Deployment Optimization of Indoor Positioning Signal Sources with Fireworks Algorithm

  • Jianhui Zhao
  • Shiqi Wen
  • Haojun Ai
  • Bo CaiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)


Spatial deployment of signal sources affects performance of indoor positioning systems, thus has received more attentions in recent years. This paper presents a FWA method from fireworks algorithm, to provide the optimal deployment solution. Taking fine chromosomes as fireworks, the explosion factors are set including the number of explosion sparks and the radius of all explosion sparks. The supplemented individuals are produced from explosion and random generation, which helps increase the diversity of population and guarantee the qualities of individuals. After crossover and mutation, population evolves to the next generation. The optimal result from evolutions refers to a deployment solution, i.e., certain number of signal sources with their locations. The FWA algorithm has been tested to have good convergence ability by a series of experiments, with iBeacons based indoor positioning system in an underground parking lot and the fingerprint based indoor location method. Compared with the usually used optimization algorithms, FWA has the best searching ability in single-objective and multi-objective cases, and it obtains the best optimization result considering only positioning error, or both positioning error and the cost of iBeacons. Therefore, the proposed FWA provides optimal deployment of signal sources for indoor positioning systems.


Spatial deploying Fireworks method Indoor position  Fingerprint 



This work was supported by the National Key Research and Development Program of China (Project No. 2016YFB0502201).


  1. 1.
    Jung, S.H., Han, D.: Automated construction and maintenance of wi-fi radio maps for crowdsourcing-based indoor positioning systems. IEEE Access 6, 1764–1777 (2018)CrossRefGoogle Scholar
  2. 2.
    Chen, K., Wang, C., Yin, Z.: Slide: towards fast and accurate mobile fingerprinting for wi-fi indoor positioning systems. IEEE Sens. J. 18(3), 1213–1223 (2018)CrossRefGoogle Scholar
  3. 3.
    Popoola, O.R., Sinanovic, S.: Design and analysis of collision reduction algorithms for LED-based indoor positioning with simulation and experimental validation. IEEE Access 6, 10754–10770 (2017)CrossRefGoogle Scholar
  4. 4.
    Zheng, Z., Liu, L., Zhao, C.: High accuracy indoor positioning scheme using single LED and camera. Electron. Lett. 54(4), 227–229 (2018)CrossRefGoogle Scholar
  5. 5.
    Lindo, A., García, E., Ureña, J.: Multiband waveform design for an ultrasonic indoor positioning system. IEEE Sens. J. 15(12), 7190–7199 (2015)CrossRefGoogle Scholar
  6. 6.
    Alvarez, Y., Heras, F.L.: ZigBee-based sensor network for indoor location and tracking applications. IEEE Lat. Am. Trans. 14(7), 3208–3214 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhang, X., Wong, K.S., Lea, C.T.: Unambiguous association of crowd-sourced radio maps to floor plans for indoor localization. IEEE Trans. Mobile Comput. 17, 488–502 (2017)CrossRefGoogle Scholar
  8. 8.
    Zou, H., Chen, Z., Jiang, H.: Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon. In: IEEE International Symposium on Inertial Sensors and Systems, pp. 1–4. IEEE (2017)Google Scholar
  9. 9.
    Menzies, T., Greenwald, J., Frank, A.: Data mining static code attributes to learn defect predictors. IEEE Trans. Softw. Eng. 33(1), 2–13 (2016)CrossRefGoogle Scholar
  10. 10.
    Dhillon, S.S., Chakrabarty, K.: Sensor placement for effective coverage and surveillance in distributed sensor networks. In: Wireless Communications and Networking, 2003. WCNC 2003, pp. 1609–1614. IEEE (2003)Google Scholar
  11. 11.
    Zhou, M., Xu, K.: Error bound analysis of indoor wi-fi location fingerprint based positioning for intelligent Access Point optimization via Fisher information. Comput. Commun. 86(C), 57–74 (2016)CrossRefGoogle Scholar
  12. 12.
    Chen, X., Zou, S.: Improved wi-fi indoor positioning based on particle swarm optimization. IEEE Sens. J. 99, 1 (2017)Google Scholar
  13. 13.
    Chen, C.M., Pi, D.C., Fang, Z.R.: Artificial immune algorithm applied to short-term prediction for mobile object location. Electron. Lett. 48(17), 1061–1062 (2012)CrossRefGoogle Scholar
  14. 14.
    Eldeeb, H., Arafa, M., Saidahmed, M.T.F.: Optimal placement of access points for indoor positioning using a genetic algorithm. In: Computer Engineering and Systems, pp. 306–313 (2017)Google Scholar
  15. 15.
    Kim, D.W., Park, G.J., Lee, J.H.: Hybridization algorithm of fireworks optimization and generating set search for optimal design of IPMSM. IEEE Trans. Magn. 53(6), 1–4 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.Collaborative Innovation Center of Geospatial TechnologyWuhanChina

Personalised recommendations