Research on RSSI and Triangles Deformation Based APIT Localization Algorithm for Wireless Sensor Networks

2014 ◽  
Vol 998-999 ◽  
pp. 1305-1310
Author(s):  
Fei Liu ◽  
Guang Zeng Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Sparse Network ◽  
Unknown Node

The localization accuracy of traditional APIT localization algorithm for wireless sensor network depends on the Approximate Perfect Point-In-Triangulation Test (APIT), and the localization error can be promoted in sparse network. We design one improved localization algorithm (RTD-APIT) based on APIT by using the RSSI and the triangles deformation. RTD-APIT uses the RSSI to improve the APIT for achieving the preliminary location of unknown node, and expand or deform the triangles for solving the Point-In-Triangulation (PIT) problem well and enhancing the localization. Simulation shows RTD-APIT can reduce the localization error effectively, and it also promote the localization coverage.

Sequence Localization Algorithm Based on 3D Voronoi Diagram in Wireless Sensor Network

2014 ◽  
Vol 644-650 ◽  
pp. 4422-4426 ◽  
Author(s):  
Xi Yang ◽  
Jun Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Voronoi Diagram ◽  
Wireless Sensor ◽  
Weighted Estimate ◽  
Localization Algorithm ◽  
Simulation Experiments ◽  
Localization Accuracy ◽  
3D Space ◽  
Unknown Node

For nodes’ self-localization in wireless sensor networks (WSN), a new localization algorithm called Sequence Localization algorithm based on 3D Voronoi diagram (SL3V) is proposed, which uses 3D Voronoi diagram to divide the localization space.It uses the polyhedron vertices as the virtual beacon nodes and constructs the rank sequence table of virtual beacon nodes. Then it computes Kendall coefficients of the ranks in the optimal rank sequence table and that of the unknown node. Finally, it realizes the weighted estimate of the unknown node by normalization processing Kendall coefficients. Simulation experiments prove that itcan obviously improve the localization accuracy compared with the traditional 2D sequence-based localization and can satisfy the need of localization for 3D space.

A Collaborative Multilateral Localization Algorithm for Wireless Sensor Networks

2013 ◽  
Vol 303-306 ◽  
pp. 201-205
Author(s):  
Shao Ping Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Unknown Node

Localization technology is one of the key supporting technologies in wireless sensor networks. In this paper, a collaborative multilateral localization algorithm is proposed to localization issues for wireless sensor networks. The algorithm applies anchor nodes within two hops to localize unknown nodes, and uses Nelder-Mead simplex optimization method to compute coordinates of the unknown nodes. If an unknown node can not be localized through two-hop anchor nodes, it is localized by anchor nodes and localized nodes within two hops through auxiliary iterative localization method. Simulation results show that the localization accuracy of this algorithm is very good, even in larger range errors.

scholarly journals Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4152
Author(s):  
Sana Messous ◽  
Hend Liouane ◽  
Omar Cheikhrouhou ◽  
Habib Hamam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Position Estimation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Recursive Computation

As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.

scholarly journals Localization of Large-Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Huanqing Cui ◽  
Yongquan Liang ◽  
Chuanai Zhou ◽  
Ning Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Large Scale ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Unknown Node

Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

Research on Particle Swarm Optimization-Based Node Positioning Algorithm for Wireless Sensor Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1415-1419 ◽  
Author(s):  
Jie He ◽  
La Yuan Li
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Swarm Optimization

In many instances, as special applications of wireless sensor networks, wireless sensor networks need to know the location of nodes. A wireless sensor network localization algorithm based on Particle Swarm Optimization is proposed in this thesis to solve the problem of inaccurate positioning and large energy consumption for wireless sensor network node positioning. The algorithm combines the particle swarm optimization algorithm (PSO) and node localization algorithm to improve the positioning accuracy.

scholarly journals Simulation of Wireless Sensor Networks Based on Directed Diffusion Algorithm

2017 ◽  
Vol 13 (10) ◽  
pp. 73 ◽  
Author(s):  
Yu Tong
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Joint Analysis ◽  
Communication Model ◽  
Localization Algorithm ◽  
Directed Diffusion ◽  
Delay Performance

<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">In order to reduce the energy consumption and data congestion caused by flooding at the initial stage of the network, the cluster head and the boundary node are used as the key nodes to reduce the data flow in the network, so that the network has better delay performance. Various clustering algorithms in wireless sensor networks and the improved method of clustering protocols are introduced. On the basis of directed diffusion algorithm, a directed diffusion protocol based on CONID clustering is studied. Through the OMNeT++ basic platform, the wireless sensor network simulation platform is built on the basis of the MiXiM module. The platform uses a hierarchical structure, and its network layer implements a directed diffusion protocol based on CONID clustering. The application layer implements the two step WLS localization algorithm. The MAC layer uses the FIexiTP protocol. The physical layer uses the simplest flat path fading model. Because the sensor has both perception and data transmission characteristics, the dual channel communication model is adopted. The platform is suitable for wireless sensor network detection, localization and tracking algorithm simulation. Through the hierarchical function of the platform, it can analyze the practicability and effectiveness of the algorithm in more detail, and has certain value. It can also be used in the simulation of cross layer protocols, and can combine several parameters to perform joint analysis of system performance. Simulation results show that the CONID based directional diffusion protocol has better delay performance than the conventional directed diffusion protocol. Finally, the performance of the two step WLS location protocol is analyzed from several perspectives, which provides an example for the platform.</span></span></p>

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

scholarly journals Localization Algorithm Based on Iterative Centroid Estimation for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Jiang ◽  
Xin Wang ◽  
Li Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Anchor Node ◽  
Centroid Estimation ◽  
Anchor Nodes ◽  
Localization Precision ◽  
Unknown Node ◽  
Received Signal Strength Indication

According to the application of range-free localization technology for wireless sensor networks (WSNs), an improved localization algorithm based on iterative centroid estimation is proposed in this paper. With this methodology, the centroid coordinate of the space enclosed by connected anchor nodes and the received signal strength indication (RSSI) between the unknown node and the centroid are calculated. Then, the centroid is used as a virtual anchor node. It is proven that there is at least one connected anchor node whose distance from the unknown node must be farther than the virtual anchor node. Hence, in order to reduce the space enclosed by connected anchor nodes and improve the location precision, the anchor node with the weakest RSSI is replaced by this virtual anchor node. By applying this procedure repeatedly, the localization algorithm can achieve a good accuracy. Observing from the simulation results, the proposed algorithm has strong robustness and can achieve an ideal performance of localization precision and coverage.

scholarly journals Time Synchronization and Communication Program of Wireless Sensor Network for Online Monitoring of Electrical Equipment

2017 ◽  
Vol 13 (07) ◽  
pp. 36
Author(s):  
Yuxia Shen
Keyword(s):  
Operating System ◽  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Online Monitoring ◽  
Time Synchronization ◽  
Electrical Equipment ◽  
Wireless Sensor ◽  
Zigbee Protocol

<p><span style="font-size: medium;"><span style="font-family: 宋体;">In wireless sensor networks, for improving the time synchronization perfromance of online monitoring and application of ZigBee protocol, a scheme is designed. For this objective, first of all, the ZigBee protocol specification is summarized, a profound analysis of the hardware abstraction architecture of TinyOS operating system is made; the advantages of the ZigBee protocol compared with the traditional radio technology are comparatively analyzed. At the same time, the node design block diagram based on CC2430 and related development system is provided. In the TinyOS2.x operating system, we analyze CC2430 application program abstract architecture, and on this basis, give the realization process of program design. The research results showed that we achieve an on-line monitoring system based on ZigBee protocol, which has realistic significance of applying ZigBee protocol in wireless sensor network of electrical equipment online monitoring. Based on the above research, it is concluded that the online monitoring system can collect the temperature parameters of the monitored object in real time that it can be widely applied in wireless sensor networks.</span></span></p>

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