Implementation of Geospatial Labeling using Time of Arrival and Cramer-Rao bound approach

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Internet of Things (IoT) is one of the most emerging technology worldwide and also plays pivotal role in sensing data and also provide communication between “things”. In this paper, we implement calculation for geospatial labeling for Internet-of-Things (IoT) sort applications, which we indicate as location-of-things (LoT). The hidden thought of LoT applications is to utilize minimal effort of TW-ToA extending gadgets to perform restriction of labels. Two Way(TW) is a agreeable technique for deciding the range between two radio handset units. At the point when synchronization of the oscillators of the included transmitters is not reasonable, henceforth the tickers vary, at that point applying the estimation as a two courses go to the beneficiary and reflected back to the transmitter makes up for a portion of the stage contrasts between the oscillators included. We first propose TW-ToA localization algorithms may encounter execution debasement in situations where a portion of the APs are outside the correspondence scope of the labels. We at that point demonstrate that we can make utilization of the audible data (which demonstrates whether an AP is capable or unfit to speak with the labels). We also re-formulate the restriction issue as a factual nonlinear estimation issue. To avoid ambiguity problem that arises only at few  APs this has been sorted using Cramer-Rao bound approach.

     


  • Keywords


    IOT, LOT, two-way time-of-arrival ranging TW – TOA , wireless sensor networks(WSN) , localization, audibility.

  • References


      [1] E. Arias-de Reyna and P. M. Djuric, “Indoor localization with rangebased measurements and little prior information,” IEEE Sensors J., vol. 13, no. 5, pp. 1979–1987, 2013.

      [2] G. Wang, S. Cai, Y. Li, and M. Jin, “Second-order cone relaxation for TOA-based source localization with unknown start transmission time,” IEEE Trans. Veh. Technol., vol. 63, no. 6, pp. 2973–2977, Jul. 2014.

      [3] I. Chatzigiannakis, J. P. Drude, H. Hasemann, and A. Kröller, “Developing smart homes using the Internet of Things: How to demonstrate your system,” in Distributed, Ambient, and Pervasive Interactions. New York, NY, USA: Springer, 2014, pp. 415–426.

      [4] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of things (IOT): A vision, architectural elements, and future directions,” Future Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013.

      [5] N. Amiot, T. Pedersen, M. Laaraiedh, and B. Uguen, “A hybrid positioning method based on hypothesis testing,” IEEE Lett. Wireless Commun., vol. 1, no. 4, pp. 348–351, Aug. 2012.

      [6] W. R. Jung, S. Bell, A. Petrenko, and A. Sizo, “Potential risks of wifi-based indoor positioning and progress on improving localization functionality,” in Proc. 4th ACM SIGSPATIAL Int. Workshop Indoor Spatial Awareness, 2012, pp. 13–20.

      [7] Y. Wang, X. Ma, and G. Leus, “Robust time-based localization for asynchronous networks,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4397–4410, Sep. 2011.

      [8] IEEE ISO/IEC 24730-5 information technology – Real-time locating systems (RTLS) – Part 5: Chirp spread spectrum (CSS) at 2.4 GHz air interface, 2010.

      [9] (2015, Sep.). Timedomain, Timedomain Web-Site [Online]. Available: http://www.timedomain.com/

      [10] K. W.-K. Lui, F. K. Chan, and H.-C. So, “Semidefinite programming approach for range-difference based source localization,” IEEE Trans. Signal Process., vol. 57, no. 4, pp. 1630–1633, Apr. 2009. NEVAT et al.: LOCATION OF THINGS 185


 

View

Download

Article ID: 11170
 
DOI: 10.14419/ijet.v7i2.4.11170




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.