Research on Centroid Localization Algorithm That Uses Modified Weight in WSN

Cheng, Haiqing; Wang, Hua-kai; Wang, Hua

doi:10.1109/ncis.2011.154

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…Reference [18] developed a modified Weighted Centroid location Algorithm (WCA), which can reduce the effects of the density of anchor nodes and uniformity of deployment. This algorithm is based on the CA where the contribution of every anchor nodes is quantified by the RSSI thus makes it one of the WCA benefits.…”

Section: A Range-free Dv-hopmentioning

confidence: 99%

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Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

2020

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Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. Anchor nodes generate the packets of nodes position information to calculate the number of hops between the beacon nodes and the anchor nodes, and these packets are flooded in the respective Wireless Sensor Networks (WSN). The DV-Hop algorithm obtains the distance information from the unknown nodes to the anchor nodes through the network topology calculation rather than the radio wave signals measurement which are used in the range-based algorithm. However, flooding information from all anchors to all beacon nodes will become too expensive for large networks (i.e., cost, energy), even with low anchor fraction. The region of nodes flooding through the network needs to be confined within logical constraints to produce better accuracy to reduce the localization error while reducing the cost. This research proposes and develops an enhanced algorithm that can improve the region area for sensor nodes placement within the specific localization area. This research aims to produce a longer network lifetime by reducing the energy used of the sensor nodes and at the same time minimize the localization error. This research describes the flooding control of the node's placement for the localization of the DV-Hop algorithm. The placement of nodes is limited inside the region of the anchor nodes. The simulation environment setup comes with parameters such as the radius, area, number of nodes, types of nodes, the anchor proportion, and the value of the regions. The result shows an improvement for the localization error, the power consumption, and the accuracy of the nodes positioning.

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Section: A Range-free Dv-hopmentioning

confidence: 99%

“…Reference [36] proposed the Improved Weighted Connectivity (IWC) based algorithm as an effort to improve the fundamental CA as already briefly discussed in [18]. There are two types of the proposed IWC CA, which are the Connectivity-Based Centroid (CBC) and Improved Connectivity-based Centroid (ICB) respectively.…”

Section: A Range-free Dv-hopmentioning

confidence: 99%

Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

2020

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“…The location principle of centroid localization is as follows: the unknown node sends broadcast messages to the network for determining its location information; after anchor nodes received broadcast messages, they sent response of information of its own location to the unknown node; the center of mass in this graphic is regarded as the estimation coordinate of the unknown node [17, 18], which is formed by anchor nodes.…”

Section: Introductionmentioning

confidence: 99%

A Location Estimation Algorithm Based on RSSI Vector Similarity Degree

Shang

Wen

Wang

et al. 2014

International Journal of Distributed Sensor Networks

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We present a detailed study on the RSS-based location techniques in wireless sensor networks (WSN). There are two aspects in this paper. On the one hand, the accurate RSSI received from nodes is the premise of accurate location. Firstly, the distribution trend of RSSI is analyzed in this experiment and determined the loss model of signal propagation by processing experimental data. Secondly, in order to determine the distance between receiving nodes and sending nodes, Gaussian fitting is used to process specific RSSI at different distance. Moreover, the piecewise linear interpolation is introduced to calculate the distance of any RSSI. On the other hand, firstly, the RSSI vector similarity degree (R-VSD) is used to choose anchor nodes. Secondly, we designed a new localization algorithm which is based on the quadrilateral location unit by using more accurate RSSI and range. Particularly, there are two localization mechanisms in our study. In addition, the generalized inverse is introduced to solve the coordinates of nodes. At last, location error of the new algorithm is about 17.6% by simulation experiment.

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“…In [8], Hongyu et al propose an anchor_optimized modified weighted centroid localization algorithm based on RSSI (AMWCL-RSSI). Other schemes have discussed inexact position problems [9], or analyzed the performance of weighted centroid localization algorithm [10], or enhanced its performance for specific scenarios [11].…”

Section: Introductionmentioning

confidence: 99%

Weighted centroid localization for wireless sensor networks

Xie

Wang

2014

2014 IEEE International Conference on Consumer Electronics - China

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Localization is essential in the field of wireless sensor networks (WSNs). Compared to other non-interactive localization algorithms, the weighted centroid localization scheme only uses the information of the received signal strength (RSS), which makes it simple to implement and robust to variation in the propagation environment. In this paper, we propose an improved weighted centroid localization algorithm based on RSSI for an indoor localization. Real experimental results show that IWCL-RSSI is better than WCL (weighted centroid localization) in terms of the localization accuracy.

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Research on Centroid Localization Algorithm That Uses Modified Weight in WSN

Cited by 7 publications

References 11 publications

Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

A Location Estimation Algorithm Based on RSSI Vector Similarity Degree

Weighted centroid localization for wireless sensor networks

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