6G wireless systems : a vision, architectural elements, and future directions
- Khan, Latif, Yaqoob, Ibrar, Imran, Muhammad, Han, Zhu, Hong, Choong
- Authors: Khan, Latif , Yaqoob, Ibrar , Imran, Muhammad , Han, Zhu , Hong, Choong
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147029-147044
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- Description: Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions. © 2013 IEEE.
- Authors: Khan, Latif , Yaqoob, Ibrar , Imran, Muhammad , Han, Zhu , Hong, Choong
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147029-147044
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- Description: Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions. © 2013 IEEE.
A blockchain-based deep-learning-driven architecture for quality routing in wireless sensor networks
- Khan, Zahoor, Amjad, Sana, Ahmed, Farwa, Almasoud, Abdullah, Imran, Muhammad, Javaid, Nadeem
- Authors: Khan, Zahoor , Amjad, Sana , Ahmed, Farwa , Almasoud, Abdullah , Imran, Muhammad , Javaid, Nadeem
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Access Vol. 11, no. (2023), p. 31036-31051
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- Description: Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities. © 2013 IEEE.
A blockchain-based deep-learning-driven architecture for quality routing in wireless sensor networks
- Authors: Khan, Zahoor , Amjad, Sana , Ahmed, Farwa , Almasoud, Abdullah , Imran, Muhammad , Javaid, Nadeem
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Access Vol. 11, no. (2023), p. 31036-31051
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- Description: Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities. © 2013 IEEE.
A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems
- Al-Muhtadi, Jalal, Qiang, Ma, Zeb, Khan, Chaudhry, Junaid, Imran, Muhammad
- Authors: Al-Muhtadi, Jalal , Qiang, Ma , Zeb, Khan , Chaudhry, Junaid , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 16363-16376
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- Description: Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
- Authors: Al-Muhtadi, Jalal , Qiang, Ma , Zeb, Khan , Chaudhry, Junaid , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 16363-16376
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- Description: Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
A deep learning model based on concatenation approach for the diagnosis of brain tumor
- Noreen, Neelum, Palaniappan, Sellappan, Qayyum, Abdul, Ahmad, Iftikhar, Imran, Muhammad, Shoaib, M.uhammad
- Authors: Noreen, Neelum , Palaniappan, Sellappan , Qayyum, Abdul , Ahmad, Iftikhar , Imran, Muhammad , Shoaib, M.uhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 55135-55144
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- Description: Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. © 2013 IEEE.
- Authors: Noreen, Neelum , Palaniappan, Sellappan , Qayyum, Abdul , Ahmad, Iftikhar , Imran, Muhammad , Shoaib, M.uhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 55135-55144
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- Description: Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. © 2013 IEEE.
A framework and mathematical modeling for the vehicular delay tolerant network routing
- Nasir, Mostofa, Noor, Rafidah, Iftikhar, Mohsin, Imran, Muhammad, Abdul Wahab, Ainuddin, Jabbarpour, Mohammad, Khokhar, R.
- Authors: Nasir, Mostofa , Noor, Rafidah , Iftikhar, Mohsin , Imran, Muhammad , Abdul Wahab, Ainuddin , Jabbarpour, Mohammad , Khokhar, R.
- Date: 2016
- Type: Text , Journal article
- Relation: Mobile Information Systems Vol. 2016, no. (2016), p.
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- Description: Vehicular ad hoc networks (VANETs) are getting growing interest as they are expected to play crucial role in making safer, smarter, and more efficient transportation networks. Due to unique characteristics such as sparse topology and intermittent connectivity, Delay Tolerant Network (DTN) routing in VANET becomes an inherent choice and is challenging. However, most of the existing DTN protocols do not accurately discover potential neighbors and, hence, appropriate intermediate nodes for packet transmission. Moreover, these protocols cause unnecessary overhead due to excessive beacon messages. To cope with these challenges, this paper presents a novel framework and an Adaptive Geographical DTN Routing (AGDR) for vehicular DTNs. AGDR exploits node position, current direction, speed, and the predicted direction to carefully select an appropriate intermediate node. Direction indicator light is employed to accurately predict the vehicle future direction so that the forwarding node can relay packets to the desired destination. Simulation experiments confirm the performance supremacy of AGDR compared to contemporary schemes in terms of packet delivery ratio, overhead, and end-to-end delay. Simulation results demonstrate that AGDR improves the packet delivery ratio (5-7%), reduces the overhead (1-5%), and decreases the delay (up to 0.02 ms). Therefore, AGDR improves route stability by reducing the frequency of route failures. © 2016 Mostofa Kamal Nasir et al.
- Authors: Nasir, Mostofa , Noor, Rafidah , Iftikhar, Mohsin , Imran, Muhammad , Abdul Wahab, Ainuddin , Jabbarpour, Mohammad , Khokhar, R.
- Date: 2016
- Type: Text , Journal article
- Relation: Mobile Information Systems Vol. 2016, no. (2016), p.
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- Description: Vehicular ad hoc networks (VANETs) are getting growing interest as they are expected to play crucial role in making safer, smarter, and more efficient transportation networks. Due to unique characteristics such as sparse topology and intermittent connectivity, Delay Tolerant Network (DTN) routing in VANET becomes an inherent choice and is challenging. However, most of the existing DTN protocols do not accurately discover potential neighbors and, hence, appropriate intermediate nodes for packet transmission. Moreover, these protocols cause unnecessary overhead due to excessive beacon messages. To cope with these challenges, this paper presents a novel framework and an Adaptive Geographical DTN Routing (AGDR) for vehicular DTNs. AGDR exploits node position, current direction, speed, and the predicted direction to carefully select an appropriate intermediate node. Direction indicator light is employed to accurately predict the vehicle future direction so that the forwarding node can relay packets to the desired destination. Simulation experiments confirm the performance supremacy of AGDR compared to contemporary schemes in terms of packet delivery ratio, overhead, and end-to-end delay. Simulation results demonstrate that AGDR improves the packet delivery ratio (5-7%), reduces the overhead (1-5%), and decreases the delay (up to 0.02 ms). Therefore, AGDR improves route stability by reducing the frequency of route failures. © 2016 Mostofa Kamal Nasir et al.
A multi-hop angular routing protocol for wireless sensor networks
- Akbar, Mariam, Javaid, Nadeem, Imran, Muhammad, Rao, Areeba, Younis, Muhammad, Niaz, Iftikhar
- Authors: Akbar, Mariam , Javaid, Nadeem , Imran, Muhammad , Rao, Areeba , Younis, Muhammad , Niaz, Iftikhar
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Distributed Sensor Networks Vol. 12, no. 9 (2016), p.
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- Description: In this article, we propose two new routing protocols for wireless sensor networks. First one is AM-DisCNT (angular multi-hop distance-based clustering network transmission) protocol which uses circular deployment of sensors (nodes) for uniform energy consumption in the network. The protocol operates in such a way that nodes with maximum residual energy are selected as cluster heads for each round. Second one is iAM-DisCNT (improved AM-DisCNT) protocol which exploits both mobile and static base stations for throughput maximization. Besides the proposition of routing protocols, iAM-DisCNT is provided with three mathematical models: two linear-programming-based models for information flow maximization and packet drop rate minimization and one model for calculating energy consumption of nodes. Graphical analysis for linear-programming-based mathematical formulation is also part of this work. Simulation results show that AM-DisCNT has 32% and iAM-DisCNT has 48% improved stability period as compared to LEACH (low-energy adaptive clustering hierarchy) and DEEC (distributed energy-efficient clustering) routing protocols. Similarly, throughput of AM-DisCNT and iAM-DisCNT is improved by 16% and 80%, respectively, in comparison with the counterpart schemes. © The Author(s) 2016.
- Authors: Akbar, Mariam , Javaid, Nadeem , Imran, Muhammad , Rao, Areeba , Younis, Muhammad , Niaz, Iftikhar
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Distributed Sensor Networks Vol. 12, no. 9 (2016), p.
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- Description: In this article, we propose two new routing protocols for wireless sensor networks. First one is AM-DisCNT (angular multi-hop distance-based clustering network transmission) protocol which uses circular deployment of sensors (nodes) for uniform energy consumption in the network. The protocol operates in such a way that nodes with maximum residual energy are selected as cluster heads for each round. Second one is iAM-DisCNT (improved AM-DisCNT) protocol which exploits both mobile and static base stations for throughput maximization. Besides the proposition of routing protocols, iAM-DisCNT is provided with three mathematical models: two linear-programming-based models for information flow maximization and packet drop rate minimization and one model for calculating energy consumption of nodes. Graphical analysis for linear-programming-based mathematical formulation is also part of this work. Simulation results show that AM-DisCNT has 32% and iAM-DisCNT has 48% improved stability period as compared to LEACH (low-energy adaptive clustering hierarchy) and DEEC (distributed energy-efficient clustering) routing protocols. Similarly, throughput of AM-DisCNT and iAM-DisCNT is improved by 16% and 80%, respectively, in comparison with the counterpart schemes. © The Author(s) 2016.
A multivariant stream analysis approach to detect and mitigate DDoS attacks in vehicular ad hoc networks
- Kolandaisamy, Raenu, Md Noor, Rafidah, Ahmedy, Ismail, Ahmad, Iftikhar, Imran, Muhammad
- Authors: Kolandaisamy, Raenu , Md Noor, Rafidah , Ahmedy, Ismail , Ahmad, Iftikhar , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Wireless Communications and Mobile Computing Vol. 2018, no. (2018), p.
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- Description: Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle-to-Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication. © 2018 Raenu Kolandaisamy et al. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
- Authors: Kolandaisamy, Raenu , Md Noor, Rafidah , Ahmedy, Ismail , Ahmad, Iftikhar , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Wireless Communications and Mobile Computing Vol. 2018, no. (2018), p.
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- Description: Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle-to-Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication. © 2018 Raenu Kolandaisamy et al. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
A novel collaborative IoD-assisted VANET approach for coverage area maximization
- Ahmed, Gamil, Sheltami, Tarek, Mahmoud, Ashraf, Imran, Muhammad, Shoaib, Muhammad
- Authors: Ahmed, Gamil , Sheltami, Tarek , Mahmoud, Ashraf , Imran, Muhammad , Shoaib, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 61211-61223
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- Description: Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular ad-hoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes. © 2013 IEEE.
- Authors: Ahmed, Gamil , Sheltami, Tarek , Mahmoud, Ashraf , Imran, Muhammad , Shoaib, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 61211-61223
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- Description: Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular ad-hoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes. © 2013 IEEE.
A privacy-preserving framework for smart context-aware healthcare applications
- Azad, Muhammad, Arshad, Junaid, Mahmoud, Shazia, Salah, Khaled, Imran, Muhammad
- Authors: Azad, Muhammad , Arshad, Junaid , Mahmoud, Shazia , Salah, Khaled , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Transactions on Emerging Telecommunications Technologies Vol. 33, no. 8 (2022), p.
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- Description: Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications. © 2019 John Wiley & Sons, Ltd.
- Authors: Azad, Muhammad , Arshad, Junaid , Mahmoud, Shazia , Salah, Khaled , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Transactions on Emerging Telecommunications Technologies Vol. 33, no. 8 (2022), p.
- Full Text:
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- Description: Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications. © 2019 John Wiley & Sons, Ltd.
A quantitative risk assessment model involving frequency and threat degree under line-of-business services for infrastructure of emerging sensor networks
- Jing, Xu, Hu, Hanwen, Yang, Huijun, Au, Man, Li, Shuqin, Xiong, Naixue, Imran, Muhammad, Vasilakos, Athanasios
- Authors: Jing, Xu , Hu, Hanwen , Yang, Huijun , Au, Man , Li, Shuqin , Xiong, Naixue , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 3 (2017), p.
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- Description: The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Jing, Xu , Hu, Hanwen , Yang, Huijun , Au, Man , Li, Shuqin , Xiong, Naixue , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 3 (2017), p.
- Full Text:
- Reviewed:
- Description: The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
A robust consistency model of crowd workers in text labeling tasks
- Alqershi, Fattoh, Al-Qurishi, Muhammad, Aksoy, Mehmet, Alrubaian, Majed, Imran, Muhammad
- Authors: Alqershi, Fattoh , Al-Qurishi, Muhammad , Aksoy, Mehmet , Alrubaian, Majed , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 168381-168393
- Full Text:
- Reviewed:
- Description: Crowdsourcing is a popular human-based model to acquire labeled data. Despite its ability to generate huge amounts of labelled data at moderate costs, it is susceptible to low quality labels. This can happen through unintentional or intentional errors by the crowd workers. Consistency is an important attribute of reliability. It is a practical metric that evaluates a crowd workers' reliability based on their ability to conform to themselves by yielding the same output when repeatedly given a particular input. Consistency has not yet been sufficiently explored in the literature. In this work, we propose a novel consistency model based on the pairwise comparisons method. We apply this model on unpaid workers. We measure the workers' consistency on tasks of labeling political text-based claims and study the effects of different duplicate task characteristics on their consistency. Our results show that the proposed model outperforms the current state-of-the-art models in terms of accuracy. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
- Authors: Alqershi, Fattoh , Al-Qurishi, Muhammad , Aksoy, Mehmet , Alrubaian, Majed , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 168381-168393
- Full Text:
- Reviewed:
- Description: Crowdsourcing is a popular human-based model to acquire labeled data. Despite its ability to generate huge amounts of labelled data at moderate costs, it is susceptible to low quality labels. This can happen through unintentional or intentional errors by the crowd workers. Consistency is an important attribute of reliability. It is a practical metric that evaluates a crowd workers' reliability based on their ability to conform to themselves by yielding the same output when repeatedly given a particular input. Consistency has not yet been sufficiently explored in the literature. In this work, we propose a novel consistency model based on the pairwise comparisons method. We apply this model on unpaid workers. We measure the workers' consistency on tasks of labeling political text-based claims and study the effects of different duplicate task characteristics on their consistency. Our results show that the proposed model outperforms the current state-of-the-art models in terms of accuracy. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing
- Nasser, Nidal, Emad-ul-Haq, Qazi, Imran, Muhammad, Ali, Asmaa, Razzak, Imran, Al-Helali, Abdulaziz
- Authors: Nasser, Nidal , Emad-ul-Haq, Qazi , Imran, Muhammad , Ali, Asmaa , Razzak, Imran , Al-Helali, Abdulaziz
- Date: 2023
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 35, no. 19 (2023), p. 13775-13789
- Full Text:
- Reviewed:
- Description: Coronavirus (COVID-19) is a very contagious infection that has drawn the world’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data’s intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system’s robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Nasser, Nidal , Emad-ul-Haq, Qazi , Imran, Muhammad , Ali, Asmaa , Razzak, Imran , Al-Helali, Abdulaziz
- Date: 2023
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 35, no. 19 (2023), p. 13775-13789
- Full Text:
- Reviewed:
- Description: Coronavirus (COVID-19) is a very contagious infection that has drawn the world’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data’s intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system’s robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
A zero-watermarking algorithm for privacy protection in biomedical signals
- Ali, Zulfiqar, Imran, Muhammad, Alsulaiman, Mansour, Zia, Tanveer, Shoaib, Muhammad
- Authors: Ali, Zulfiqar , Imran, Muhammad , Alsulaiman, Mansour , Zia, Tanveer , Shoaib, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 82, no. (2018), p. 290-303
- Full Text:
- Reviewed:
- Description: Confidentiality of health information is indispensable to protect privacy of an individual. However, recent advances in electronic healthcare systems allow transmission of sensitive information through the Internet, which is prone to various vulnerabilities, attacks and may leads to unauthorized disclosure. Such situations may not only create adverse effects for individuals but may also cause severe consequences such as hefty regulatory fines, bad publicity, legal fees, and forensics. To avoid such predicaments, a privacy protected healthcare system is proposed in this study that protects the identity of an individual as well as detects vocal fold disorders. The privacy of the developed healthcare system is based on the proposed zero-watermarking algorithm, which embeds a watermark in a secret key instead of the signals to avoid the distortion in an audio sample. The identity is protected by the generation of its secret shares through visual cryptography. The generated shares are embedded by finding the patterns into the audio with the application of one-dimensional local binary pattern. The proposed zero-watermarking algorithm is evaluated by using audio samples taken from the Massachusetts Eye and Ear Infirmary voice disorder database. Experimental results demonstrate that the proposed algorithm achieves imperceptibility and is reliable in its extraction of identity. In addition, the proposed algorithm does not affect the results of disorder detection and it is robust against noise attacks of various signal-to-noise ratios. © 2017 Elsevier B.V.
- Authors: Ali, Zulfiqar , Imran, Muhammad , Alsulaiman, Mansour , Zia, Tanveer , Shoaib, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 82, no. (2018), p. 290-303
- Full Text:
- Reviewed:
- Description: Confidentiality of health information is indispensable to protect privacy of an individual. However, recent advances in electronic healthcare systems allow transmission of sensitive information through the Internet, which is prone to various vulnerabilities, attacks and may leads to unauthorized disclosure. Such situations may not only create adverse effects for individuals but may also cause severe consequences such as hefty regulatory fines, bad publicity, legal fees, and forensics. To avoid such predicaments, a privacy protected healthcare system is proposed in this study that protects the identity of an individual as well as detects vocal fold disorders. The privacy of the developed healthcare system is based on the proposed zero-watermarking algorithm, which embeds a watermark in a secret key instead of the signals to avoid the distortion in an audio sample. The identity is protected by the generation of its secret shares through visual cryptography. The generated shares are embedded by finding the patterns into the audio with the application of one-dimensional local binary pattern. The proposed zero-watermarking algorithm is evaluated by using audio samples taken from the Massachusetts Eye and Ear Infirmary voice disorder database. Experimental results demonstrate that the proposed algorithm achieves imperceptibility and is reliable in its extraction of identity. In addition, the proposed algorithm does not affect the results of disorder detection and it is robust against noise attacks of various signal-to-noise ratios. © 2017 Elsevier B.V.
An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers
- Naseem, Usman, Rashid, Junaid, Ali, Liaqat, Kim, Jungeun, Haq, Qazi, Awan, Mazhar, Imran, Muhammad
- Authors: Naseem, Usman , Rashid, Junaid , Ali, Liaqat , Kim, Jungeun , Haq, Qazi , Awan, Mazhar , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78242-78252
- Full Text:
- Reviewed:
- Description: Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods. © 2013 IEEE.
- Authors: Naseem, Usman , Rashid, Junaid , Ali, Liaqat , Kim, Jungeun , Haq, Qazi , Awan, Mazhar , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78242-78252
- Full Text:
- Reviewed:
- Description: Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods. © 2013 IEEE.
An automatic digital audio authentication/forensics system
- Ali, Zulfiqar, Imran, Muhammad, Alsulaiman, Mansour
- Authors: Ali, Zulfiqar , Imran, Muhammad , Alsulaiman, Mansour
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 2994-3007
- Full Text:
- Reviewed:
- Description: With the continuous rise in ingenious forgery, a wide range of digital audio authentication applications are emerging as a preventive and detective control in real-world circumstances, such as forged evidence, breach of copyright protection, and unauthorized data access. To investigate and verify, this paper presents a novel automatic authentication system that differentiates between the forged and original audio. The design philosophy of the proposed system is primarily based on three psychoacoustic principles of hearing, which are implemented to simulate the human sound perception system. Moreover, the proposed system is able to classify between the audio of different environments recorded with the same microphone. To authenticate the audio and environment classification, the computed features based on the psychoacoustic principles of hearing are dangled to the Gaussian mixture model to make automatic decisions. It is worth mentioning that the proposed system authenticates an unknown speaker irrespective of the audio content i.e., independent of narrator and text. To evaluate the performance of the proposed system, audios in multi-environments are forged in such a way that a human cannot recognize them. Subjective evaluation by three human evaluators is performed to verify the quality of the generated forged audio. The proposed system provides a classification accuracy of 99.2% ± 2.6. Furthermore, the obtained accuracy for the other scenarios, such as text-dependent and text-independent audio authentication, is 100% by using the proposed system. © 2017 IEEE.
- Authors: Ali, Zulfiqar , Imran, Muhammad , Alsulaiman, Mansour
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 2994-3007
- Full Text:
- Reviewed:
- Description: With the continuous rise in ingenious forgery, a wide range of digital audio authentication applications are emerging as a preventive and detective control in real-world circumstances, such as forged evidence, breach of copyright protection, and unauthorized data access. To investigate and verify, this paper presents a novel automatic authentication system that differentiates between the forged and original audio. The design philosophy of the proposed system is primarily based on three psychoacoustic principles of hearing, which are implemented to simulate the human sound perception system. Moreover, the proposed system is able to classify between the audio of different environments recorded with the same microphone. To authenticate the audio and environment classification, the computed features based on the psychoacoustic principles of hearing are dangled to the Gaussian mixture model to make automatic decisions. It is worth mentioning that the proposed system authenticates an unknown speaker irrespective of the audio content i.e., independent of narrator and text. To evaluate the performance of the proposed system, audios in multi-environments are forged in such a way that a human cannot recognize them. Subjective evaluation by three human evaluators is performed to verify the quality of the generated forged audio. The proposed system provides a classification accuracy of 99.2% ± 2.6. Furthermore, the obtained accuracy for the other scenarios, such as text-dependent and text-independent audio authentication, is 100% by using the proposed system. © 2017 IEEE.
An effective data-collection scheme with AUV path planning in underwater wireless sensor networks
- Khan, Wahab, Hua, Wang, Anwar, Muhammad, Alharbi, Abdullah, Imran, Muhammad, Khan, Javed
- Authors: Khan, Wahab , Hua, Wang , Anwar, Muhammad , Alharbi, Abdullah , Imran, Muhammad , Khan, Javed
- Date: 2022
- Type: Text , Journal article
- Relation: Wireless Communications and Mobile Computing Vol. 2022, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Data collection in underwater wireless sensor networks (UWSNs) using autonomous underwater vehicles (AUVs) is a more robust solution than traditional approaches, instead of transmitting data from each node to a destination node. However, the design of delay-aware and energy-efficient path planning for AUVs is one of the most crucial problems in collecting data for UWSNs. To reduce network delay and increase network lifetime, we proposed a novel reliable AUV-based data-collection routing protocol for UWSNs. The proposed protocol employs a route planning mechanism to collect data using AUVs. The sink node directs AUVs for data collection from sensor nodes to reduce energy consumption. First, sensor nodes are organized into clusters for better scalability, and then, these clusters are arranged into groups to assign an AUV to each group. Second, the traveling path for each AUV is crafted based on the Markov decision process (MDP) for the reliable collection of data. The simulation results affirm the effectiveness and efficiency of the proposed technique in terms of throughput, energy efficiency, delay, and reliability. © 2022 Wahab Khan et al.
- Authors: Khan, Wahab , Hua, Wang , Anwar, Muhammad , Alharbi, Abdullah , Imran, Muhammad , Khan, Javed
- Date: 2022
- Type: Text , Journal article
- Relation: Wireless Communications and Mobile Computing Vol. 2022, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Data collection in underwater wireless sensor networks (UWSNs) using autonomous underwater vehicles (AUVs) is a more robust solution than traditional approaches, instead of transmitting data from each node to a destination node. However, the design of delay-aware and energy-efficient path planning for AUVs is one of the most crucial problems in collecting data for UWSNs. To reduce network delay and increase network lifetime, we proposed a novel reliable AUV-based data-collection routing protocol for UWSNs. The proposed protocol employs a route planning mechanism to collect data using AUVs. The sink node directs AUVs for data collection from sensor nodes to reduce energy consumption. First, sensor nodes are organized into clusters for better scalability, and then, these clusters are arranged into groups to assign an AUV to each group. Second, the traveling path for each AUV is crafted based on the Markov decision process (MDP) for the reliable collection of data. The simulation results affirm the effectiveness and efficiency of the proposed technique in terms of throughput, energy efficiency, delay, and reliability. © 2022 Wahab Khan et al.
An effective solution to the optimal power flow problem using meta-heuristic algorithms
- Aurangzeb, Khursheed, Shafiq, Sundas, Alhussein, Musaed, Pamir, Javaid, Nadeem, Imran, Muhammad
- Authors: Aurangzeb, Khursheed , Shafiq, Sundas , Alhussein, Musaed , Pamir , Javaid, Nadeem , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Frontiers in Energy Research Vol. 11, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Financial loss in power systems is an emerging problem that needs to be resolved. To tackle the mentioned problem, energy generated from various generation sources in the power network needs proper scheduling. In order to determine the best settings for the control variables, this study formulates and solves an optimal power flow (OPF) problem. In the proposed work, the bird swarm algorithm (BSA), JAYA, and a hybrid of both algorithms, termed as HJBSA, are used for obtaining the settings of optimum variables. We perform simulations by considering the constraints of voltage stability and line capacity, and generated reactive and active power. In addition, the used algorithms solve the problem of OPF and minimize carbon emission generated from thermal systems, fuel cost, voltage deviations, and losses in generation of active power. The suggested approach is evaluated by putting it into use on two separate IEEE testing systems, one with 30 buses and the other with 57 buses. The simulation results show that for the 30-bus system, the minimization in cost by HJBSA, JAYA, and BSA is 860.54 $/h, 862.31, $/h and 900.01 $/h, respectively, while for the 57-bus system, it is 5506.9 $/h, 6237.4, $/h and 7245.6 $/h for HJBSA, JAYA, and BSA, respectively. Similarly, for the 30-bus system, the power loss by HJBSA, JAYA, and BSA is 9.542 MW, 10.102 MW, and 11.427 MW, respectively, while for the 57-bus system, the value of power loss is 13.473 MW, 20.552, MW and 18.638 MW for HJBSA, JAYA, and BSA, respectively. Moreover, HJBSA, JAYA, and BSA cause reduction in carbon emissions by 4.394 ton/h, 4.524, ton/h and 4.401 ton/h, respectively, with the 30-bus system. With the 57-bus system, HJBSA, JAYA, and BSA cause reduction in carbon emissions by 26.429 ton/h, 27.014, ton/h and 28.568 ton/h, respectively. The results show the outperformance of HJBSA. Copyright © 2023 Aurangzeb, Shafiq, Alhussein, Pamir, Javaid and Imran.
- Authors: Aurangzeb, Khursheed , Shafiq, Sundas , Alhussein, Musaed , Pamir , Javaid, Nadeem , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Frontiers in Energy Research Vol. 11, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Financial loss in power systems is an emerging problem that needs to be resolved. To tackle the mentioned problem, energy generated from various generation sources in the power network needs proper scheduling. In order to determine the best settings for the control variables, this study formulates and solves an optimal power flow (OPF) problem. In the proposed work, the bird swarm algorithm (BSA), JAYA, and a hybrid of both algorithms, termed as HJBSA, are used for obtaining the settings of optimum variables. We perform simulations by considering the constraints of voltage stability and line capacity, and generated reactive and active power. In addition, the used algorithms solve the problem of OPF and minimize carbon emission generated from thermal systems, fuel cost, voltage deviations, and losses in generation of active power. The suggested approach is evaluated by putting it into use on two separate IEEE testing systems, one with 30 buses and the other with 57 buses. The simulation results show that for the 30-bus system, the minimization in cost by HJBSA, JAYA, and BSA is 860.54 $/h, 862.31, $/h and 900.01 $/h, respectively, while for the 57-bus system, it is 5506.9 $/h, 6237.4, $/h and 7245.6 $/h for HJBSA, JAYA, and BSA, respectively. Similarly, for the 30-bus system, the power loss by HJBSA, JAYA, and BSA is 9.542 MW, 10.102 MW, and 11.427 MW, respectively, while for the 57-bus system, the value of power loss is 13.473 MW, 20.552, MW and 18.638 MW for HJBSA, JAYA, and BSA, respectively. Moreover, HJBSA, JAYA, and BSA cause reduction in carbon emissions by 4.394 ton/h, 4.524, ton/h and 4.401 ton/h, respectively, with the 30-bus system. With the 57-bus system, HJBSA, JAYA, and BSA cause reduction in carbon emissions by 26.429 ton/h, 27.014, ton/h and 28.568 ton/h, respectively. The results show the outperformance of HJBSA. Copyright © 2023 Aurangzeb, Shafiq, Alhussein, Pamir, Javaid and Imran.
An efficient network intrusion detection and classification system
- Ahmad, Iftikhar, Haq, Qazi, Imran, Muhammad, Alassafi, Madini, Alghamdi, Rayed
- Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
An investigation of the drivers of social commerce and e-word-of-mouth intentions : elucidating the role of social commerce in e-business
- Goraya, M. Awals, Jing, Zhu, Shareef, Mahmud, Imran, Muhammad, Malik, Aneela, Akram, M. Shakaib
- Authors: Goraya, M. Awals , Jing, Zhu , Shareef, Mahmud , Imran, Muhammad , Malik, Aneela , Akram, M. Shakaib
- Date: 2021
- Type: Text , Journal article
- Relation: Electronic Markets Vol. 31, no. 1 (2021), p. 181-195
- Full Text:
- Reviewed:
- Description: Building on social commerce (s-commerce) perspectives and the trust transfer theory, this study develops a theoretical model that explains the indirect effects of two types of s-commerce attributes (community and platform) on behavioral outcomes (s-commerce intentions and e-Word-of-Mouth (e-WOM) intentions) through trust in community and platform. We analyze data collected from s-commerce users on travel booking websites using structural equation modeling technique. Results confirm that s-commerce intentions and e-WOM intentions are contingent upon s-commerce community and platform attributes. Moreover, the results provide evidence for the mediating effects of trust in community and platform on the relationship between s-commerce attributes and behavioral outcomes. The study provides further insights about the impact of s-commerce experience on s-commerce intention and e-WOM intention. Moreover, this study contributes to s-commerce research and practice by developing and validating the role of s-commerce community and platform attributes in forming consumers’ s-commerce behavioral outcomes. © 2019, Institute of Applied Informatics at University of Leipzig.
- Authors: Goraya, M. Awals , Jing, Zhu , Shareef, Mahmud , Imran, Muhammad , Malik, Aneela , Akram, M. Shakaib
- Date: 2021
- Type: Text , Journal article
- Relation: Electronic Markets Vol. 31, no. 1 (2021), p. 181-195
- Full Text:
- Reviewed:
- Description: Building on social commerce (s-commerce) perspectives and the trust transfer theory, this study develops a theoretical model that explains the indirect effects of two types of s-commerce attributes (community and platform) on behavioral outcomes (s-commerce intentions and e-Word-of-Mouth (e-WOM) intentions) through trust in community and platform. We analyze data collected from s-commerce users on travel booking websites using structural equation modeling technique. Results confirm that s-commerce intentions and e-WOM intentions are contingent upon s-commerce community and platform attributes. Moreover, the results provide evidence for the mediating effects of trust in community and platform on the relationship between s-commerce attributes and behavioral outcomes. The study provides further insights about the impact of s-commerce experience on s-commerce intention and e-WOM intention. Moreover, this study contributes to s-commerce research and practice by developing and validating the role of s-commerce community and platform attributes in forming consumers’ s-commerce behavioral outcomes. © 2019, Institute of Applied Informatics at University of Leipzig.
An IoT-based smart healthcare system to detect dysphonia
- Ali, Zulfiqar, Imran, Muhammad, Shoaib, Muhammad
- Authors: Ali, Zulfiqar , Imran, Muhammad , Shoaib, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 14 (2022), p. 11255-11265
- Full Text:
- Reviewed:
- Description: Smart healthcare systems for the internet of things (IoT) platform are cost-efficient and facilitate continuous remote monitoring of patients to avoid unnecessary hospital visits and long waiting times to see practitioners. Presenting a smart healthcare system for the detection of dysphonia can reduce the suffering and pain of patients by providing an initial evaluation of voice. This preliminary feedback of voice could minimize the burden on ENT specialists by referring only genuine cases to them as well as giving an early alarm of potential voice complications to patients. Any possible delay in the treatment and/or inaccurate diagnosis using the subjective nature of tools may lead to severe circumstances for an individual because some types of dysphonia are life-threatening. Therefore, an accurate and reliable smart healthcare system for IoT platform to detect dysphonia is proposed and implemented in this study. Higher-order directional derivatives are used to analyze the time–frequency spectrum of signals in the proposed system. The computed derivatives provide essential and vital information by analyzing the spectrum along different directions to capture the changes that appeared due to malfunctioning the vocal folds. The proposed system provides 99.1% accuracy, while the sensitivity and specificity are 99.4 and 98.1%, respectively. The experimental results showed that the proposed system could provide better classification accuracy than the traditional non-directional first-order derivatives. Hence, the system can be used as a reliable tool for detecting dysphonia and implemented in edge devices to avoid latency issues and protect privacy, unlike cloud processing. © 2021, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Ali, Zulfiqar , Imran, Muhammad , Shoaib, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 14 (2022), p. 11255-11265
- Full Text:
- Reviewed:
- Description: Smart healthcare systems for the internet of things (IoT) platform are cost-efficient and facilitate continuous remote monitoring of patients to avoid unnecessary hospital visits and long waiting times to see practitioners. Presenting a smart healthcare system for the detection of dysphonia can reduce the suffering and pain of patients by providing an initial evaluation of voice. This preliminary feedback of voice could minimize the burden on ENT specialists by referring only genuine cases to them as well as giving an early alarm of potential voice complications to patients. Any possible delay in the treatment and/or inaccurate diagnosis using the subjective nature of tools may lead to severe circumstances for an individual because some types of dysphonia are life-threatening. Therefore, an accurate and reliable smart healthcare system for IoT platform to detect dysphonia is proposed and implemented in this study. Higher-order directional derivatives are used to analyze the time–frequency spectrum of signals in the proposed system. The computed derivatives provide essential and vital information by analyzing the spectrum along different directions to capture the changes that appeared due to malfunctioning the vocal folds. The proposed system provides 99.1% accuracy, while the sensitivity and specificity are 99.4 and 98.1%, respectively. The experimental results showed that the proposed system could provide better classification accuracy than the traditional non-directional first-order derivatives. Hence, the system can be used as a reliable tool for detecting dysphonia and implemented in edge devices to avoid latency issues and protect privacy, unlike cloud processing. © 2021, Springer-Verlag London Ltd., part of Springer Nature.