Monitoring Technology

Klonovs, Juris; Haque, Mohammad A.; Krüger, Volker; Nasrollahi, Kamal; Andersen-Ranberg, Karen; Moeslund, Thomas B.; Spaich, Erika G.

doi:10.1007/978-3-319-27024-1_4

Cited by 3 publications

(2 citation statements)

References 202 publications

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“…This network is used in wide range applications such as Industry [2], Agriculture [3], Civil infrastructure [4] and Security [5]. Recent developments in WSN are popularly used for indoor applications [6]. The deployment of sensor nodes in random positions, impact on finding accurate locations of nodes.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Indoor Localization in WSN using PSO tuned EKF

Janapati¹,

Balaswamy²,

Soundararajan³

2017

IJISA

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In Wireless Sensor Networks, nodes are positioned arbitrarily and finding location of nodes is difficult. In this network, the nodes need to know their location is important for indoor applications. In this applications signals are affected by various factors such as noise, multipath, NLOS etc. This impact on inaccurate location information of node, which leads finding path to the destination node is difficult. Cooperative location based routing is alternative solution for finding better path. In this paper a solution is proposed for effective route in indoor application of WSN. The proposed solution uses Particle Swarm Optimization assisted Adaptive Extended Kalman Filter (PSO-AKF) for finding location of nodes. In this mechanism, finding accurate position of node impact on network performance such as minimization of delay, location error and also minimizes complexity.

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Section: Introductionmentioning

confidence: 99%

Enhancement of Indoor Localization in WSN using PSO tuned EKF

Janapati¹,

Balaswamy²,

Soundararajan³

2017

IJISA

View full text Add to dashboard Cite

show abstract

“…The proposed system in this paper extends [9] by addressing the abovementioned shortcomings and thereby allows for automatic and more reliable detection of fatigue time offsets from facial video captured by a simple webcam. To address the shortcomings, we introduce a face quality assessment (FQA) method that prunes the captured video data so that low quality face frames cannot contribute to erroneous results [12,13]. Following [10,14], we track feature points (Fig.…”

Section: Introductionmentioning

confidence: 99%