Localization of WSN node based on Time of Arrival using Ultra wide band spectrum

Meghani, Sunil Kumar; Asif, Muhammad; Amir, Samreen

doi:10.1109/wamicon.2012.6208430

Cited by 11 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time Of Arrival (TOA) [21] and Angle Of Arrival (AOA) [15] are examples of anchor based algorithms using rangebased techniques. Range-based techniques rely on the absolute distance to compute the location of a node.…”

Section: Related Workmentioning

confidence: 99%

Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

Zodi

Hancke

Bagula

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

View full text Add to dashboard Cite

The drop in cost and reduction in size of sensor nodes has eased the development of wireless sensor networks (WSNs) applications. However, the noise and disturbing nature of most sensing environments require accurate algorithms that can overcome these difficulties. Nodes' localization is one of the basic activity a WSN can perform to make other network's functionalities, such as routing easy to tackle. Nowadays there exists many localization methods, however many pose computational and/or accuracy issues. Centroid is a localization algorithm by which an unknown node's coordinates are estimated as the centroid of anchors' coordinates. Its implementation is simple but it has a high error rate. In this paper, two methods are proposed to enhance the centroid localization algorithm. The first, Linear Weighting Centroid (LWC) uses the distance between the anchor and the unknown nodes to linearly weight each anchor's coordinates. The second, the Neighbor Weighting Centroid (NWC) uses the number of intersect nodes between an unknown node and its neighbor anchors to estimate the degree of proximity of the anchor nodes. Both methods assign larger weights to closer anchors and lesser weights to remote anchors to improve centroid accuracy while keeping computation almost at the same level. Simulation is used to study the performance of both mechanisms. The results show that for a large WSN, both methods localize unknown nodes with better position accuracy than centroid, with LWC performing better than NWC.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

Zodi

Hancke

Bagula

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

View full text Add to dashboard Cite

show abstract

“…The node is localized by using the SVD trilateration method. The method use equations (7)(8)(9)(10)(11)(12)(13)(14) and estimated ranges (distances) from unknown node and anchor nodes. The distances are estimated by using the above methodology (based on RTT To A).…”

Section: Simulation Analysismentioning

confidence: 99%

“…As there are certain applications demand that put many constraints in WSN such as node (that comprise sensor, digital circuitry, RF circuitry and battery module) size, power consumption, location in formation (the node), network designing (topology, protocols etc), communication (digital & RF) and QoS. There has been lot of research and development to tackle and address these issues [6], [7], [8], [9]. As with localization, the node has to be localize within the network from where the sensor data obtained as it is not viable to have the data without location stamps in many applications [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Localization of WSN node based on RTT ToA using ultra wide band & 802.15.4a channel

Meghani

Asif

2014

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control

Self Cite

View full text Add to dashboard Cite

Ahstract-A modified round trip time (RTT) time of arrival (ToA ) technique is present to measure the distance between two nodes in wireless sensor network. The ultra wide band spectrum and Gaussian monopulse shaping is used on MATLAB platform for simulations. The nodes are simulated under following envi ronments of 802.1S.4a channel model for distances measurement: residential, office, industrial, outdoor and open outdoor. The measured distances are analyzed and compared with set distances using calculated absolute error versus set distance graphs for different UWB frequencies, 802.1S.4a channels and sampling fre quencies. Finally, trilateration using singular value decomposition (SVD) method calculations are done for localization by using the measured distances. The position errors with respect to set positions are calculated for different trilateration layouts.

show abstract

“…Examples on range-based position algorithms are time of arrival (TOA) [10] and angle of arrival (AOA) [11]. ii.…”

mentioning

confidence: 99%

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Sulaiman,

Natsheh,

Sharaf

et al. 2023

IJIE

View full text Add to dashboard Cite

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies.

show abstract

Localization of WSN node based on Time of Arrival using Ultra wide band spectrum

Cited by 11 publications

References 12 publications

Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

Localization of WSN node based on RTT ToA using ultra wide band & 802.15.4a channel

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Contact Info

Product

Resources

About

Localization of WSN node based on Time of Arrival using Ultra wide band spectrum

Cited by 11 publications

References 12 publications

Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

Localization of WSN node based on RTT ToA using ultra wide band &amp; 802.15.4a channel

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Contact Info

Product

Resources

About

Localization of WSN node based on RTT ToA using ultra wide band & 802.15.4a channel