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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 data aggregation count.
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Related In: Results  -  Collection


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fig6: Measure of data aggregation count.

Mentions: Figure 6 illustrates the data aggregation count offered by CVDRN-IN and comparison analysis is made with the existing adaptive traffic segmentation [7] with the sensing range of 20 m to 120 m observed in the network with the beginning event time as 14:00 and ending event time as 17:00. From the figure it is illustrative that the level of data aggregation count using cluster-based vehicle detection in road traffic network maintains the highest aggregation efficiency. This is because the movement of the vehicle is grouped into separate clusters using intention numeration methods based on node energy, whereas the existing method was based on the packet transmission rate. The aggregation efficiency achieved using CVDRN-IN is 3–5% higher than the existing adaptive traffic segmentation [7].


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

Devasenapathy D, Kannan K - ScientificWorldJournal (2015)

Measure of data aggregation count.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Measure of data aggregation count.
Mentions: Figure 6 illustrates the data aggregation count offered by CVDRN-IN and comparison analysis is made with the existing adaptive traffic segmentation [7] with the sensing range of 20 m to 120 m observed in the network with the beginning event time as 14:00 and ending event time as 17:00. From the figure it is illustrative that the level of data aggregation count using cluster-based vehicle detection in road traffic network maintains the highest aggregation efficiency. This is because the movement of the vehicle is grouped into separate clusters using intention numeration methods based on node energy, whereas the existing method was based on the packet transmission rate. The aggregation efficiency achieved using CVDRN-IN is 3–5% higher than the existing adaptive traffic segmentation [7].

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