A Sensor Node Localization Algorithm Based on Fuzzy RSSI Distance

2014 ◽  
Vol 543-547 ◽  
pp. 989-992
Author(s):  
Xiao Qin Li ◽  
Guang Rong Chen
Keyword(s):  
Sensor Node ◽  
Large Deviation ◽  
Sensor Nodes ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Traditional Algorithm ◽  
Intensity Changes ◽  
Better Than

The node self-localization is the basis of target localization for wireless sensor network (WSN), the WSN nodes localization algorithms have two types based on distance and non distance. The node localization based on RSSI is simple and widely used in application. According to the traditional WSN nodes localization algorithm, the RSSI signal intensity changes greatly and with nonlinearity. And it is converted into distance feature with a large deviation, which leads to inaccurate positioning and localization. In order to solve this problem, a sensor node localization algorithm is proposed based on fuzzy RSSI distance. The nodes information is collected based on RSSI ranging method. And the location information is processed with fuzzy operation. The disturbance from the environmental factors for the positioning is solved. The accuracy of the node localization is improved. Simulation result shows that this algorithm can locate the sensor nodes accurately. The localization accuracy is high, and the performance of nodes localization is better than the traditional algorithm. It has good application value in the WSN nodes distribution and localization design.

scholarly journals Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

2021 ◽  
Author(s):  
Lismer Andres Caceres Najarro ◽  
Iickho Song ◽  
Kiseon Kim
Keyword(s):  
Sensor Node ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Healthcare Industry ◽  
Target Node ◽  
Node Localization ◽  
Localization Accuracy ◽  
Fundamental Limitations ◽  
Points Of View

<p> </p><p>With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks</p> <p>(WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.</p> <p><br></p>

scholarly journals Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 343 ◽  
Author(s):  
Dezhi Han ◽  
Yunping Yu ◽  
Kuan-Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Random Mutation ◽  
Localization Accuracy ◽  
De Algorithm ◽  
Distance Vector

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

scholarly journals Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

2021 ◽  
Author(s):  
Soumya J. Bhat ◽  
K. V. Santhosh
Keyword(s):  
Least Squares Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Smart Systems ◽  
Reference Node ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Node Ratio

AbstractInternet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

scholarly journals An Enhanced Hybrid 3D Localization Algorithm Based on APIT and DV-Hop

2017 ◽  
Vol 13 (09) ◽  
pp. 69 ◽  
Author(s):  
Lianjun Yi ◽  
Miaochao Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Hybrid Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
3D Localization ◽  
Localization Algorithms ◽  
Wide Range

<p>Wireless sensor networks (WSN), as a new method of information collection and processing, has a wide range of applications. Since the acquired data must be bound with the location information of sensor nodes, the sensor localization is one of the supporting technologies of wireless sensor networks. However, the common localization algorithms, such as APIT algorithm and DV-Hop algorithm, have the following problems: 1) the localization accuracy of beacon nodes is not high; 2) low coverage rate in sparse environment. In this paper, an enhanced hybrid 3D localization algorithm is designed with combining the advantages of APIT algorithm and DV-Hop algorithm. The proposed hybrid algorithm can improve the localization accuracy of the beacon nodes in dense environments by reducing the triangles in the triangle interior point test (PIT) and selecting good triangles. In addition, the algorithm can combine the advantages of APIT algorithm and DV-Hop algorithm localization algorithm to calculate the unknown node coordinates, and also improve the location coverage of the beacon nodes in sparse environment. Simulation results show that the proposed hybrid algorithm can effectively improve the localization accuracy of beacon nodes in the dense environment and the location coverage of beacon nodes in sparse environment.</p>

Improved DV-Hop Algorithm for Enhancing Localization Accuracy in WSN

2014 ◽  
Vol 543-547 ◽  
pp. 3256-3259 ◽  
Author(s):  
Da Peng Man ◽  
Guo Dong Qin ◽  
Wu Yang ◽  
Wei Wang ◽  
Shi Chang Xuan
Keyword(s):  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Global Average ◽  
Key Technologies ◽  
Simulation Results ◽  
Range Free

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

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 Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Huthaifa M. Kanoosh ◽  
Essam Halim Houssein ◽  
Mazen M. Selim
Keyword(s):  
Computing Time ◽  
Wireless Sensor ◽  
Grey Wolf Optimizer ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Salp Swarm Algorithm ◽  
Localization Scheme ◽  
Swarm Algorithm ◽  
Better Than

Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.

A Mobile Node Localization Algorithm Based on the Angle Self-Adjustment Model for Wireless Sensor Networks

2018 ◽  
Vol 33 (02) ◽  
pp. 1958004 ◽  
Author(s):  
Kangshun Li ◽  
Hui Wang ◽  
Fei Tang ◽  
Wei Li ◽  
Yunru Lu
Keyword(s):  
Prior Information ◽  
Gaussian Elimination ◽  
Mobile Node ◽  
Selection Mechanism ◽  
Localization Algorithm ◽  
Accuracy Control ◽  
Node Localization ◽  
Localization Accuracy ◽  
Moving Direction ◽  
Better Than

The goal of this study is to improve the accuracy of mobile node localization and to avoid the influence of moving direction-offsets introduced by the positioning system’s accuracy control. The proposed localization algorithm, which is based on an overlap self-adjustment method, and an anchor node selection mechanism which uses the Gaussian elimination method, is based on the error probability. The proposed algorithm has the advantages of requiring little prior information, and it reduces the power consumption. The simulation results show that the proposed algorithm is better than the self-adjustment localization (SAL) algorithm in terms of its localization accuracy.

A Research on Circular Localization Algorithm of Wireless Sensor Network

2011 ◽  
Vol 58-60 ◽  
pp. 1657-1663
Author(s):  
Xin Jiang Xia ◽  
Gang Hu ◽  
Qin Wei Wei
Keyword(s):  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Wireless Sensor Node ◽  
Node Localization ◽  
Minimum Area ◽  
Anchor Node ◽  
Localization Algorithms ◽  
Low Energy Consumption ◽  
Improved Algorithm

This paper based on several common wireless sensor node localization algorithms. According to the concentric localization algorithm principle, we proposed an annular localization algorithm and its improved algorithm .The algorithm uses the anchor node to do node ring through certain rules, narrows unknown nodes estimate area continually, and until finally gets the minimum area contains unknown nodes. Then taking the minimum area centroid position as unknown node’s estimate coordinates. Through the simulation of concentric localization algorithm and its improved algorithm, circular localization algorithm and its improved algorithm, can conclude that: When the proportion of anchor node increases from 5% to 10%, the positioning accuracy is obviously improved in the situation of low energy consumption.

scholarly journals Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

2021 ◽  
Author(s):  
Lismer Andres Caceres Najarro ◽  
Iickho Song ◽  
Kiseon Kim
Keyword(s):  
Sensor Node ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Healthcare Industry ◽  
Target Node ◽  
Node Localization ◽  
Localization Accuracy ◽  
Fundamental Limitations ◽  
Points Of View

<p> </p><p>With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks</p> <p>(WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.</p> <p><br></p>

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