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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 node energy consumption.
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Related In: Results  -  Collection


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fig3: Measure of node energy consumption.

Mentions: The node energy consumption is measured based on the number of sensor nodes or the number of cars measured in the previous five minutes using Dodgers loop sensor data in the network area. The value of the proposed CVDRN-IN is compared with the existing adaptive traffic segmentation [7] method. Figure 3 describes the process of consumption of energy required to process the specified sensor nodes in terms of Joules in the road network with the number of sensor nodes in the range of 50 to 300 with the beginning event time as 14:00 and ending event time as 19:00. Compared to the existing adaptive traffic segmentation [7] method, the CVDRN-IN consumes less energy even when more numbers of sensor nodes are present. This is because the proposed CVDRN-IN method clusters the sensor nodes based on the movement of vehicles. Since adaptive node energy clustering is performed, energy consumption for movement of vehicles is noted appreciably whereas in the existing method the energy conservation mechanism was provided but at the cost of increased routing protocol. The improvement of energy consumption is significantly improved by 3–8% compared to the existing adaptive traffic segmentation [7] method.


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

Devasenapathy D, Kannan K - ScientificWorldJournal (2015)

Measure of node energy consumption.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Measure of node energy consumption.
Mentions: The node energy consumption is measured based on the number of sensor nodes or the number of cars measured in the previous five minutes using Dodgers loop sensor data in the network area. The value of the proposed CVDRN-IN is compared with the existing adaptive traffic segmentation [7] method. Figure 3 describes the process of consumption of energy required to process the specified sensor nodes in terms of Joules in the road network with the number of sensor nodes in the range of 50 to 300 with the beginning event time as 14:00 and ending event time as 19:00. Compared to the existing adaptive traffic segmentation [7] method, the CVDRN-IN consumes less energy even when more numbers of sensor nodes are present. This is because the proposed CVDRN-IN method clusters the sensor nodes based on the movement of vehicles. Since adaptive node energy clustering is performed, energy consumption for movement of vehicles is noted appreciably whereas in the existing method the energy conservation mechanism was provided but at the cost of increased routing protocol. The improvement of energy consumption is significantly improved by 3–8% compared to the existing adaptive traffic segmentation [7] method.

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