Aya Ayadi - Academia.edu (original) (raw)

Papers by Aya Ayadi

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017

Pipelines constitute one of the most important ways to transport large amounts of water through l... more Pipelines constitute one of the most important ways to transport large amounts of water through long distance. However, existing damage detection methods do not well in monitoring pipelines due to the harsh environmental condition. for this reason, current systems need to be more automated, efficient and accurate for continuous supervising about damage. For this purpose, Wireless Sensors Networks (WSN) have been brought into the research community in the context of monitoring water pipeline. In this paper, we have discussed the task amounts to provide low-cost and real-time damage detection technique. Our proposed technique is based on Fisher discriminant analysis (FDA) coupled with Support Vector Machine (SVM). We aim to recognize data as normal or outlier to identify specific events based on WSN implemented in a water pipeline. Moreover, we have compared our adopted approach with other four classifiers including Bayesian Network, Neural Network, K-Nearest Neighbors and Decision Tree. Thus, the suggested technique is validated in terms of accuracy and training time.

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2017

In the last years, Wireless Sensor Networks (WSNs) has become a very interesting field for resear... more In the last years, Wireless Sensor Networks (WSNs) has become a very interesting field for researchers. They are widely employed to solve several problems in different domains like agriculture, monitoring, health care. Then, outlier detection method is considered as a very important step in construction of sensor network systems to ensure data grade for perfect decision making. So, this task helps to create a gainful approach to find out if data is normal regular or an outlier. Therefore, in this paper, a newest outlier’s detection and classification model has been developed to complement the existing hardware redundancy and limit checking techniques. To overcome these problems, we present a Combined Outliers Detection Method (CODM) for water pipeline to detect damaged data in WSNs. To this end, the application of kernel-based non-linear approach is introduced. The main objective of this work was to combine the advantages of Kernel Fisher Discriminant Analysis (KFDA) and Support Vec...

2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2017

Advances in data processing, electronics and wireless communications have made the vision of wire... more Advances in data processing, electronics and wireless communications have made the vision of wireless sensor nodes an important reality. Wireless sensor nodes are cheap tiny sensor apparatus integrated with sensing, processing and short-range wireless communication abilities. Recent experimentations have been exploding in terms of usage and performance to improve the way of working in many contexts like the detection of outliers in a water pipeline. These pipelines are often subject to failure like erosion and sabotage that can cause high financial, environmental and health risks. Consequently, detecting damage and esteeming its location is very important. For this case, several techniques have been investigated in the research community. In this paper, we have constructed a novel leakage detection model based on Fisher Discriminant Analysis (FDA) and the Support Vector Machine (SVM) classifier for the detection of outliers based on Wireless Sensors Networks implemented in a water p...

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017

For classification data, we use Wireless sensor networks (WSNs) as hardware for collecting data f... more For classification data, we use Wireless sensor networks (WSNs) as hardware for collecting data from harsh environments and controlling important events in phenomena. To evaluate the quality of a sensor and its network, we use the accuracy of sensor readings as surely one of the most important measures. Therefore, for anomalous measurement, real time detection is required to guarantee the quality of data collected by these networks. In this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have shown excellent performance. So, to extract relevant feature for classification and to prevent from the events, we use KPCA based on Mahalanobis kernel as a preprocessing step. In the original space, the totality of computation is done thus saving computing time. Then the classification was done on real Intel Berkeley data collecting from urban area. Compared to a standard KPCA, the results show that our method are specially designed to be used in the field of wireless sensor networks (WSNs).

Journal of King Saud University - Computer and Information Sciences, 2019

Computer Networks, 2019

Abstract Currently, the technology for sensing and control has become the potential for significa... more Abstract Currently, the technology for sensing and control has become the potential for significant advances not only in science and business but equally important on a range of industrial applications. In addition to reducing costs and increasing efficiencies for monitoring systems, Wireless Sensor Networking (WSN) is expected to bring consumers a new generation of conveniences. However, there are issues when treating extremely interrelated, composite, and noisy databases with a large dimension. For that purpose, outliers detection techniques (ODT) are used for an effective monitoring system to ensure the safety of a transport process. Therefore, in this paper, a novel model of outliers detection and classification has been developed to complement the existing hardware redundancy and limit checking techniques. To overcome these problems, we present a Combined Kernelized Outliers Detection Technique (CKODT) based WSN for damages detection in water pipeline. Initially, training pressure measurement is collected from the sensors which are implemented outside the pipe. Next, these data are fed into the data reduction algorithm as known as Kernel Fisher Discriminant Analysis (KFDA) to create discriminant vectors. Then, these vectors were utilized as inputs for the One Class Support Vector Machine (OCSVM) method to classify the feature sets which were extracted using the proposed technique. The main objective of this work was to combine the advantages of these tools to enhance the performance of the monitoring water pipeline system. The accuracy of our Combined Kernelized Outliers Detection Technique for classification was analyzed and compared with variety of techniques. The experimental results showed the improvements of the proposed framework compared to other techniques in the context of damage detection in the monitoring water pipeline process.

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)

Nowadays, many wireless sensor networks have been distributed in the real world to collect valuab... more Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This paper aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017

Pipelines constitute one of the most important ways to transport large amounts of water through l... more Pipelines constitute one of the most important ways to transport large amounts of water through long distance. However, existing damage detection methods do not well in monitoring pipelines due to the harsh environmental condition. for this reason, current systems need to be more automated, efficient and accurate for continuous supervising about damage. For this purpose, Wireless Sensors Networks (WSN) have been brought into the research community in the context of monitoring water pipeline. In this paper, we have discussed the task amounts to provide low-cost and real-time damage detection technique. Our proposed technique is based on Fisher discriminant analysis (FDA) coupled with Support Vector Machine (SVM). We aim to recognize data as normal or outlier to identify specific events based on WSN implemented in a water pipeline. Moreover, we have compared our adopted approach with other four classifiers including Bayesian Network, Neural Network, K-Nearest Neighbors and Decision Tree. Thus, the suggested technique is validated in terms of accuracy and training time.

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2017

In the last years, Wireless Sensor Networks (WSNs) has become a very interesting field for resear... more In the last years, Wireless Sensor Networks (WSNs) has become a very interesting field for researchers. They are widely employed to solve several problems in different domains like agriculture, monitoring, health care. Then, outlier detection method is considered as a very important step in construction of sensor network systems to ensure data grade for perfect decision making. So, this task helps to create a gainful approach to find out if data is normal regular or an outlier. Therefore, in this paper, a newest outlier’s detection and classification model has been developed to complement the existing hardware redundancy and limit checking techniques. To overcome these problems, we present a Combined Outliers Detection Method (CODM) for water pipeline to detect damaged data in WSNs. To this end, the application of kernel-based non-linear approach is introduced. The main objective of this work was to combine the advantages of Kernel Fisher Discriminant Analysis (KFDA) and Support Vec...

2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2017

Advances in data processing, electronics and wireless communications have made the vision of wire... more Advances in data processing, electronics and wireless communications have made the vision of wireless sensor nodes an important reality. Wireless sensor nodes are cheap tiny sensor apparatus integrated with sensing, processing and short-range wireless communication abilities. Recent experimentations have been exploding in terms of usage and performance to improve the way of working in many contexts like the detection of outliers in a water pipeline. These pipelines are often subject to failure like erosion and sabotage that can cause high financial, environmental and health risks. Consequently, detecting damage and esteeming its location is very important. For this case, several techniques have been investigated in the research community. In this paper, we have constructed a novel leakage detection model based on Fisher Discriminant Analysis (FDA) and the Support Vector Machine (SVM) classifier for the detection of outliers based on Wireless Sensors Networks implemented in a water p...

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017

For classification data, we use Wireless sensor networks (WSNs) as hardware for collecting data f... more For classification data, we use Wireless sensor networks (WSNs) as hardware for collecting data from harsh environments and controlling important events in phenomena. To evaluate the quality of a sensor and its network, we use the accuracy of sensor readings as surely one of the most important measures. Therefore, for anomalous measurement, real time detection is required to guarantee the quality of data collected by these networks. In this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have shown excellent performance. So, to extract relevant feature for classification and to prevent from the events, we use KPCA based on Mahalanobis kernel as a preprocessing step. In the original space, the totality of computation is done thus saving computing time. Then the classification was done on real Intel Berkeley data collecting from urban area. Compared to a standard KPCA, the results show that our method are specially designed to be used in the field of wireless sensor networks (WSNs).

Journal of King Saud University - Computer and Information Sciences, 2019

Computer Networks, 2019

Abstract Currently, the technology for sensing and control has become the potential for significa... more Abstract Currently, the technology for sensing and control has become the potential for significant advances not only in science and business but equally important on a range of industrial applications. In addition to reducing costs and increasing efficiencies for monitoring systems, Wireless Sensor Networking (WSN) is expected to bring consumers a new generation of conveniences. However, there are issues when treating extremely interrelated, composite, and noisy databases with a large dimension. For that purpose, outliers detection techniques (ODT) are used for an effective monitoring system to ensure the safety of a transport process. Therefore, in this paper, a novel model of outliers detection and classification has been developed to complement the existing hardware redundancy and limit checking techniques. To overcome these problems, we present a Combined Kernelized Outliers Detection Technique (CKODT) based WSN for damages detection in water pipeline. Initially, training pressure measurement is collected from the sensors which are implemented outside the pipe. Next, these data are fed into the data reduction algorithm as known as Kernel Fisher Discriminant Analysis (KFDA) to create discriminant vectors. Then, these vectors were utilized as inputs for the One Class Support Vector Machine (OCSVM) method to classify the feature sets which were extracted using the proposed technique. The main objective of this work was to combine the advantages of these tools to enhance the performance of the monitoring water pipeline system. The accuracy of our Combined Kernelized Outliers Detection Technique for classification was analyzed and compared with variety of techniques. The experimental results showed the improvements of the proposed framework compared to other techniques in the context of damage detection in the monitoring water pipeline process.

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)

Nowadays, many wireless sensor networks have been distributed in the real world to collect valuab... more Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This paper aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.