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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 draining speed.
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Related In: Results  -  Collection


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fig5: Measure of node draining speed.

Mentions: Figure 5 describes the measurement of node draining speed in terms of Joules based on the time taken to place the vehicles in the road network. The beginning event time and ending event time for one cycle were considered between 13:00 and 16:00, 19:00 and 21:00, respectively. In this way the observations were made with an interval of 10-second gap. Compared to the existing adaptive traffic segmentation [7] method, the proposed CVDRN-IN method provides less draining speed. The less draining speed observed in CVDRN-IN is due to the fact that the sensor nodes in the network are fixed and it detects only the movement of vehicles on the road network. Normally, when the sensor nodes are fixed, the draining speed of the node is very low compared to the dynamic sensor nodes of existing methods and improved by 10–30% compared to 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 node draining speed.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Measure of node draining speed.
Mentions: Figure 5 describes the measurement of node draining speed in terms of Joules based on the time taken to place the vehicles in the road network. The beginning event time and ending event time for one cycle were considered between 13:00 and 16:00, 19:00 and 21:00, respectively. In this way the observations were made with an interval of 10-second gap. Compared to the existing adaptive traffic segmentation [7] method, the proposed CVDRN-IN method provides less draining speed. The less draining speed observed in CVDRN-IN is due to the fact that the sensor nodes in the network are fixed and it detects only the movement of vehicles on the road network. Normally, when the sensor nodes are fixed, the draining speed of the node is very low compared to the dynamic sensor nodes of existing methods and improved by 10–30% compared to 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