Sliding Window Algorithm for Mobile Communication Networks, 2017
A MSC provides call setup services in response to a call setup request from a mobile subscriber p... more A MSC provides call setup services in response to a call setup request from a mobile subscriber provided its profile is available in VLR. The mobile subscribers roam randomly among the service areas of GSM network area. One naive policy is to delete immediately the profile of a mobile subscriber from VLR whenever it exits the MSC service area and fetch the same from HLR when it reenters in quick succession of exit and makes the first instance of call setup request. This policy increases the load on the network traffic; delays call setup time, and reduce the throughput of MSC. An alternative policy is deferring deletion of the profile of a mobile subscriber even though it exits the MSC service area expecting its reentry in quick succession. However, the optimal period of deferring deletion or retention shall be determined for which the average call setup time is minimum or conversely the throughput of MSC is maximum. Nuka and Naidu [23] proposed a sliding window algorithm that minimizes the average time for call setup or conversely maximizes throughput of the MSC. However, they have not considered the waiting time of call setup requests in queue at MSC when it is found busy. This chapter presents, a model for realistic measurement of throughput of a MSC considering the waiting time of call setup requests in queue integrating sliding window algorithm with a single server finite queuing model.
In previous two chapters, it is concluded that the consideration of waiting time of call setup re... more In previous two chapters, it is concluded that the consideration of waiting time of call setup request in queue at MSC with a single channel increases the average call setup time or conversely decreases the throughput of MSC. It is deduced that the consideration of waiting time of call setup request in queue at MSC with two channels decreases the average call setup time or conversely increases the throughput of MSC. In this chapter, an aspiration is proposed for determining the optimal number of channels for the average call setup time and percentage of idle time of channels are less than specified service levels.
In this chapter, a simulation model is developed for evaluating the performance of FBSD and SWSSD... more In this chapter, a simulation model is developed for evaluating the performance of FBSD and SWSSD algorithms. Performance metrics, simulation model, input parameters, data assumptions, experimentation, and simulation output analyses are presented in the following sections.
Decision trees have been found to be very effective for classification in the emerging field of d... more Decision trees have been found to be very effective for classification in the emerging field of data mining. This paper proposes a new method: CC-SLIQ (Cascading Clustering and Supervised Learning In Quest) to improve the performance of the SLIQ decision tree algorithm. The drawback of the SLIQ algorithm is that in order to decide which attribute is to be split at each node, a large number of Gini indices have to be computed for all attributes and for each successor pair for all records that have not been classified. SLIQ employs a presorting technique in the tree growth phase that strongly affects its ability to find the best split at a decision tree node. However, the proposed model eliminates the need to sort the data at every node of the decision tree; as an alternative the training data uses a k-means clustering data segmentation only once for every numeric attribute at the beginning of the tree growth phase. The CC-SLIQ algorithm inexpensively evaluates split points that are t...
In this chapter, a model is proposed for determining an optimal sliding window size minimizing th... more In this chapter, a model is proposed for determining an optimal sliding window size minimizing the average call setup time at an MSC. It is presumed that the objective function behavior is unimodal. However, it cannot be solved either by an analytical method or by a numerical method. Hence, it is solved through simulation. The analysis of simulation output proved that the presumption is valid and the optimal sliding window size is ten for the given input parameters and data assumptions. However, the use of multi-level indexed file structure for VLR represented by B-tree of an appropriate order that does not exceed three levels resulted in lower average call setup time/higher throughput than that of simple indexed file structure. Hence, the model proposed in this chapter is significant for maximizing the throughput of GSM network.
Sliding Window Algorithm for Mobile Communication Networks, 2017
Mobile subscribers move randomly in the area of a GSM network. The location identity of roaming m... more Mobile subscribers move randomly in the area of a GSM network. The location identity of roaming mobile subscribers is required to offer essential services to the subscriber call setup requests. In a network, subscriber information is maintained by databases, referred to as home location register (HLR) and visitor location register (VLR). The HLR is a centralized database which is located at Gateway Mobile Switching Center (GMSC) to maintain and keep switching profiles of all mobile subscribers and also to their current location data. VLR is distributed database in MSC to keep switching replications of subscriber profiles that are currently in its jurisdiction.
Journal of Computer Networks and Communications, 2016
The sliding window algorithm proposed for determining an optimal sliding window does not consider... more The sliding window algorithm proposed for determining an optimal sliding window does not consider the waiting times of call setup requests of a mobile station in queue at a Mobile Switching Centre (MSC) in the Global System for Mobile (GSM) Communication Network. This study proposes a model integrating the sliding window algorithm with a single server finite queuing model, referred to as integrated model for measurement of realistic throughput of a MSC considering the waiting times of call setup requests. It assumes that a MSC can process one call setup request at a time. It is useful in determining an optimal sliding window size that maximizes the realistic throughput of a MSC. Though the model assumes that a MSC can process one call setup request at a time, its scope can be extended for measuring the realistic throughput of a MSC that can process multiple call setup requests at a time.
Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically an... more Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex. Approaches such as the back propagation model of neural network, linear regression, support vector machine, Bayesian networks, and fuzzy logic can be applied, but their rate of prediction is very low, which leads to unpredictable results. The present study focuses on the investigation of the application of decision trees, which is a data mining technique in the prediction of precipitation. This paper aims at improving the prediction of precipitation compared to Supervised Learning in Quest (SLIQ) decision trees, especially when datasets are large. Because SLIQ decision trees take more computational steps to find split points, they consume more time and thus cannot be applied to huge datasets. An elegant decision tree using entropy as an attribute selection measure is adopted in this study, which increases the accuracy rate and decreases the computation time. This approach provides an average accuracy of 76.12% with a reduction of 63% in computational time over SLIQ decision trees.
International Journal of Hydrology Science and Technology, 2014
The satellite imagery-based hydro image processing (SIHIP) model elucidates a new precipitation n... more The satellite imagery-based hydro image processing (SIHIP) model elucidates a new precipitation nowcasting methodology by relating humidity and intensity of satellite infrared image. With this relation, the relative humidity can be quickly and reliably estimated in near real-time to eliminate the need for site-specific radiosonde. The algorithm uses clustering technique to separate the cloud texture from the ground surface textures and Haar wavelet to obtain its mean wavelength. This wavelength is used to obtain a relation between a real time entity and the observed entity, which affects the precipitation. SIHIP focuses on convective and precipitating clouds over the Indian subcontinent region for the periods June–September 2012 and 2013. It has undergone a rigorous test for validation on real time data from NASA Global Precipitation Measurement mission. The results of the SIHIP model reflect a significant improvement over existing global satellite precipitation nowcasting algorithms with a success rate of 95.14%.
2013 8th EUROSIM Congress on Modelling and Simulation, 2013
Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically an... more Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex. Approaches such as the back propagation, linear regression, support vector machine, Bayesian networks, and fuzzy logic can be applied, but their rate of prediction is very low, which leads to unpredictable results. This paper aims at improving the prediction of precipitation compared to Supervised Learning in Quest (SLIQ) decision trees, especially when datasets are large. Because SLIQ decision trees take more computational steps to find split points, they consume more time and thus cannot be applied to huge datasets. An elegant decision tree using gain ratio as an attribute selection measure is adopted, which increases the accuracy rate and decreases the computation time. This approach provides an average accuracy of 76.93% with a reduction of 63% in computational time over SLIQ decision trees.
Water is one of the most important of nature's gifts to the living creatures on Earth. Rainfa... more Water is one of the most important of nature's gifts to the living creatures on Earth. Rainfall is one form of precipitation, and it primarily depends on humidity, temperature, pressure, wind speed, dew point, and so on. The present research is focused on using the gini index as an attribute selection measure in an elegant decision tree to predict precipitation for voluminous datasets. This study aims at improving the prediction of precipitation over the supervised learning in a Quest decision tree, especially when the datasets are large. A decision tree using the gini index increases the accuracy rate while decreasing computational time by reducing the computation of total split points. This approach provides an average accuracy of 72.98% with a reduction of 63% in computational time over a SLIQ decision tree.
Sliding Window Algorithm for Mobile Communication Networks, 2017
A MSC provides call setup services in response to a call setup request from a mobile subscriber p... more A MSC provides call setup services in response to a call setup request from a mobile subscriber provided its profile is available in VLR. The mobile subscribers roam randomly among the service areas of GSM network area. One naive policy is to delete immediately the profile of a mobile subscriber from VLR whenever it exits the MSC service area and fetch the same from HLR when it reenters in quick succession of exit and makes the first instance of call setup request. This policy increases the load on the network traffic; delays call setup time, and reduce the throughput of MSC. An alternative policy is deferring deletion of the profile of a mobile subscriber even though it exits the MSC service area expecting its reentry in quick succession. However, the optimal period of deferring deletion or retention shall be determined for which the average call setup time is minimum or conversely the throughput of MSC is maximum. Nuka and Naidu [23] proposed a sliding window algorithm that minimizes the average time for call setup or conversely maximizes throughput of the MSC. However, they have not considered the waiting time of call setup requests in queue at MSC when it is found busy. This chapter presents, a model for realistic measurement of throughput of a MSC considering the waiting time of call setup requests in queue integrating sliding window algorithm with a single server finite queuing model.
In previous two chapters, it is concluded that the consideration of waiting time of call setup re... more In previous two chapters, it is concluded that the consideration of waiting time of call setup request in queue at MSC with a single channel increases the average call setup time or conversely decreases the throughput of MSC. It is deduced that the consideration of waiting time of call setup request in queue at MSC with two channels decreases the average call setup time or conversely increases the throughput of MSC. In this chapter, an aspiration is proposed for determining the optimal number of channels for the average call setup time and percentage of idle time of channels are less than specified service levels.
In this chapter, a simulation model is developed for evaluating the performance of FBSD and SWSSD... more In this chapter, a simulation model is developed for evaluating the performance of FBSD and SWSSD algorithms. Performance metrics, simulation model, input parameters, data assumptions, experimentation, and simulation output analyses are presented in the following sections.
Decision trees have been found to be very effective for classification in the emerging field of d... more Decision trees have been found to be very effective for classification in the emerging field of data mining. This paper proposes a new method: CC-SLIQ (Cascading Clustering and Supervised Learning In Quest) to improve the performance of the SLIQ decision tree algorithm. The drawback of the SLIQ algorithm is that in order to decide which attribute is to be split at each node, a large number of Gini indices have to be computed for all attributes and for each successor pair for all records that have not been classified. SLIQ employs a presorting technique in the tree growth phase that strongly affects its ability to find the best split at a decision tree node. However, the proposed model eliminates the need to sort the data at every node of the decision tree; as an alternative the training data uses a k-means clustering data segmentation only once for every numeric attribute at the beginning of the tree growth phase. The CC-SLIQ algorithm inexpensively evaluates split points that are t...
In this chapter, a model is proposed for determining an optimal sliding window size minimizing th... more In this chapter, a model is proposed for determining an optimal sliding window size minimizing the average call setup time at an MSC. It is presumed that the objective function behavior is unimodal. However, it cannot be solved either by an analytical method or by a numerical method. Hence, it is solved through simulation. The analysis of simulation output proved that the presumption is valid and the optimal sliding window size is ten for the given input parameters and data assumptions. However, the use of multi-level indexed file structure for VLR represented by B-tree of an appropriate order that does not exceed three levels resulted in lower average call setup time/higher throughput than that of simple indexed file structure. Hence, the model proposed in this chapter is significant for maximizing the throughput of GSM network.
Sliding Window Algorithm for Mobile Communication Networks, 2017
Mobile subscribers move randomly in the area of a GSM network. The location identity of roaming m... more Mobile subscribers move randomly in the area of a GSM network. The location identity of roaming mobile subscribers is required to offer essential services to the subscriber call setup requests. In a network, subscriber information is maintained by databases, referred to as home location register (HLR) and visitor location register (VLR). The HLR is a centralized database which is located at Gateway Mobile Switching Center (GMSC) to maintain and keep switching profiles of all mobile subscribers and also to their current location data. VLR is distributed database in MSC to keep switching replications of subscriber profiles that are currently in its jurisdiction.
Journal of Computer Networks and Communications, 2016
The sliding window algorithm proposed for determining an optimal sliding window does not consider... more The sliding window algorithm proposed for determining an optimal sliding window does not consider the waiting times of call setup requests of a mobile station in queue at a Mobile Switching Centre (MSC) in the Global System for Mobile (GSM) Communication Network. This study proposes a model integrating the sliding window algorithm with a single server finite queuing model, referred to as integrated model for measurement of realistic throughput of a MSC considering the waiting times of call setup requests. It assumes that a MSC can process one call setup request at a time. It is useful in determining an optimal sliding window size that maximizes the realistic throughput of a MSC. Though the model assumes that a MSC can process one call setup request at a time, its scope can be extended for measuring the realistic throughput of a MSC that can process multiple call setup requests at a time.
Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically an... more Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex. Approaches such as the back propagation model of neural network, linear regression, support vector machine, Bayesian networks, and fuzzy logic can be applied, but their rate of prediction is very low, which leads to unpredictable results. The present study focuses on the investigation of the application of decision trees, which is a data mining technique in the prediction of precipitation. This paper aims at improving the prediction of precipitation compared to Supervised Learning in Quest (SLIQ) decision trees, especially when datasets are large. Because SLIQ decision trees take more computational steps to find split points, they consume more time and thus cannot be applied to huge datasets. An elegant decision tree using entropy as an attribute selection measure is adopted in this study, which increases the accuracy rate and decreases the computation time. This approach provides an average accuracy of 76.12% with a reduction of 63% in computational time over SLIQ decision trees.
International Journal of Hydrology Science and Technology, 2014
The satellite imagery-based hydro image processing (SIHIP) model elucidates a new precipitation n... more The satellite imagery-based hydro image processing (SIHIP) model elucidates a new precipitation nowcasting methodology by relating humidity and intensity of satellite infrared image. With this relation, the relative humidity can be quickly and reliably estimated in near real-time to eliminate the need for site-specific radiosonde. The algorithm uses clustering technique to separate the cloud texture from the ground surface textures and Haar wavelet to obtain its mean wavelength. This wavelength is used to obtain a relation between a real time entity and the observed entity, which affects the precipitation. SIHIP focuses on convective and precipitating clouds over the Indian subcontinent region for the periods June–September 2012 and 2013. It has undergone a rigorous test for validation on real time data from NASA Global Precipitation Measurement mission. The results of the SIHIP model reflect a significant improvement over existing global satellite precipitation nowcasting algorithms with a success rate of 95.14%.
2013 8th EUROSIM Congress on Modelling and Simulation, 2013
Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically an... more Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex. Approaches such as the back propagation, linear regression, support vector machine, Bayesian networks, and fuzzy logic can be applied, but their rate of prediction is very low, which leads to unpredictable results. This paper aims at improving the prediction of precipitation compared to Supervised Learning in Quest (SLIQ) decision trees, especially when datasets are large. Because SLIQ decision trees take more computational steps to find split points, they consume more time and thus cannot be applied to huge datasets. An elegant decision tree using gain ratio as an attribute selection measure is adopted, which increases the accuracy rate and decreases the computation time. This approach provides an average accuracy of 76.93% with a reduction of 63% in computational time over SLIQ decision trees.
Water is one of the most important of nature's gifts to the living creatures on Earth. Rainfa... more Water is one of the most important of nature's gifts to the living creatures on Earth. Rainfall is one form of precipitation, and it primarily depends on humidity, temperature, pressure, wind speed, dew point, and so on. The present research is focused on using the gini index as an attribute selection measure in an elegant decision tree to predict precipitation for voluminous datasets. This study aims at improving the prediction of precipitation over the supervised learning in a Quest decision tree, especially when the datasets are large. A decision tree using the gini index increases the accuracy rate while decreasing computational time by reducing the computation of total split points. This approach provides an average accuracy of 72.98% with a reduction of 63% in computational time over a SLIQ decision tree.
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