High-Precision Localization Algorithms for WSN in Outdoor Environment
BASSAM FAIZ HAMOOD GUMAIDA
This thesis study the main features of RSSI based localization and propose new solutions to enhance the localization accuracy. These new solutions are based on optimization techniques and mobile anchors. Optimization techniques such as Particle Swarm Optimization (PSO), Intelligent Water Drop (IWT), Variable Neighborhood Search (VNS) and their versions have been used for the purpose to solve the low accuracy related with RSSI. In addition, this thesis introduced two models based on mobile anchors, namely GTMA and ELPMA. These two models achieved high localization accuracy; also present a perfect path planning for the mobile anchors. In this thesis, a simulation model is built using Matlab to examine the proposed solutions and to explore their performance ability comparing with other localization algorithms. In particular, the main contributions of this thesis can be briefly summarized as follows:
1) The authors proposed a novel and an efficient algorithm relied on new optimization techniques called Hierarchical Structure Poly-Particle Swarm Optimization (HSPPSO) to promote the localization precision when utilized RSSI measurements. The results evaluation demonstrates that the proposed algorithm provides quite localization accuracy regardless of the error in RSSI measurement. On the one hand, the results proved that the proposed algorithm based on HSPPSO converges and estimates the best position for unlocalized sensor nodes more speedily than algorithm that utilized the basic PSO, contribute to lowering the localization time; thus contributes to prolonging the battery lifetime of the WSN devices.
2) The authors proposed a localization algorithm known as Group of Tri-Mobile Anchors (GTMA). Where GTMA is relied on a group of tri-mobile anchors which transferring in adjustable square path to scan the whole deployment area. The purpose of GTMA is to achieve high localization accuracy and perfect path-planning. The comparison among GTMA and other travelling trajectory models has shown that GTMA algorithm remarkably outperformed them and achieved high localization precision with perfect trajectory planning.
3) The authors proposed an efficient localization algorithm called Efficient Localization Algorithm based Path Planning for Mobile Anchors (ELPMA). ELPMA is relied on a one-mobile anchor travelling in adjustable circular path to scan the whole target area. The main purpose of ELPMA is to decrease the travelling path and achieve rigor localization accuracy. The comparison among several algorithms proved the stability and the robustness of ELPMA against several factors, which comprise communication range, noise, and speed of mobile anchor. The simulation results demonstrate the efficient performance of ELPMA to improve the localization accuracy and achieve a perfect path planning.
4) The authors proposed a novel and high efficiency algorithm, which is relied on optimization approach for promote localization accuracy in an outdoor environment. This optimization approach is non-linear optimization technique and is known as Intelligent Water Drops (IWDs). The evaluation pretend that the proposed algorithm relied on IWD-CO had performed an accurate estimation despite of the error on RSSI, in the same time outperformed other compared algorithms.
5) The authors proposed an efficient algorithm based on highly efficient optimization technique called Hybrid Particle Swarm Optimization with Variable Neighborhood Search (HPSOVNS). The purpose of this algorithm is to promote the localization accuracy and saving operating cost. The evaluation results obviously demonstrated that HPSOVNS provides entirely accurate location estimates regardless of the supposed error in RSSI measurement. On the one hand, considering the localization time, the results show that HPSOVNS has strong ability to converge quickly to the best solution, therefore saves the time needed for localization, and simultaneously contributes to extending the battery lifetime of sensor nodes.
 Gumaida B.F, Luo J. An Efficient Algorithm for Wireless Sensor Network Localization Based on Hierarchical Structure Poly-Particle Swarm Optimization. Wireless Personal Communications, Volume 97, Issue 1, pp 125–151, DOI: https://doi.org/10.1007/s11277-017-4497-4, SCI: 1.2.
 Gumaida B, Liu C, Luo J. GTMA: Localization in Wireless Sensor Network Based a Group of Tri-Mobile Anchors. Journal of Computational and Theoretical Nanoscience, Volume 14, Issue 1, pp. 847-857, DOI：10.1166/jctn.2017.6287, SCI: 1.665.
 Gumaida B F, Luo J. ELPMA: Efficient Localization Algorithm Based Path Planning for Mobile Anchor in Wireless Sensor Network. Wireless Personal Communications, June 2018, Volume 100, Issue 3, pp 721–744, DOI: https://doi.org/10.1007/s11277-018-5343-z, SCI: 1.2.
 Gumaida B F, Luo J. Novel localization algorithm for wireless sensor network based on intelligent water drops. Wireless Networks, 11(2017):1-13, DOI: https://doi.org/10.1007/s11276-017-1578-y, SCI: 1.983.
版权所有©湖南大学2017 湖南大学党委宣传部 地址：湖南省长沙市岳麓区麓山南路麓山门 邮编：410082 Email：firstname.lastname@example.org 域名备案信息：[www.hnu.edu.cn,www.hnu.cn/湘ICP备05000239号] [hnu.cn 湘教QS3-200503-000481 hnu.edu.cn 湘教QS4-201312-010059]