Title :
Low complexity least-square estimator for RSS-based localization in Wireless Sensor Networks
Author :
Alhasant, A.I. ; Sharif, B.S. ; Tsimenidis, C.C. ; Neasham, J.A.
Author_Institution :
Sch. of Electr., Electron. & Comput. Eng., Newcastle Univ., Newcastle upon Tyne, UK
Abstract :
This paper presents an efficient Received Signal Strength RSS-based localization approach utilizing a Tree Search Algorithm (TSA). In comparison to the existing exhaustive search algorithms, e.g. Least Square Estimators (LSE) and Error Controlling localization (Ecolocation), the proposed approach achieves considerable reduction in computational complexity and storage requirements. The effectiveness of the TSA is evaluated through simulation and real experiments. The presented results show that the performance of the new approach closely achieves LSE and performs better than Ecolocation algorithms. Moreover, at a comparable system complexities, TSA outperforms the simplistic Proximity and Centroid localization algorithms.
Keywords :
communication complexity; estimation theory; least squares approximations; signal processing; tree searching; wireless sensor networks; LSE; RSS-based localization approach; TSA; centroid localization algorithms; comparable system complexity; computational complexity; ecolocation algorithms; error controlling localization; exhaustive search algorithms; least square estimators; low complexity least-square estimator; received signal strength; simplistic proximity; storage requirements; tree search algorithm; wireless sensor networks; Biological system modeling; Computational complexity; Computational modeling; Least squares approximation; Maximum likelihood estimation; Wireless sensor networks;
Conference_Titel :
Communications and Information Technology (ICCIT), 2012 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4673-1949-2
DOI :
10.1109/ICCITechnol.2012.6285818