Attique Shah - Academia.edu (original) (raw)

Papers by Attique Shah

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

Large amounts of annual costs are made for safety and compensations of accidents in urban interse... more Large amounts of annual costs are made for safety and compensations of accidents in urban intersections, even those with traffic lights. The main reason for accidents seems to be the convergence of different traffic flows in a particular area. The presented paper used 576 cases at intersections comprised of accident data, plus 45 fatal accident data, geometry and control status in Isfahan-Iran intersections to analyse and predict the cause of leading-to-death/injury accidents. This study used the k-modes clustering method as the main segmentation task on intersection accident data to decrease the association rule mining algorithm’s search space and remove heterogeneity of road accident data. Association rule mining helps identify the different circumstances associated with an accident in each group obtained by the k-modes algorithm. The research result shows that the extracted rules of the dataset display some valuable information that can be useful to prevent and overcome accidents.

2021 International Conference on Innovative Computing (ICIC)

Numerous statistical machine learning techniques have been proposed for solving a variety of clas... more Numerous statistical machine learning techniques have been proposed for solving a variety of classification problems. Prototype-based models, such as standard learning vector quantization (LVQ) and its extensions, have been widely applied to various applications domains due to their intuitive nature and simplicity. This paper adopts LVQ and its three variants, namely, generalized learning vector quantization (GLVQ), relevance learning vector quantization (RLVQ), and generalized relevance learning vector quantization (GRLVQ) algorithms for the problem of diabetes disease classification. Four different error metrics were used to measure the robustness and accuracy of each classifier. These measures include root mean squared error (RMSE), mean zero–one error (MZE), mean absolute error (MAE), and macro averaged mean absolute error (MMAE). The obtained results indicate that GRLVQ was very effective, which produced a minimum error in terms of all error metrics used.

2021 International Conference on Innovative Computing (ICIC)

Activation functions are an essential parameter in deep learning models, primarily when it is dep... more Activation functions are an essential parameter in deep learning models, primarily when it is deployed with CNN. These deep neural networks not only deal with linear data but handles different non-linear classifications as well. Automatic malaria parasite detection using blood smear images is a popular classification application by using CNN architecture. Several models use various activation functions for this parasite detection. Choosing among these activation functions for a custom model sometimes is a time-consuming task. In this paper, we have evaluated Sigmoid, Hyperbolic Tangent Function (Tanh), Rectified Linear Units (ReLU), Leaky ReLU, and Swish activation functions for malaria Parasite detection in the CNN model. It has been observed in our empirical results that the Swish function performs better than others in terms of accuracy and loss value on a given malaria parasite images dataset.

2021 2nd International Informatics and Software Engineering Conference (IISEC)

In today's world, disasters, both natural and manmade, are becoming increasingly frequent, an... more In today's world, disasters, both natural and manmade, are becoming increasingly frequent, and new solutions are of a compelling need to provide and disseminate information about these disasters to the public and concerned authorities in an effective and efficient manner. One of the most frequently used ways for information dissemination today is through social media, and when it comes to real-time information, Twitter is often the channel of choice. Thus, this paper discusses how Big Data Analytics (BDA) can take advantage of information streaming from Twitter to generate alerts and provide information in real-time on ongoing disasters. The paper proposes TAGS (Twitter Alert Generation System), a novel solution for collecting and analyzing social media streaming data in realtime and subsequently issue warnings related to ongoing disasters using a combination of Hadoop and Spark frameworks. The paper tests and evaluates the proposed solution using Twitter data from the 2018 earthquake in Palu City, Sulawesi, Indonesia. The proposed architecture was able to issue alert messages on various disaster scenarios and identify critical information that can be utilized for further analysis. Moreover, the performance of the proposed solution is assessed with respect to processing time and throughput that shows reliable system efficiency.

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

In this era of advanced healthcare facilities, where health and technology are immensely integrat... more In this era of advanced healthcare facilities, where health and technology are immensely integrated, the role of big data analytics is evolving in every aspect of healthcare. The large influx of patients in hospitals is a fatal problem for the hospital management systems. The patient crowding problem can cause potential consequences, including increased mortality rate, unnecessary labor cost, poor customer service, ambulance divergence, and cancelation of equipment. Therefore, it is crucial to utilize machine learning to prevent overcrowding and enhance resource allocation. This research study has used the openly available dataset of patients and a combination of machine learning algorithms such as logistic regression, support vector machine, k-nearest neighbor, and decision tree to analyze patient admission trends for effective decision making. According to the simulation results, the decision tree algorithm achieved the best accuracy with a score of (0.89). While on the parameter of the area under the receiver operating characteristics (AUROC), the logistic regression algorithm performed best with an AUROC score of (0.74), followed by the support vector machine with a score of (0.73). The least performers are KNN and decision tree with AUROC of (0.67) and (0.53), respectively.

This paper is an SLR about SDN-based IoT management frameworks.

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

In recent years, the advent of social media platforms has led users to freely express their opini... more In recent years, the advent of social media platforms has led users to freely express their opinions on various subjects, including politics, society, health, education, finance, and even business-related issues. However, this widespread usage of social media has also increased the risk of its misuse by some groups resulting in spreading hate speeches or offensive language. This paper investigates machine learning methods for hate speech classification and compared its results with advanced deep learning models to evaluate its efficiency. The data from Twitter, a popular microblogging social media platform for sharing short digital content, was used for experiments in this study. Each tweet was labeled into one of three categories: hate speech, offensive, neutral. Four machine learning methods were investigated: logistic regression (LR), random forest (RF), naive Bayes (NB), and support vector machine (SVM). The results were compared with two deep learning-based models: recurrent neural networks (RNN) and bidirectional encoder representations (BERT). The overall results indicated that both machine learning and deep learning models were effective for hate speech recognition. The highest overall accuracy was obtained using BERT (87.78%), while SVM produced the best (84.66%) among traditional classifiers.

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

In the last few decades, social media usage has exponentially increased, and people often share i... more In the last few decades, social media usage has exponentially increased, and people often share information covering various topics of interest. The social media platforms such as Twitter allow users to share images, audio, videos, and text. The textual content can be used as a powerful tool for sentiment analysis. The main goal of this work is to investigate the deep learning models for sentiment analysis of tweets related to COVID-19. The dataset was obtained using tweeter web API between December 20, 2019, to December 15, 2020, and labels were assigned manually as positive, negative, or neutral. Two deep learning models were selected for sentiment analysis: Recurrent Neural Networks (RNN) and the Bidirectional Encoder Representations (BERT) model. The experimental results showed that both RNN and BERT models were effective for sentiment analysis, resulting in 86.4% and 83.14% accuracy, respectively.

IEEE Access, 2020

The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its... more The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure...

Wireless Communications and Mobile Computing

xCalamities such as earthquakes and tsunami affect communication services by devastating the comm... more xCalamities such as earthquakes and tsunami affect communication services by devastating the communication network and electrical infrastructure. Multihop relay networks can be deployed to restore the communication environment quickly in catastrophe-stricken areas. However, performance in terms of throughput is affected by deploying the relay networks. In wireless local area networks (WLANs), the primary purpose of multiband transmission employing multihop relay networks is to increase the throughput and reduce the latency. In the future, wireless networks are believed to carry high throughput, more data rates, and less latency by expanding bandwidth-demanding applications. Simultaneous multiband transmission in WLAN systems is considered to increase the coverage area without power escalation. Due to the inherent characteristics of different bands and channel conditions, transmission rates tend to be different. The impact of such conditions may cater to the disproportional distribut...

IEEE Internet of Things Journal

As the usability of IoT devices increases, the security threats and vulnerabilities associated wi... more As the usability of IoT devices increases, the security threats and vulnerabilities associated with these resource-constrained IoT devices also rise. One of the major threats to IoT devices is Distributed Denial of Service (DDoS). To make the security of IoT devices effective and resilient, continuous monitoring and early detection, along with adaptive decision making, are required. These challenges can be addressed with Software-defined Networking (SDN), which provides an opportunity for effectively managing the DDoS threats faced by IoT devices. This research proposes a novel SDN-based secure IoT framework that can detect the vulnerabilities in IoT devices or malicious traffic generated by IoT devices using session IP counter and IP Payload analysis. The framework’s DDoS attack detection module consisting of proposed algorithms can easily detect the DDoS attack in the SD-IoT network by analyzing different parameters even with a large traffic volume. These techniques are implemented on an SDN controller and tested by generating a large volume of traffic from a compromised node which is then detected and notified. According to results and comparative analysis, the proposed framework detects DDoS attacks in the early stage with high accuracy and detection rate from 98% to 100%, having a low false-positive rate.

Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure.... more Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic sim...

Sustainable Cities and Society

Future Data and Security Engineering

IEEE Sensors Journal

Billions of IoT devices and smart objects are already in operation today and even more are expect... more Billions of IoT devices and smart objects are already in operation today and even more are expected to be on the network over time. These IoT devices will generate enormous amounts of data that cannot be allowed to transmit on the network without end-to-end encryption or any trust and security mechanism. Currently, we have certificate authorities that certify the identity of a network device by binding its identity with its public key. However, these certificate authorities are centralized in structure and will not be able to individually certify billions of IoT devices entirely. In this paper, we propose that in an SDN-based IoT network, the identities, i.e., public keys and trust indices of IoT devices, can be stored on a blockchain to ensure immutability and tamper-resistance. The paper presents a novel scalable solution for key and trust management of IoT devices in IoT networks, with a successful proof-of-concept that proves the scalability of the proposed solution. The combination of an IoT network along with blockchain technology and softwaredefined networking (SDN) is effectively demonstrated through simulation that is able to store the public keys of IoT devices on the blockchain and route the network traffic efficiently through SDN. The performance of the proposed solution is evaluated in terms of throughput and access time delay. The results illustrate that access delay and throughput were not affected linearly or exponentially and the proposed solution shows no significant degradation in the performance with the increase in the number of nodes and packets.

IEEE Access

Digital twinning is one of the top ten technology trends in the last couple of years, due to its ... more Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-ofthe-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities. INDEX TERMS Digital twin, artificial intelligence, machine learning, big data, industry 4.0.

2021 the 3rd International Conference on Big Data Engineering and Technology (BDET)

Due to the rapid impact of IT technology, data across the globe is growing exponentially as compa... more Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).

Sustainability

In the last decade, technological advancements in the cyber-physical system have set the basis fo... more In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the inc...

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

Large amounts of annual costs are made for safety and compensations of accidents in urban interse... more Large amounts of annual costs are made for safety and compensations of accidents in urban intersections, even those with traffic lights. The main reason for accidents seems to be the convergence of different traffic flows in a particular area. The presented paper used 576 cases at intersections comprised of accident data, plus 45 fatal accident data, geometry and control status in Isfahan-Iran intersections to analyse and predict the cause of leading-to-death/injury accidents. This study used the k-modes clustering method as the main segmentation task on intersection accident data to decrease the association rule mining algorithm’s search space and remove heterogeneity of road accident data. Association rule mining helps identify the different circumstances associated with an accident in each group obtained by the k-modes algorithm. The research result shows that the extracted rules of the dataset display some valuable information that can be useful to prevent and overcome accidents.

2021 International Conference on Innovative Computing (ICIC)

Numerous statistical machine learning techniques have been proposed for solving a variety of clas... more Numerous statistical machine learning techniques have been proposed for solving a variety of classification problems. Prototype-based models, such as standard learning vector quantization (LVQ) and its extensions, have been widely applied to various applications domains due to their intuitive nature and simplicity. This paper adopts LVQ and its three variants, namely, generalized learning vector quantization (GLVQ), relevance learning vector quantization (RLVQ), and generalized relevance learning vector quantization (GRLVQ) algorithms for the problem of diabetes disease classification. Four different error metrics were used to measure the robustness and accuracy of each classifier. These measures include root mean squared error (RMSE), mean zero–one error (MZE), mean absolute error (MAE), and macro averaged mean absolute error (MMAE). The obtained results indicate that GRLVQ was very effective, which produced a minimum error in terms of all error metrics used.

2021 International Conference on Innovative Computing (ICIC)

Activation functions are an essential parameter in deep learning models, primarily when it is dep... more Activation functions are an essential parameter in deep learning models, primarily when it is deployed with CNN. These deep neural networks not only deal with linear data but handles different non-linear classifications as well. Automatic malaria parasite detection using blood smear images is a popular classification application by using CNN architecture. Several models use various activation functions for this parasite detection. Choosing among these activation functions for a custom model sometimes is a time-consuming task. In this paper, we have evaluated Sigmoid, Hyperbolic Tangent Function (Tanh), Rectified Linear Units (ReLU), Leaky ReLU, and Swish activation functions for malaria Parasite detection in the CNN model. It has been observed in our empirical results that the Swish function performs better than others in terms of accuracy and loss value on a given malaria parasite images dataset.

2021 2nd International Informatics and Software Engineering Conference (IISEC)

In today's world, disasters, both natural and manmade, are becoming increasingly frequent, an... more In today's world, disasters, both natural and manmade, are becoming increasingly frequent, and new solutions are of a compelling need to provide and disseminate information about these disasters to the public and concerned authorities in an effective and efficient manner. One of the most frequently used ways for information dissemination today is through social media, and when it comes to real-time information, Twitter is often the channel of choice. Thus, this paper discusses how Big Data Analytics (BDA) can take advantage of information streaming from Twitter to generate alerts and provide information in real-time on ongoing disasters. The paper proposes TAGS (Twitter Alert Generation System), a novel solution for collecting and analyzing social media streaming data in realtime and subsequently issue warnings related to ongoing disasters using a combination of Hadoop and Spark frameworks. The paper tests and evaluates the proposed solution using Twitter data from the 2018 earthquake in Palu City, Sulawesi, Indonesia. The proposed architecture was able to issue alert messages on various disaster scenarios and identify critical information that can be utilized for further analysis. Moreover, the performance of the proposed solution is assessed with respect to processing time and throughput that shows reliable system efficiency.

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

In this era of advanced healthcare facilities, where health and technology are immensely integrat... more In this era of advanced healthcare facilities, where health and technology are immensely integrated, the role of big data analytics is evolving in every aspect of healthcare. The large influx of patients in hospitals is a fatal problem for the hospital management systems. The patient crowding problem can cause potential consequences, including increased mortality rate, unnecessary labor cost, poor customer service, ambulance divergence, and cancelation of equipment. Therefore, it is crucial to utilize machine learning to prevent overcrowding and enhance resource allocation. This research study has used the openly available dataset of patients and a combination of machine learning algorithms such as logistic regression, support vector machine, k-nearest neighbor, and decision tree to analyze patient admission trends for effective decision making. According to the simulation results, the decision tree algorithm achieved the best accuracy with a score of (0.89). While on the parameter of the area under the receiver operating characteristics (AUROC), the logistic regression algorithm performed best with an AUROC score of (0.74), followed by the support vector machine with a score of (0.73). The least performers are KNN and decision tree with AUROC of (0.67) and (0.53), respectively.

This paper is an SLR about SDN-based IoT management frameworks.

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

In recent years, the advent of social media platforms has led users to freely express their opini... more In recent years, the advent of social media platforms has led users to freely express their opinions on various subjects, including politics, society, health, education, finance, and even business-related issues. However, this widespread usage of social media has also increased the risk of its misuse by some groups resulting in spreading hate speeches or offensive language. This paper investigates machine learning methods for hate speech classification and compared its results with advanced deep learning models to evaluate its efficiency. The data from Twitter, a popular microblogging social media platform for sharing short digital content, was used for experiments in this study. Each tweet was labeled into one of three categories: hate speech, offensive, neutral. Four machine learning methods were investigated: logistic regression (LR), random forest (RF), naive Bayes (NB), and support vector machine (SVM). The results were compared with two deep learning-based models: recurrent neural networks (RNN) and bidirectional encoder representations (BERT). The overall results indicated that both machine learning and deep learning models were effective for hate speech recognition. The highest overall accuracy was obtained using BERT (87.78%), while SVM produced the best (84.66%) among traditional classifiers.

2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

In the last few decades, social media usage has exponentially increased, and people often share i... more In the last few decades, social media usage has exponentially increased, and people often share information covering various topics of interest. The social media platforms such as Twitter allow users to share images, audio, videos, and text. The textual content can be used as a powerful tool for sentiment analysis. The main goal of this work is to investigate the deep learning models for sentiment analysis of tweets related to COVID-19. The dataset was obtained using tweeter web API between December 20, 2019, to December 15, 2020, and labels were assigned manually as positive, negative, or neutral. Two deep learning models were selected for sentiment analysis: Recurrent Neural Networks (RNN) and the Bidirectional Encoder Representations (BERT) model. The experimental results showed that both RNN and BERT models were effective for sentiment analysis, resulting in 86.4% and 83.14% accuracy, respectively.

IEEE Access, 2020

The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its... more The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure...

Wireless Communications and Mobile Computing

xCalamities such as earthquakes and tsunami affect communication services by devastating the comm... more xCalamities such as earthquakes and tsunami affect communication services by devastating the communication network and electrical infrastructure. Multihop relay networks can be deployed to restore the communication environment quickly in catastrophe-stricken areas. However, performance in terms of throughput is affected by deploying the relay networks. In wireless local area networks (WLANs), the primary purpose of multiband transmission employing multihop relay networks is to increase the throughput and reduce the latency. In the future, wireless networks are believed to carry high throughput, more data rates, and less latency by expanding bandwidth-demanding applications. Simultaneous multiband transmission in WLAN systems is considered to increase the coverage area without power escalation. Due to the inherent characteristics of different bands and channel conditions, transmission rates tend to be different. The impact of such conditions may cater to the disproportional distribut...

IEEE Internet of Things Journal

As the usability of IoT devices increases, the security threats and vulnerabilities associated wi... more As the usability of IoT devices increases, the security threats and vulnerabilities associated with these resource-constrained IoT devices also rise. One of the major threats to IoT devices is Distributed Denial of Service (DDoS). To make the security of IoT devices effective and resilient, continuous monitoring and early detection, along with adaptive decision making, are required. These challenges can be addressed with Software-defined Networking (SDN), which provides an opportunity for effectively managing the DDoS threats faced by IoT devices. This research proposes a novel SDN-based secure IoT framework that can detect the vulnerabilities in IoT devices or malicious traffic generated by IoT devices using session IP counter and IP Payload analysis. The framework’s DDoS attack detection module consisting of proposed algorithms can easily detect the DDoS attack in the SD-IoT network by analyzing different parameters even with a large traffic volume. These techniques are implemented on an SDN controller and tested by generating a large volume of traffic from a compromised node which is then detected and notified. According to results and comparative analysis, the proposed framework detects DDoS attacks in the early stage with high accuracy and detection rate from 98% to 100%, having a low false-positive rate.

Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure.... more Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic sim...

Sustainable Cities and Society

Future Data and Security Engineering

IEEE Sensors Journal

Billions of IoT devices and smart objects are already in operation today and even more are expect... more Billions of IoT devices and smart objects are already in operation today and even more are expected to be on the network over time. These IoT devices will generate enormous amounts of data that cannot be allowed to transmit on the network without end-to-end encryption or any trust and security mechanism. Currently, we have certificate authorities that certify the identity of a network device by binding its identity with its public key. However, these certificate authorities are centralized in structure and will not be able to individually certify billions of IoT devices entirely. In this paper, we propose that in an SDN-based IoT network, the identities, i.e., public keys and trust indices of IoT devices, can be stored on a blockchain to ensure immutability and tamper-resistance. The paper presents a novel scalable solution for key and trust management of IoT devices in IoT networks, with a successful proof-of-concept that proves the scalability of the proposed solution. The combination of an IoT network along with blockchain technology and softwaredefined networking (SDN) is effectively demonstrated through simulation that is able to store the public keys of IoT devices on the blockchain and route the network traffic efficiently through SDN. The performance of the proposed solution is evaluated in terms of throughput and access time delay. The results illustrate that access delay and throughput were not affected linearly or exponentially and the proposed solution shows no significant degradation in the performance with the increase in the number of nodes and packets.

IEEE Access

Digital twinning is one of the top ten technology trends in the last couple of years, due to its ... more Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-ofthe-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities. INDEX TERMS Digital twin, artificial intelligence, machine learning, big data, industry 4.0.

2021 the 3rd International Conference on Big Data Engineering and Technology (BDET)

Due to the rapid impact of IT technology, data across the globe is growing exponentially as compa... more Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).

Sustainability

In the last decade, technological advancements in the cyber-physical system have set the basis fo... more In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the inc...