A Greedy Heuristic Based Beacons Selection for Localization

Wi-Fi based localization technology is a hot issue in recent indoor localization research. Due to the exist of obstacles and signal fluctuation in indoor environment, RSSI measurements from beacons are often noisy. To solve this problem, this paper first proposes a greedy heuristic algorithm to choose optimal beacons involved in localization. During the localization process, the reference points in the area covered by the selected beacons form triangles. The gravity centers of the triangles jointly determine the target’s location. Finally, a comprehensive set of simulations are provided to invalidate the performance of the proposed algorithm.

[1]  Yunhao Liu,et al.  Shake and walk: Acoustic direction finding and fine-grained indoor localization using smartphones , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[2]  Richard P. Martin,et al.  The Robustness of Localization Algorithms to Signal Strength Attacks: A Comparative Study , 2006, DCOSS.

[3]  Binwen Fan,et al.  The indoor wireless location technology research based on WiFi , 2014, 2014 10th International Conference on Natural Computation (ICNC).

[4]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[5]  Holger Claussen,et al.  Wireless RSSI fingerprinting localization , 2017, Signal Process..

[6]  Luca Reggiani,et al.  Design of RSSI based fingerprinting with reduced quantization measures , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[7]  Rudolf Mathar,et al.  A deep learning approach to fingerprinting indoor localization solutions , 2017, 2017 27th International Telecommunication Networks and Applications Conference (ITNAC).

[8]  Tao Li,et al.  Secure Crowdsourced Indoor Positioning Systems , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[9]  Tarek F. Abdelzaher,et al.  Range-free localization schemes for large scale sensor networks , 2003, MobiCom '03.

[10]  Min Sheng,et al.  3D Indoor localization based on spectral clustering and weighted backpropagation neural networks , 2017, 2017 IEEE/CIC International Conference on Communications in China (ICCC).

[11]  Wee-Seng Soh,et al.  A survey of calibration-free indoor positioning systems , 2015, Comput. Commun..

[12]  Pei Zhang,et al.  PANDAA: physical arrangement detection of networked devices through ambient-sound awareness , 2011, UbiComp '11.

[13]  Aaron Striegel,et al.  Face-to-Face Proximity EstimationUsing Bluetooth On Smartphones , 2014, IEEE Transactions on Mobile Computing.