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Automatic Clone Detection in Wireless Sensor Networks using ANN
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Abstract
In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor node. Decreasing the measurement of information transmission in sensor networks becomes an important issue. Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks. Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, ANN algorithm has been implemented to detect the clone nodes. The performance has been compared with the existing work. The comparison has been done with time and efficiency of the existing and the proposed work. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. This algorithm is used to detect the clone nodes easily and it is time effective.
Title: Automatic Clone Detection in Wireless Sensor Networks using ANN
Description:
Abstract
In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces.
According to field tests, this information corresponds to most of the energy use of the sensor node.
Decreasing the measurement of information transmission in sensor networks becomes an important issue.
Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load.
However, the total number of classification of information delivered using pure CS is still enormous.
The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.
Further the energy productivity is a test task for the sensor nodes.
However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology.
It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks.
Here, ANN algorithm has been implemented to detect the clone nodes.
The performance has been compared with the existing work.
The comparison has been done with time and efficiency of the existing and the proposed work.
Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network.
Personal data from the source centre (e.
g.
personality and geographical data) is provided to the observer at the optional witness stage.
The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes.
It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node.
The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink.
This algorithm is used to detect the clone nodes easily and it is time effective.
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