Mobile element assisted cooperative localization for wireless sensor networks with obstacles

Chen, Hongyang; Shi, Qingjiang; Tan, Rui; Poor, H. Vincent; Sezaki, Kaoru

doi:10.1109/twc.2010.03.090706

Cited by 125 publications

(67 citation statements)

References 26 publications

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“…Literature [2] introduced a coordinate influence coefficient estimates, improving the accuracy of node localization to solve the problem of sparse beacon nodes. Literature [3] from the security considerations nodes in the network, the nodes to achieve safe positioning ideas presented SAPIT algorithm. Literature [4] for sparse node localization inaccurate positioning algorithm proposed superimposed annular region ROCRSSI, the algorithm control signals are issued beacon nodes, node density connectivity and neighbors will not affect its error rate, but the algorithm is more complex.…”

Section: Performance Comparison Of Improved Apit Algorithmmentioning

confidence: 99%

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The Positioning Algorithm Research for Forest Fire Prediction

Liú¹,

Zhang²

2014

IJSH

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Keywords: wireless sensor network, forest fire prediction, positioning algorithm, gravity scanning algorithmForest fire prediction focuses on prediction accuracy and fast prediction, you need to take advantage of wireless sensor positioning algorithm to reduce the positioning error rate. This article will study wireless sensor positioning algorithm by comparison and positioning algorithms to further enhance forest fire prediction the occurrence of positioning accuracy, thus providing a good algorithm to support forest fire forecast.

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Section: Performance Comparison Of Improved Apit Algorithmmentioning

confidence: 99%

“…Positioning accuracy is high. When the beacon nodes is less than six and the count cannot be added since the density of beacon node is not averagely, neighbor nodes will be used for the calculation [2][3]. The location of neighbor will be calculated firstly since neighbor may be another unknown node.…”

mentioning

confidence: 99%

The Positioning Algorithm Research for Forest Fire Prediction

Liú¹,

Zhang²

2014

IJSH

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“…Moreover, we considered the RSS error histograms We can recognize two class of RSS-based localization algorithms: range-based, which use several target-toanchor distance estimations obtained through the RSS measurements, and range-free, which determine the position of the target node without performing distance estimation [5], [23].…”

Section: B Channel Parameters Estimationmentioning

confidence: 99%

Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

Pivato

Palopoli

Petri

2011

IEEE Trans. Instrum. Meas.

172

107

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“…Also, range free localization algorithms are categorized based on the deployment scenarios. The categorization has been divided into four groups: (1) static sensor nodes and static anchor nodes [34,35]; (2) static sensor nodes and mobile anchor nodes [36,37]; (3) mobile sensor nodes and static anchor nodes [38,39]; and (4) mobile sensor nodes and mobile anchor nodes [40,41].…”

Section: Introductionmentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Paul

Sato

2017

JSAN

166

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Abstract:Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today's communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

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Mobile element assisted cooperative localization for wireless sensor networks with obstacles

Cited by 125 publications

References 26 publications

The Positioning Algorithm Research for Forest Fire Prediction

The Positioning Algorithm Research for Forest Fire Prediction

Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

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