Supplemental Conference Proceedings for the Short Papers (Non-peer reviewed) of IEEE CIBCB 2021
- Date: 2021
- Type: Text , Conference proceedings
- Relation: IEEE CIBCB 2021, Melbourne ; 13th to 15th October 2021
- Full Text:
- Date: 2021
- Type: Text , Conference proceedings
- Relation: IEEE CIBCB 2021, Melbourne ; 13th to 15th October 2021
- Full Text:
Dynamic voltage signature of large scale PV enriched streesed power system
- Alzahrani, Saeed, Shah, Rakibuzzaman, Mithulananthan, Nadarajah, Sode-Yome, Arthit
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Sode-Yome, Arthit
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 275-280
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- Description: Renewable power generations including flexible demand and energy storage systems leverage significant changes in network operation. Thereby, power systems with high renewable penetration manifest deteriorated resilience to disturbances. Hence, the stable operation of the system could be affected. With a paradigm shift, dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs). Predicting the dynamic voltage signature for the transmission system with high penetration of renewables is essential to assist in selecting appropriate corrective control. This paper utilized a comprehensive assessment framework to identify the dynamic voltage signature of the power system with PV and various loads. The voltage recovery index has been chosen as the quantifiable index to extricate the dynamic voltage signature. The applicability of the proposed framework is discussed using simulation studies on the IEEE-39 bus test system. © 2020 IEEE.
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Sode-Yome, Arthit
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 275-280
- Full Text:
- Reviewed:
- Description: Renewable power generations including flexible demand and energy storage systems leverage significant changes in network operation. Thereby, power systems with high renewable penetration manifest deteriorated resilience to disturbances. Hence, the stable operation of the system could be affected. With a paradigm shift, dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs). Predicting the dynamic voltage signature for the transmission system with high penetration of renewables is essential to assist in selecting appropriate corrective control. This paper utilized a comprehensive assessment framework to identify the dynamic voltage signature of the power system with PV and various loads. The voltage recovery index has been chosen as the quantifiable index to extricate the dynamic voltage signature. The applicability of the proposed framework is discussed using simulation studies on the IEEE-39 bus test system. © 2020 IEEE.
Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations
- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan, Choudhury, Tanveer
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 Vol. 2020-June, p. 536-542
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- Description: This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space . This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method. © 2020 IEEE.
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 Vol. 2020-June, p. 536-542
- Full Text:
- Reviewed:
- Description: This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space . This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method. © 2020 IEEE.
Impact of PV plant and load models on system strength and voltage recovery of power systems
- Alshareef, Abdulrhman, Shah, Rakibuzzaman, Mithulananthan, Nadarajah
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 263-268
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- Description: In recent years, non-conventional inverter-based sources, namely, wind, PV, and others have emerged as excellent alternatives to the traditional synchronous machine for power generation. It has also been reported that the so-called system strength may be reduced with high penetration of non-conventional generations (NCGs). A number of methods have been used to assess system strength which may not reflect the interdependency or reciprocal influence of various factors affecting it. This paper presents a thorough assessment to quantify the implications of and the interaction of various factors affecting system strength, with the voltage recovery index being used as a quantification tool. © 2020 IEEE.
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 263-268
- Full Text:
- Reviewed:
- Description: In recent years, non-conventional inverter-based sources, namely, wind, PV, and others have emerged as excellent alternatives to the traditional synchronous machine for power generation. It has also been reported that the so-called system strength may be reduced with high penetration of non-conventional generations (NCGs). A number of methods have been used to assess system strength which may not reflect the interdependency or reciprocal influence of various factors affecting it. This paper presents a thorough assessment to quantify the implications of and the interaction of various factors affecting system strength, with the voltage recovery index being used as a quantification tool. © 2020 IEEE.
Influence of induction motor in stability of power system with high penetration of large-scale PV
- Alshareef, Abdulrhman, Nadarajah, Mithulananthan, Shah, Rakibuzzaman
- Authors: Alshareef, Abdulrhman , Nadarajah, Mithulananthan , Shah, Rakibuzzaman
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 269-274
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- Description: Inverter-Based Energy Resources (IBERs) have become an ordinary portion of the generation mix in power systems. Furthermore, converter-based technology has come to dominate modern motor loads on the consumption side. This transition in components towards accommodating power electronic devices alters the dynamic response of the power system. This paper investigates the impact of these elements on the dynamic stability of the power system. Firstly, this study successes to optimize a suitable model for converter-based motor loads. Secondly, indices of transient and voltage stabilities are used to quantify the strength of the power system at different circumstances incorporating the induction motor loads. Finally, this analysis provides an insight into the mutual interactions between transient and voltage stabilities. It is concluded that converter-based motor loads improve the voltage recovery when compared with direct-connected induction motors. However, the system is vulnerable to transient stability with the proliferation of inverter-based motor loads when IBERs dominant in the generation mix. © 2020 IEEE.
- Authors: Alshareef, Abdulrhman , Nadarajah, Mithulananthan , Shah, Rakibuzzaman
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 269-274
- Full Text:
- Reviewed:
- Description: Inverter-Based Energy Resources (IBERs) have become an ordinary portion of the generation mix in power systems. Furthermore, converter-based technology has come to dominate modern motor loads on the consumption side. This transition in components towards accommodating power electronic devices alters the dynamic response of the power system. This paper investigates the impact of these elements on the dynamic stability of the power system. Firstly, this study successes to optimize a suitable model for converter-based motor loads. Secondly, indices of transient and voltage stabilities are used to quantify the strength of the power system at different circumstances incorporating the induction motor loads. Finally, this analysis provides an insight into the mutual interactions between transient and voltage stabilities. It is concluded that converter-based motor loads improve the voltage recovery when compared with direct-connected induction motors. However, the system is vulnerable to transient stability with the proliferation of inverter-based motor loads when IBERs dominant in the generation mix. © 2020 IEEE.
Motivational factors of Australian mobile gamers
- Greenwood, Jordan, Achterbosch, Leigh, Meredith, Grant, Vamplew, Peter
- Authors: Greenwood, Jordan , Achterbosch, Leigh , Meredith, Grant , Vamplew, Peter
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 4th-6th February 2020 p. 6
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- Description: Mobile games are a fast growing industry, overtaking all other video game platforms with year on year increases in revenue. Many studies have been conducted to explore the motivations of why video games players play their selected games. However very little research has focused on mobile gamers. In addition, Australian studies on the topic are sparse. This paper aimed to discover what motivates a mobile gamer from the perspective of the initial motivational factors attracting them to a mobile game, and the motivational factors that provide interest to continue playing and thereby increase game longevity. A survey was conducted online for Australian participants, which attracted 123 respondents. The survey was formulated by focusing on the 12 key subcomponents as motivational factors of the Gamer Motivational Profile v2 model devised by Quantic Foundry. It was discovered that mobile gamers are a completely different breed of gamer in contrast to the general video gamer. Strategy and challenge which are subcomponents of mastery proved popular among all mobile gamers, while destruction and excitement, subcomponents of action, were often the least motivating factors of all. With the newly discovered data, perhaps mobile game developers can pursue the correct avenues of game design when catering to their target audience.
- Authors: Greenwood, Jordan , Achterbosch, Leigh , Meredith, Grant , Vamplew, Peter
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 4th-6th February 2020 p. 6
- Full Text:
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- Description: Mobile games are a fast growing industry, overtaking all other video game platforms with year on year increases in revenue. Many studies have been conducted to explore the motivations of why video games players play their selected games. However very little research has focused on mobile gamers. In addition, Australian studies on the topic are sparse. This paper aimed to discover what motivates a mobile gamer from the perspective of the initial motivational factors attracting them to a mobile game, and the motivational factors that provide interest to continue playing and thereby increase game longevity. A survey was conducted online for Australian participants, which attracted 123 respondents. The survey was formulated by focusing on the 12 key subcomponents as motivational factors of the Gamer Motivational Profile v2 model devised by Quantic Foundry. It was discovered that mobile gamers are a completely different breed of gamer in contrast to the general video gamer. Strategy and challenge which are subcomponents of mastery proved popular among all mobile gamers, while destruction and excitement, subcomponents of action, were often the least motivating factors of all. With the newly discovered data, perhaps mobile game developers can pursue the correct avenues of game design when catering to their target audience.
OFFER: A Motif Dimensional Framework for Network Representation Learning
- Yu, Shuo, Xia, Feng, Xu, Jin, Chen, Zhikui, Lee, Ivan
- Authors: Yu, Shuo , Xia, Feng , Xu, Jin , Chen, Zhikui , Lee, Ivan
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
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- Description: Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.
- Authors: Yu, Shuo , Xia, Feng , Xu, Jin , Chen, Zhikui , Lee, Ivan
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
- Full Text:
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- Description: Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.
Partial undersampling of imbalanced data for cyber threats detection
- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal
- Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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- Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
- Description: E1
- Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
- Full Text:
- Reviewed:
- Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
- Description: E1
Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location
- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan, Choudhury, Tanveer
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
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- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
- Full Text:
- Reviewed:
- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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- Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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- Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Assessing transformer oil quality using deep convolutional networks
- Alam, Mohammad, Karmakar, Gour, Islam, Syed, Kamruzzaman, Joarder, Chetty, Madhu, Lim, Suryani, Appuhamillage, Gayan, Chattopadhyay, Gopi, Wilcox, Steve, Verheyen, Vincent
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
- Full Text:
- Reviewed:
- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
Blockchain leveraged task migration in body area sensor networks
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
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- Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
- Description: E1
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
- Full Text:
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- Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
- Description: E1
Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control
- Farivar, Faezeh, Haghighi, Mohammad, Barchinezhad, Soheila, Jolfaei, Alireza
- Authors: Farivar, Faezeh , Haghighi, Mohammad , Barchinezhad, Soheila , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1143-1148
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- Description: Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.
- Authors: Farivar, Faezeh , Haghighi, Mohammad , Barchinezhad, Soheila , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1143-1148
- Full Text:
- Reviewed:
- Description: Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.
Development of a risk-based maintenance (RBM) strategy for sewerage pumping station network
- Masud, M., Chattopadhyay, Gopi, Gunawan, Indra
- Authors: Masud, M. , Chattopadhyay, Gopi , Gunawan, Indra
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019 p. 455-458
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- Description: Industries have been facing ever-increasing challenges to do more with less under ongoing budget constraints. They are pushing the boundary by challenging the OEM recommended maintenance intervals and relaxing or tightening based on where it is needed. This is also evident in water sector where industries are trying to do targeted maintenance based on balancing costs, performances and risks. The unexpected failures, the down time associated with such failures, the environmental overflows and, the increasing maintenance costs are major challenges all wastewater reticulation and distribution networks. Industries have been working hard to increase the availability of equipment and reduce the life-cycle cost without compromising safety and environmental targets. Risk-based maintenance (RBM) strategy is useful for allocation of maintenance resources where first allocation occurs to the highest risk item and progressively allocated till it reached budget limits. This paper is based on findings from a study covering 186 sewerage pumping stations of Townsville Water in North of Queensland in Australia. This study covered identifying the critical subsystems and mitigating the risks of failure of those subsystems. Implementation of risk based maintenance strategy was useful in further enhancing reliability and reduction of maintenance costs. © 2019 IEEE.
- Description: E1
- Authors: Masud, M. , Chattopadhyay, Gopi , Gunawan, Indra
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019 p. 455-458
- Full Text:
- Reviewed:
- Description: Industries have been facing ever-increasing challenges to do more with less under ongoing budget constraints. They are pushing the boundary by challenging the OEM recommended maintenance intervals and relaxing or tightening based on where it is needed. This is also evident in water sector where industries are trying to do targeted maintenance based on balancing costs, performances and risks. The unexpected failures, the down time associated with such failures, the environmental overflows and, the increasing maintenance costs are major challenges all wastewater reticulation and distribution networks. Industries have been working hard to increase the availability of equipment and reduce the life-cycle cost without compromising safety and environmental targets. Risk-based maintenance (RBM) strategy is useful for allocation of maintenance resources where first allocation occurs to the highest risk item and progressively allocated till it reached budget limits. This paper is based on findings from a study covering 186 sewerage pumping stations of Townsville Water in North of Queensland in Australia. This study covered identifying the critical subsystems and mitigating the risks of failure of those subsystems. Implementation of risk based maintenance strategy was useful in further enhancing reliability and reduction of maintenance costs. © 2019 IEEE.
- Description: E1
Generating linked data repositories using UML artifacts
- Authors: Khan, Aqsa , Malik, Saleem
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 1st Intelligent Technologies and Applications, Intap 2018; Bahawalpur, Pakistan; 23rd-25th October 2018; published in Communications in Computer and Information Science book Series Vol. 932, p. 142-152
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- Description: The usability of diagrams and models is increasing day by day, because of this we experience problem in searching and accessing from large size repositories of diagrams and models of software systems. This research might be helpful to search and access the diagrams and models in bigger repositories. For this purpose, this research developed linked data repositories which contain UML (Unified Modeling Language) artifacts, these artifacts are being organized with using UML class model. In particular, UML is being broadly applied to data modeling in many application domains, and generating linked data repositories from the UML class model is becoming a challenging task in the context of semantic web. This paper proposes an approach, in which we will build a construction tool by joining the characteristics of RDF (Resource Description Framework) and UML. Firstly we will formally define design artifacts and linked data repositories. After that we will propose a construction tool in which we will extract UML artifacts, these UML class model further transforms into the corresponding RDFs. The generated RDF linked data then will be verified by using W3C RDF, this is a validating service used to generate and verify the RDF triples and graphs. Finally, the proposed construction tool will be implemented with few experiments and research is validated using W3C RDF validating service. The proposed approach aims to give such a design that may facilitate the users to customize linked data repositories so that diagrams and models could be examined from large size data.
- Authors: Khan, Aqsa , Malik, Saleem
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 1st Intelligent Technologies and Applications, Intap 2018; Bahawalpur, Pakistan; 23rd-25th October 2018; published in Communications in Computer and Information Science book Series Vol. 932, p. 142-152
- Full Text:
- Reviewed:
- Description: The usability of diagrams and models is increasing day by day, because of this we experience problem in searching and accessing from large size repositories of diagrams and models of software systems. This research might be helpful to search and access the diagrams and models in bigger repositories. For this purpose, this research developed linked data repositories which contain UML (Unified Modeling Language) artifacts, these artifacts are being organized with using UML class model. In particular, UML is being broadly applied to data modeling in many application domains, and generating linked data repositories from the UML class model is becoming a challenging task in the context of semantic web. This paper proposes an approach, in which we will build a construction tool by joining the characteristics of RDF (Resource Description Framework) and UML. Firstly we will formally define design artifacts and linked data repositories. After that we will propose a construction tool in which we will extract UML artifacts, these UML class model further transforms into the corresponding RDFs. The generated RDF linked data then will be verified by using W3C RDF, this is a validating service used to generate and verify the RDF triples and graphs. Finally, the proposed construction tool will be implemented with few experiments and research is validated using W3C RDF validating service. The proposed approach aims to give such a design that may facilitate the users to customize linked data repositories so that diagrams and models could be examined from large size data.
Improved image analysis methodology for detecting changes in evidence positioning at crime scenes
- Petty, Mark, Teng, Shyh, Murshed, Manzur
- Authors: Petty, Mark , Teng, Shyh , Murshed, Manzur
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
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- Description: This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography. © 2019 IEEE.
- Description: E1
- Authors: Petty, Mark , Teng, Shyh , Murshed, Manzur
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
- Full Text:
- Reviewed:
- Description: This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography. © 2019 IEEE.
- Description: E1
Measuring soil strain using fibre optic sensors
- Costa, Susanga, Kahandawa, Gayan, Chen, Jian, Xue, Jianfeng
- Authors: Costa, Susanga , Kahandawa, Gayan , Chen, Jian , Xue, Jianfeng
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 8th International Congress on Environmental Geotechnics, ICEG 2018; Hangzhou, China; 28th October-1st November 2018; part of the Environmental Science and Engineering book series p. 43-50
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- Description: Monitoring subsurface soil movement is important in many geotechnical engineering applications such as stability of slopes, road embankments and settlement in foundations. Soil displacement measurement is also helpful in understanding the formation of shrinkage cracks. Clay soils undergo shrinkage during drying and experience substantial stresses and strains, which results in shrinkage cracks. This paper presents a novel approach to measure soil strain using Fibre Bragg grating (FBG) sensors. In the experiments described, FBG sensors have been used to investigate the strain development in clay during drying. FBG sensors are fabricated in the core region of specially fabricated single mode low-loss germanium doped silicate optical fibres. The grating is the laser-inscribed region with a periodically varying refractive index, which reflects a specific light wavelength. Due to the applied strain, ε, there is a change in the wavelength which can be measured and is directly proposal to strain. Kaolin clay, mixed with water close to the liquid limit, was allowed to dry under room temperature. The specimens were prepared in thin, long linear shrinkage moulds. FBG sensors were placed inside soil at the centre of the specimen. The strain development during drying underwent four phases moving from compression to tension. An oscillating nature of strain was also observed throughout the drying process. Results obtained are useful to develop analytical solutions to describe stress-strain behavior of drying soil. © Springer Nature Singapore Pte Ltd. 2019.
- Authors: Costa, Susanga , Kahandawa, Gayan , Chen, Jian , Xue, Jianfeng
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 8th International Congress on Environmental Geotechnics, ICEG 2018; Hangzhou, China; 28th October-1st November 2018; part of the Environmental Science and Engineering book series p. 43-50
- Full Text:
- Reviewed:
- Description: Monitoring subsurface soil movement is important in many geotechnical engineering applications such as stability of slopes, road embankments and settlement in foundations. Soil displacement measurement is also helpful in understanding the formation of shrinkage cracks. Clay soils undergo shrinkage during drying and experience substantial stresses and strains, which results in shrinkage cracks. This paper presents a novel approach to measure soil strain using Fibre Bragg grating (FBG) sensors. In the experiments described, FBG sensors have been used to investigate the strain development in clay during drying. FBG sensors are fabricated in the core region of specially fabricated single mode low-loss germanium doped silicate optical fibres. The grating is the laser-inscribed region with a periodically varying refractive index, which reflects a specific light wavelength. Due to the applied strain, ε, there is a change in the wavelength which can be measured and is directly proposal to strain. Kaolin clay, mixed with water close to the liquid limit, was allowed to dry under room temperature. The specimens were prepared in thin, long linear shrinkage moulds. FBG sensors were placed inside soil at the centre of the specimen. The strain development during drying underwent four phases moving from compression to tension. An oscillating nature of strain was also observed throughout the drying process. Results obtained are useful to develop analytical solutions to describe stress-strain behavior of drying soil. © Springer Nature Singapore Pte Ltd. 2019.
Multi-source cyber-attacks detection using machine learning
- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung
- Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
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- Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
- Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Patient-empowered electronic health records
- Sahama, Tony, Stranieri, Andrew, Butler-Henderson, Kerryn
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
- Full Text:
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
Privacy and Security of Connected Vehicles in Intelligent Transportation System
- Jolfaei, Alireza, Kant, Krishna
- Authors: Jolfaei, Alireza , Kant, Krishna
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019, Portland, United States; 24-27 June 2019. p. 9-10
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- Description: The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data. © 2019 IEEE.
- Description: E1
- Authors: Jolfaei, Alireza , Kant, Krishna
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019, Portland, United States; 24-27 June 2019. p. 9-10
- Full Text:
- Reviewed:
- Description: The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data. © 2019 IEEE.
- Description: E1