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An Enhanced-Time Difference of Arrival Technique for Estimating Mobile Station Position in Wireless Sensor Networks

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Adekunle A. Adeyelu, Onaji J. Onah, Iwuese J. Orban

Adekunle A Adeyelu, Onaji J Onah and Iwuese J Orban. An Enhanced-Time Difference of Arrival Technique for Estimating Mobile Station Position in Wireless Sensor Networks. International Journal of Computer Applications 175(22):5-11, October 2020. BibTeX

	author = {Adekunle A. Adeyelu and Onaji J. Onah and Iwuese J. Orban},
	title = {An Enhanced-Time Difference of Arrival Technique for Estimating Mobile Station Position in Wireless Sensor Networks},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2020},
	volume = {175},
	number = {22},
	month = {Oct},
	year = {2020},
	issn = {0975-8887},
	pages = {5-11},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2020920181},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


There have been continuous search several hundred years from now for techniques to track an object from a remote location given certain facts. One of such is locating a Mobile Station (MS) which is an object within a cellular network. Existing outdoor techniques to locate this MS require optimization both in terms of accuracy and latency. In this paper, an Enhanced Mobile Station Positioning (MSP) model for Wireless Sensor Networks was developed and its performance was appraised using accuracy and latency metrics in line with Time Difference of Arrival (TDOA) procedure. This model used the difference in arrival time of the signal received at four base stations (BS) positioned within the neighborhood of the Mobile Station (MS) to locate the MS. The TDOA forms a hyperbola on which the MS can be found. The mathematical model was derived by solving the hyperbolas with Taylor’s series expansion formula. The estimated position of the MS was calculated using Linear Least Square (LLS) solution in a repetitive manner. The performance of the formulated model was evaluated using accuracy and latency metrics. The result showed that the model located the MS within error distances of 189m for 67% and 300m for 95% of the time it was deployed. This result outclassed the same technique using three BS which located the MS within 235m at 67% deployment and 349m at 95% of the time the model was used. This gave approximately 14% improvement in accuracy. Simulation results also revealed that the latency experienced when the BSs were increased from three to four increased by 42.86% (0.085 seconds). It can be concluded that increasing the number of BSs from three to four gave a significant better accuracy in locating a MS within the BSs.


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Mobile Station, Base Station, Time Difference of Arrival.