Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmiss...Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.展开更多
Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natu...Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.展开更多
基金The authors are grateful to the Raytheon Chair for Systems Engineering for funding.
文摘Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.
文摘Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.
