optimization, Data mining, Smart city, Energy system}
}

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