Optimized low complexity sensor node positioning in wireless sensor networks

SALMAN, Naveed, GHOGHO, Mounir and KEMP, Andrew (2014). Optimized low complexity sensor node positioning in wireless sensor networks. IEEE Sensors Journal, 14 (1), 39-46.

Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/document/6582519/
Link to published version:: https://doi.org/10.1109/JSEN.2013.2278864
Related URLs:


Localization of sensor nodes in wireless sensor networks (WSNs) promotes many new applications. A longer life time is imperative for WSNs, this requirement constrains the energy consumption and computation power of the nodes. To locate sensors at a low cost, the received signal strength (RSS)-based localization is favored by many researchers. RSS positioning does not require any additional hardware on the sensors and does not consume extra power. A low complexity solution to RSS localization is the linear least squares (LLS) method. In this paper, we analyze and improve the performance of this technique. First, a weighted least squares (WLS) algorithm is proposed, which considerably improves the location estimation accuracy. Second, reference anchor optimization using a technique based on the minimization of the theoretical mean square error is also proposed to further improve performance of LLS and WLS algorithms. Finally, to realistically bound the performance of any unbiased RSS location estimator based on the linear model, the linear Cramer–Rao bound (CRB) is derived. It is shown via simulations that employment of the optimal reference anchor selection technique considerably improves system performance, while the WLS algorithm pushes the estimation performance closer to the linear CRB. Finally, it is also shown that the linear CRB has larger error than the exact CRB, which is the expected outcome.

Item Type: Article
Additional Information: INSPEC Accession Number: 13875746
Research Institute, Centre or Group - Does NOT include content added after October 2018: National Centre of Excellence for Food Engineering
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number: https://doi.org/10.1109/JSEN.2013.2278864
Page Range: 39-46
Depositing User: Naveed Salman
Date Deposited: 06 Jan 2017 12:23
Last Modified: 18 Mar 2021 22:15
URI: https://shura.shu.ac.uk/id/eprint/14561

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics