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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 clustering efficiency.
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Related In: Results  -  Collection


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fig4: Measure of clustering efficiency.

Mentions: Figure 4 describes the efficiency of cluster measured based on the number of sensor nodes in the network area. The clustering efficiency (in terms of %) is measured based on the rate at which the sensor nodes are grouped for collecting the information of vehicle movement and obtained through the value of W/L score obtained through the Dodger data set. Based on each ratio of W/L score, the clustering efficiency also gets improved. Compared to the existing adaptive traffic segmentation [7] method, the proposed CVDRN-IN has a higher cluster efficiency. This is because the clustering is performed based on the node drain rate of each of the sensor nodes in the network, whereas, in the existing adaptive traffic segmentation, efficiency was improved in terms of packets being processed. Though the packet processing efficiency was improved, repeated similar packets were processed, resulting in increased computation overhead. The variance in the clustering efficiency is 20–25% higher in the proposed CVDRN-IN than in 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 clustering efficiency.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Measure of clustering efficiency.
Mentions: Figure 4 describes the efficiency of cluster measured based on the number of sensor nodes in the network area. The clustering efficiency (in terms of %) is measured based on the rate at which the sensor nodes are grouped for collecting the information of vehicle movement and obtained through the value of W/L score obtained through the Dodger data set. Based on each ratio of W/L score, the clustering efficiency also gets improved. Compared to the existing adaptive traffic segmentation [7] method, the proposed CVDRN-IN has a higher cluster efficiency. This is because the clustering is performed based on the node drain rate of each of the sensor nodes in the network, whereas, in the existing adaptive traffic segmentation, efficiency was improved in terms of packets being processed. Though the packet processing efficiency was improved, repeated similar packets were processed, resulting in increased computation overhead. The variance in the clustering efficiency is 20–25% higher in the proposed CVDRN-IN than in 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