Grid-Based Monte Carlo Localization for Mobile Wireless Sensor Networks

  • Qin TangEmail author
  • Jing Liang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Localization is an important requirement for wireless sensor networks (WSNs), but the inclusion of GPS receivers in sensor network nodes is often too expensive. Therefore, many solutions focus on static networks and do not consider mobility. In this paper, we analyze the Monte Carlo location (MCL) algorithm and propose an improved method—grid-based MCL. It applies the mobility of nodes to reduce the sampling area and to build an internal grid to predict the behavior of nodes. We investigate the properties of our technology and analyze its performance. The simulation and analysis show that the proposed grid-based MCL not only reduces localization error, but also improves the sampling efficiency.


WSNs Grid-based MCL Mobility 



This work was supported by the National Natural Science Foundation of China (61671138, 61731006) and was partly supported by the 111 Project No. B17008.


  1. 1.
    Chien CH, Hsu CC, Wang WY, Kao WC, Chien CJ: Global localization of Monte Carlo localization based on multi-objective particle swarm optimization. In: 2016 IEEE 6th international conference on consumer electronics-Berlin (ICCE-Berlin). IEEE; 2016. p. 96–7.Google Scholar
  2. 2.
    Saarinen J, Andreasson H, Stoyanov T, Lilienthal AJ. Normal distributions transform Monte-Carlo localization (NDT-MCL). In: 2013 IEEE/RSJ international conference on intelligent robots and systems; Nov 2013. p. 382–9.Google Scholar
  3. 3.
    Nuss D, Yuan T, Krehl G, Stuebler M, Reuter S, Dietmayer K. Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter. In: 2015 IEEE intelligent vehicles symposium (IV); June 2015. p. 1074–81.Google Scholar
  4. 4.
    Doherty L, Pister KSJ, Ghaoui LE. Convex position estimation in wireless sensor networks. In: Proceedings IEEE INFOCOM 2001. Conference on computer communications. Twentieth annual joint conference of the IEEE computer and communications society (Cat. No. 01CH37213), vol. 3; 2001. p. 1655–63.Google Scholar
  5. 5.
    Li CY, Li IH, Chien YH, Wang WY, Hsu CC. Improved Monte Carlo localization with robust orientation estimation based on cloud computing. In: 2016 IEEE congress on evolutionary computation (CEC); July 2016. p. 4522–7.Google Scholar
  6. 6.
    Adewumi OG, Djouani K, Kurien AM. RSSI based indoor and outdoor distance estimation for localization in WSN. In: 2013 IEEE international conference on industrial technology (ICIT); Feb 2013. p. 1534–9.Google Scholar
  7. 7.
    Bellili F, Amor SB, Affes S, Samet A. A new importance-sampling ml estimator of time delays and angles of arrival in multipath environments. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP); May 2014. p. 4219–23.Google Scholar
  8. 8.
    Al-Jazzar SO, Strangeways HJ, McLernon DC. 2-d angle of arrival estimation using a one-dimensional antenna array. In: 2014 22nd European signal processing conference (EUSIPCO); Sept 2014. p. 1905–9.Google Scholar
  9. 9.
    Militzer B, Driver KP. Development of path integral Monte Carlo simulations with localized nodal surfaces for second-row elements. Phys Rev Lett. 2015;115:176403. Scholar
  10. 10.
    Hartung S, Kellner A, Rieck K, Hogrefe D. Monte Carlo localization for path-based mobility in mobile wireless sensor networks. In: 2016 IEEE wireless communications and networking conference; Apr 2016. p. 1–7.Google Scholar
  11. 11.
    Baggio A, Langendoen K. Monte Carlo localization for mobile wireless sensor networks. Ad Hoc Netw. 2008;6(5):718–33. Scholar
  12. 12.
    Zhu H, Mao J, Wang L, Fu L, Guo N. The study on point average energy consumption by Monte Carlo in large-scale wireless sensor networks. In: 2015 IEEE international conference on information and automation; Aug 2015. p. 1700–3.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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