Three-Dimensional Localization Algorithm of APIT Based on Fermat-Point Divided for Wireless Sensor Networks

2014 ◽  
Author(s):  
Xiong Xiaoming ◽  
Yan Chuan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Fermat Point

scholarly journals Research on Algorithm of Three-Dimensional Wireless Sensor Networks Node Localization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jiang Minlan ◽  
Luo Jingyuan ◽  
Zou Xiaokang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Minimum Problem ◽  
Localization Algorithm ◽  
Node Localization ◽  
Anchor Nodes

This paper proposes a three-dimensional wireless sensor networks node localization algorithm based on multidimensional scaling anchor nodes, which is used to realize the absolute positioning of unknown nodes by using the distance between the anchor nodes and the nodes. The core of the proposed localization algorithm is a kind of repeated optimization method based on anchor nodes which is derived from STRESS formula. The algorithm employs the Tunneling Method to solve the local minimum problem in repeated optimization, which improves the accuracy of the optimization results. The simulation results validate the effectiveness of the algorithm. Random distribution of three-dimensional wireless sensor network nodes can be accurately positioned. The results satisfy the high precision and stability requirements in three-dimensional space node location.

A Decentralized Positioning Method for Wireless Sensor Networks

2014 ◽  
Vol 668-669 ◽  
pp. 1194-1197 ◽  
Author(s):  
Yan Feng ◽  
Bo Yi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Research Direction ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Position Information ◽  
Initial Location ◽  
3D Positioning ◽  
Positioning Algorithm

The three-dimensional positioning algorithm has become a hot research direction in wireless sensor networks localization algorithms, however the existing 3D positioning algorithms have general shortcomings, such as high complexity, low positioning accuracy, great energy consumption. Aiming at the existing problems of 3D localization algorithm, we propose an decentralized 3D positioning algorithm based on RSSI ranging and free ranging mechanism. The algorithm firstly use measured RSSI to establish beacon node neighborhood. Then the method adopts regional division to obtain initial location information for unknown nodes. Finally, the method use the iterative optimization process to achieve a position information updates. Simulation results demonstrate that proposed algorithm is feasible and has better localization accuracy.

scholarly journals A Survey on DV-Hop localization Techniques in Three- Dimensional Wireless Sensor Networks

2020 ◽  
Vol 16 (10) ◽  
pp. 23
Author(s):  
Fengrong Han ◽  
Izzeldin Ibrahim Mohamed Abdelaziz ◽  
Xinni Liu ◽  
Kamarul Hawari Ghazali ◽  
Hao Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Future Research ◽  
Localization Algorithm ◽  
Monitoring And Control ◽  
Position Information ◽  
Localization Algorithms ◽  
Range Free

Location information is prerequisite for wireless sensor networks (WSNs) monitoring and control applications, since there is no meaning without position information for collected data. Distance vector hop (DV-Hop) localization algorithm as the typical range-free algorithm that has been widely applied in various applications. Nowadays, the research on range-free localization for WSNs is mostly based on two-dimensional (2D) space. Hence, there are few surveys concentrated on range-free localization in three-dimensional (3D) WSNs. This motivated us to present an extensive overview of enhanced DV-Hop localization algorithms in 3D WSNs. This paper focused on critical challenge between 2D and 3D in localization model, representative range-free 3D localization technique surveys. Moreover, a comprehensive taxonomy of most essential enhanced methods applied in 3D DV-Hop is illustrated. A considerable comparison in term of localization error, computational complexity and node type is given. Future research directions dealing with localization under 3D DV-Hop is also discussed.

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