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An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

Devasenapathy D, Kannan K - ScientificWorldJournal (2015)

Bottom Line: The traffic in the road network is progressively increasing at a greater extent.Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree.The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Technology, Easwari Engineering College, Chennai 600089, India.

ABSTRACT
The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

No MeSH data available.


Related in: MedlinePlus

Measure of traffic delivery ratio.
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Related In: Results  -  Collection


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fig8: Measure of traffic delivery ratio.

Mentions: Figure 8 illustrates the traffic delivery ratio. The sensing range of each sensor varies from 30 to 180 meters. Each simulation time was configured for 50 seconds, with each simulation being carried out 4 times on a random topology to normalize the graph. The traffic delivery ratio offered using CVDRN-IN is higher than that using the existing adaptive traffic segmentation [7] method, though with the increasing range of sensors the delivery ratio is reduced. Comparatively, it is higher using CVDRN-IN than with the existing model. The traffic delivery ratio was higher because the enhanced data aggregation is performed by encrypting the MVs destined to the base station and then by checking the validity of the aggregation results based on the digital signature ensuring higher traffic delivery ratio. The variance achieved using CVDRN-IN is 7–10% higher.


An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

Devasenapathy D, Kannan K - ScientificWorldJournal (2015)

Measure of traffic delivery ratio.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352484&req=5

fig8: Measure of traffic delivery ratio.
Mentions: Figure 8 illustrates the traffic delivery ratio. The sensing range of each sensor varies from 30 to 180 meters. Each simulation time was configured for 50 seconds, with each simulation being carried out 4 times on a random topology to normalize the graph. The traffic delivery ratio offered using CVDRN-IN is higher than that using the existing adaptive traffic segmentation [7] method, though with the increasing range of sensors the delivery ratio is reduced. Comparatively, it is higher using CVDRN-IN than with the existing model. The traffic delivery ratio was higher because the enhanced data aggregation is performed by encrypting the MVs destined to the base station and then by checking the validity of the aggregation results based on the digital signature ensuring higher traffic delivery ratio. The variance achieved using CVDRN-IN is 7–10% higher.

Bottom Line: The traffic in the road network is progressively increasing at a greater extent.Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree.The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Technology, Easwari Engineering College, Chennai 600089, India.

ABSTRACT
The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

No MeSH data available.


Related in: MedlinePlus