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Dr. Muhammad Shoaib
King saud university, college of computer and information sciences

Basic Info


Research Keywords & Expertise

0 Cloud Computing
0 ICT
0 Internet of Things
0 Video Coding
0 Mobile and wireless networks

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Short Biography

Muhammad Shoaib received his Ph.D. (2010) in communication and information systems from Beijing University of Posts and Telecommunications, China. He received his M.Eng. (2005) and B.Eng. (1995) from NED University of Engineering and Technology, Pakistan. His research areas include compression and error resilience in video coding, multimedia framework in eHealth application, communication and information security, cloud computing, IoT, and mobile and wireless networks. He has published 40+ research articles in peer-reviewed, well-recognized international conferences and journals. Many of his research articles are among the highly cited and most downloaded. He worked as a Senior Manager (IP Operations) in Pakistan Telecommunication Company Limited, Pakistan, between 2001-2011. He has also worked as a Maintenance Engineer in R. M. International, between 1996-2001. Currently, he is working as an Assistant Professor in the College of Computer and Information Sciences at King Saud University, Saudi Arabia.

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Journal article
Published: 31 May 2021 in Transportation Research Part A: Policy and Practice
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Most of Flying Adhoc Networks (FANETs) applications consume GPS based location information for their services, which are also shared in real-time with other UAVs, ground control stations and centralized service operators. GPS spoofing is among the most popular attacks in FANETs that lead the Global Navigation Satellite System (GNSS) receivers to generate false navigation solutions. Several anti-spoofing techniques have been proposed in the literature. However, Conventional detection methods present vulnerabilities against Colluding GPS Spoofing Attack where multiple GPS spoofing signal sources are used. In this paper, we propose a policy-based distributed detection mechanism to face colluding GPS-Spoofing attack in FANETs. Based on Burglary scene and the location of each UAV at the time of the attack, we can distinguish between Active and Passive witnesses that are respectively used to help the target to detect and confirm the presence of GPS spoofing signal using respectively Absolute power and Carrier-to-Noise density ratio. A trust model based on Beta and Weibull Distribution has been adopted as a combination technique of different testimonies to both mitigate the spread of false rumors and classify the different signals. Simulation results depict that our proposal is able to detect and revoke the GPS Spoofing signal with high accuracy reaching the 99% and low communication overhead.

ACS Style

Mousaab Bada; Djallel Eddine Boubiche; Nasreddine Lagraa; Chaker Abdelaziz Kerrache; Muhammad Imran; Muhammad Shoaib. A policy-based solution for the detection of colluding GPS-Spoofing attacks in FANETs. Transportation Research Part A: Policy and Practice 2021, 149, 300 -318.

AMA Style

Mousaab Bada, Djallel Eddine Boubiche, Nasreddine Lagraa, Chaker Abdelaziz Kerrache, Muhammad Imran, Muhammad Shoaib. A policy-based solution for the detection of colluding GPS-Spoofing attacks in FANETs. Transportation Research Part A: Policy and Practice. 2021; 149 ():300-318.

Chicago/Turabian Style

Mousaab Bada; Djallel Eddine Boubiche; Nasreddine Lagraa; Chaker Abdelaziz Kerrache; Muhammad Imran; Muhammad Shoaib. 2021. "A policy-based solution for the detection of colluding GPS-Spoofing attacks in FANETs." Transportation Research Part A: Policy and Practice 149, no. : 300-318.

Journal article
Published: 12 April 2021 in IEEE Access
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Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular adhoc 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.

ACS Style

Gamil A. Ahmed; Tarek R. Sheltami; Ashraf S. Mahmoud; Muhammad Imran; Muhammad Shoaib. A Novel Collaborative IoD-Assisted VANET Approach for Coverage Area Maximization. IEEE Access 2021, 9, 61211 -61223.

AMA Style

Gamil A. Ahmed, Tarek R. Sheltami, Ashraf S. Mahmoud, Muhammad Imran, Muhammad Shoaib. A Novel Collaborative IoD-Assisted VANET Approach for Coverage Area Maximization. IEEE Access. 2021; 9 (99):61211-61223.

Chicago/Turabian Style

Gamil A. Ahmed; Tarek R. Sheltami; Ashraf S. Mahmoud; Muhammad Imran; Muhammad Shoaib. 2021. "A Novel Collaborative IoD-Assisted VANET Approach for Coverage Area Maximization." IEEE Access 9, no. 99: 61211-61223.

Journal article
Published: 09 March 2021 in IEEE Access
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There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life.

ACS Style

Ashraf Aldabbas; Zoltan Gal; Khawaja MoyeezUllah Ghori; Muhammad Imran; Muhammad Shoaib. Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft. IEEE Access 2021, 9, 39111 -39125.

AMA Style

Ashraf Aldabbas, Zoltan Gal, Khawaja MoyeezUllah Ghori, Muhammad Imran, Muhammad Shoaib. Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft. IEEE Access. 2021; 9 ():39111-39125.

Chicago/Turabian Style

Ashraf Aldabbas; Zoltan Gal; Khawaja MoyeezUllah Ghori; Muhammad Imran; Muhammad Shoaib. 2021. "Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft." IEEE Access 9, no. : 39111-39125.

Journal article
Published: 01 March 2021 in Sustainability
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Software risks are a common phenomenon in the software development lifecycle, and risks emerge into larger problems if they are not dealt with on time. Software risk management is a strategy that focuses on the identification, management, and mitigation of the risk factors in the software development lifecycle. The management itself depends on the nature, size, and skill of the project under consideration. This paper proposes a model that deals with identifying and dealing with the risk factors by introducing different observatory and participatory project factors. It is assumed that most of the risk factors can be dealt with by doing effective business processing that in response deals with the orientation of risks and elimination or reduction of those risk factors that emerge over time. The model proposes different combinations of resource allocation that can help us conclude a software project with an extended amount of acceptability. This paper presents a Risk Reduction Model, which effectively handles the application development risks. The model can synchronize its working with medium to large-scale software projects. The reduction in software failures positively affects the software development environment, and the software failures shall reduce consequently.

ACS Style

Basit Shahzad; Fazal- E- Amin; Ahsanullah Abro; Muhammad Imran; Muhammad Shoaib. Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development. Sustainability 2021, 13, 2602 .

AMA Style

Basit Shahzad, Fazal- E- Amin, Ahsanullah Abro, Muhammad Imran, Muhammad Shoaib. Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development. Sustainability. 2021; 13 (5):2602.

Chicago/Turabian Style

Basit Shahzad; Fazal- E- Amin; Ahsanullah Abro; Muhammad Imran; Muhammad Shoaib. 2021. "Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development." Sustainability 13, no. 5: 2602.

Journal article
Published: 12 February 2021 in IEEE Access
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A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail.

ACS Style

Mohammad Babar; Muhammad Sohail Khan; Farman Ali; Muhammad Imran; Muhammad Shoaib. Cloudlet Computing: Recent Advances, Taxonomy, and Challenges. IEEE Access 2021, 9, 29609 -29622.

AMA Style

Mohammad Babar, Muhammad Sohail Khan, Farman Ali, Muhammad Imran, Muhammad Shoaib. Cloudlet Computing: Recent Advances, Taxonomy, and Challenges. IEEE Access. 2021; 9 ():29609-29622.

Chicago/Turabian Style

Mohammad Babar; Muhammad Sohail Khan; Farman Ali; Muhammad Imran; Muhammad Shoaib. 2021. "Cloudlet Computing: Recent Advances, Taxonomy, and Challenges." IEEE Access 9, no. : 29609-29622.

Journal article
Published: 02 January 2021 in Neural Computing and Applications
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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.

ACS Style

Zulfiqar Ali; Muhammad Imran; Muhammad Shoaib. An IoT-based smart healthcare system to detect dysphonia. Neural Computing and Applications 2021, 1 -11.

AMA Style

Zulfiqar Ali, Muhammad Imran, Muhammad Shoaib. An IoT-based smart healthcare system to detect dysphonia. Neural Computing and Applications. 2021; ():1-11.

Chicago/Turabian Style

Zulfiqar Ali; Muhammad Imran; Muhammad Shoaib. 2021. "An IoT-based smart healthcare system to detect dysphonia." Neural Computing and Applications , no. : 1-11.

Journal article
Published: 30 June 2020 in IEEE Access
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Cybercriminals are constantly on the lookout for new attack vectors, and the recent COVID-19 pandemic is no exception. For example, social distancing measures have resulted in travel bans, lockdowns, and stay-at-home orders, consequently increasing the reliance on information and communications technologies, such as Zoom. Cybercriminals have also attempted to exploit the pandemic to facilitate a broad range of malicious activities, such as attempting to take over videoconferencing platforms used in online meetings/educational activities, information theft, and other fraudulent activities. This study briefly reviews some of the malicious cyber activities associated with COVID-19 and the potential mitigation solutions. We also propose an attack taxonomy, which (optimistically) will help guide future risk management and mitigation responses.

ACS Style

Saqib Hakak; Wazir Zada Khan; Muhammad Imran; Kim-Kwang Raymond Choo; Muhammad Shoaib. Have You Been a Victim of COVID-19-Related Cyber Incidents? Survey, Taxonomy, and Mitigation Strategies. IEEE Access 2020, 8, 124134 -124144.

AMA Style

Saqib Hakak, Wazir Zada Khan, Muhammad Imran, Kim-Kwang Raymond Choo, Muhammad Shoaib. Have You Been a Victim of COVID-19-Related Cyber Incidents? Survey, Taxonomy, and Mitigation Strategies. IEEE Access. 2020; 8 (99):124134-124144.

Chicago/Turabian Style

Saqib Hakak; Wazir Zada Khan; Muhammad Imran; Kim-Kwang Raymond Choo; Muhammad Shoaib. 2020. "Have You Been a Victim of COVID-19-Related Cyber Incidents? Survey, Taxonomy, and Mitigation Strategies." IEEE Access 8, no. 99: 124134-124144.

Journal article
Published: 19 March 2020 in Computer Communications
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Low power Internet of Things (IoT) is suffering from two limitations: battery-power limitation of IoT nodes and inflexibility of infrastructure-node deployment. In this paper, we propose an Unmanned Aerial Vehicle (UAV)-enabled data acquisition scheme with directional wireless energy transfer (WET) to overcome the limitations of low power IoT. The main idea of the proposed scheme is to employ a UAV to serve as both a data collector and an energy supplier. The UAV first transfers directional wireless energy to an IoT node which then sends back the data packets to the UAV by using the harvested energy. Meanwhile, we minimize the overall energy consumption under conditions of balanced energy supply and limited overall time. Moreover, we derive the optimal values of WET time and data transmission power. After analysing the feasibility of the optimal WET time and data transmission, we design an allocation scheme based on the feasible ranges of data size level and channel-fading degree. The numerical results show the feasibility and adaptability of our allocation scheme against the varied values of multiple system parameters. We further extend our scheme to the multi-node scenario by re-designing energy beamforming and adopting multi-access mechanisms. Moreover, we also analyse the mobility of UAVs in the proposed scheme.

ACS Style

Yalin Liu; Hong-Ning Dai; Hao Wang; Muhammad Imran; Xiaofen Wang; Muhammad Shoaib. UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things. Computer Communications 2020, 155, 184 -196.

AMA Style

Yalin Liu, Hong-Ning Dai, Hao Wang, Muhammad Imran, Xiaofen Wang, Muhammad Shoaib. UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things. Computer Communications. 2020; 155 ():184-196.

Chicago/Turabian Style

Yalin Liu; Hong-Ning Dai; Hao Wang; Muhammad Imran; Xiaofen Wang; Muhammad Shoaib. 2020. "UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things." Computer Communications 155, no. : 184-196.

Journal article
Published: 05 March 2020 in IEEE Access
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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.

ACS Style

Neelum Noreen; Sellappan Palaniappan; Abdul Qayyum; Iftikhar Ahmad; Muhammad Imran; Muhammad Shoaib. A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor. IEEE Access 2020, 8, 55135 -55144.

AMA Style

Neelum Noreen, Sellappan Palaniappan, Abdul Qayyum, Iftikhar Ahmad, Muhammad Imran, Muhammad Shoaib. A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor. IEEE Access. 2020; 8 (99):55135-55144.

Chicago/Turabian Style

Neelum Noreen; Sellappan Palaniappan; Abdul Qayyum; Iftikhar Ahmad; Muhammad Imran; Muhammad Shoaib. 2020. "A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor." IEEE Access 8, no. 99: 55135-55144.

Journal article
Published: 27 February 2020 in IEEE Access
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Medical imaging has become of vital importance for diagnosing diseases and conducting noninvasive procedures. Advances in eHealth applications are challenged by the fact that Digital Imaging and Communications in Medicine (DICOM) requires high-resolution images, thereby increasing their size and the associated computational complexity, particularly when these images are communicated over IP and wireless networks. Therefore, medical research requires an efficient coding technique to achieve highquality and low-complexity images with error-resilient features. In this study, we propose an improved coding scheme that exploits the content features of encoded videos with low complexity combined with flexible macroblock ordering for error resilience. We identify the homogeneous region in which the search for optimal macroblock modes is terminated early. For non-homogeneous regions, the integration of smaller blocks is employed only if the vector difference is less than the threshold. Results confirm that the proposed technique achieves a considerable performance improvement compared with existing schemes in terms of reducing the computational complexity without compromising the bit-rate and peak signal-to-noise ratio.

ACS Style

Muhammad Shoaib; Muhammad Imran; Fazli Subhan; Iftikhar Ahmad. Towards a Low Complexity Scheme for Medical Images in Scalable Video Coding. IEEE Access 2020, 8, 41439 -41451.

AMA Style

Muhammad Shoaib, Muhammad Imran, Fazli Subhan, Iftikhar Ahmad. Towards a Low Complexity Scheme for Medical Images in Scalable Video Coding. IEEE Access. 2020; 8 (99):41439-41451.

Chicago/Turabian Style

Muhammad Shoaib; Muhammad Imran; Fazli Subhan; Iftikhar Ahmad. 2020. "Towards a Low Complexity Scheme for Medical Images in Scalable Video Coding." IEEE Access 8, no. 99: 41439-41451.

Journal article
Published: 14 January 2020 in IEEE Access
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The future of the hash based digital signature schemes appears to be very bright in the upcoming quantum era because of the quantum threats to the number theory based digital signature schemes. The Shor’s algorithm is available to allow a sufficiently powerful quantum computer to break the building blocks of the number theory based signature schemes in a polynomial time. The hash based signature schemes being quite efficient and provably secure can fill in the gap effectively. However, a draw back of the hash based signature schemes is the larger key and signature sizes which can prove a barrier in their adoption by the space critical applications, like the blockchain. A hash based signature scheme is constructed using a one time signature (OTS) scheme. The underlying OTS scheme plays an important role in determining key and signature sizes of a hash based signature scheme. In this article, we have proposed a novel OTS scheme with minimized key and signature sizes as compared to all of the existing OTS schemes. Our proposed OTS scheme offers an 88% reduction in both key and signature sizes as compared to the popular Winternitz OTS scheme. Furthermore, our proposed OTS scheme offers an 84% and an 86% reductions in the signature and the key sizes respectively as compared to an existing compact variant of the WOTS scheme, i.e. WOTS+.

ACS Style

Furqan Shahid; Iftikhar Ahmad; Muhammad Imran; Muhammad Shoaib. Novel One Time Signatures (NOTS): A Compact Post-Quantum Digital Signature Scheme. IEEE Access 2020, 8, 15895 -15906.

AMA Style

Furqan Shahid, Iftikhar Ahmad, Muhammad Imran, Muhammad Shoaib. Novel One Time Signatures (NOTS): A Compact Post-Quantum Digital Signature Scheme. IEEE Access. 2020; 8 (99):15895-15906.

Chicago/Turabian Style

Furqan Shahid; Iftikhar Ahmad; Muhammad Imran; Muhammad Shoaib. 2020. "Novel One Time Signatures (NOTS): A Compact Post-Quantum Digital Signature Scheme." IEEE Access 8, no. 99: 15895-15906.

Journal article
Published: 30 October 2019 in IEEE Internet of Things Journal
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ACS Style

Hao Tang; Di Li; Jiafu Wan; Muhammad Imran; Muhammad Shoaib. A Reconfigurable Method for Intelligent Manufacturing Based on Industrial Cloud and Edge Intelligence. IEEE Internet of Things Journal 2019, 7, 4248 -4259.

AMA Style

Hao Tang, Di Li, Jiafu Wan, Muhammad Imran, Muhammad Shoaib. A Reconfigurable Method for Intelligent Manufacturing Based on Industrial Cloud and Edge Intelligence. IEEE Internet of Things Journal. 2019; 7 (5):4248-4259.

Chicago/Turabian Style

Hao Tang; Di Li; Jiafu Wan; Muhammad Imran; Muhammad Shoaib. 2019. "A Reconfigurable Method for Intelligent Manufacturing Based on Industrial Cloud and Edge Intelligence." IEEE Internet of Things Journal 7, no. 5: 4248-4259.

Journal article
Published: 29 March 2019 in Future Generation Computer Systems
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The rapid growth in communication technology facilitates the health industry in many aspects from transmission of sensor’s data to real-time diagnosis using cloud-based frameworks. However, the secure transmission of data and its authenticity become a challenging task, especially, for health-related applications. The medical information must be accessible to only the relevant healthcare staff to avoid any inevitable circumstances for the patient as well as to the healthcare providers. Therefore, a method to protect the identity of a patient and authentication of transmitted data is proposed in this study. The proposed method provides dual protection. First, it encrypts the identity using Shamir’s secret sharing scheme without the increase in dimension of the original identity. Second, the identity is watermarked using zero-watermarking to avoid any distortion into the host signal. The experimental results show that the proposed method encrypts, embeds and extracts identities reliably. Moreover, in case of malicious attack, the method distorts the embedded identity which provides a clear indication of fabrication. An automatic disorder detection system using Mel-frequency cepstral coefficients and Gaussian mixture model is also implemented which concludes that malicious attacks greatly impact on the accurate diagnosis of disorders.

ACS Style

Zulfiqar Ali; Muhammad Imran; Sally McClean; Naveed Khan; Muhammad Shoaib. Protection of records and data authentication based on secret shares and watermarking. Future Generation Computer Systems 2019, 98, 331 -341.

AMA Style

Zulfiqar Ali, Muhammad Imran, Sally McClean, Naveed Khan, Muhammad Shoaib. Protection of records and data authentication based on secret shares and watermarking. Future Generation Computer Systems. 2019; 98 ():331-341.

Chicago/Turabian Style

Zulfiqar Ali; Muhammad Imran; Sally McClean; Naveed Khan; Muhammad Shoaib. 2019. "Protection of records and data authentication based on secret shares and watermarking." Future Generation Computer Systems 98, no. : 331-341.

Journal article
Published: 20 March 2019 in Journal of Systems Architecture
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Software Defined Network (SDN) is a new network architecture that controls the network through a logically centralized controller. The controller computes and installs the flow rules (i.e., entries) in the flow table at switches. When a switch receives the data packet and does not have the flow rule in its flow table, the switch contacts controller. The best path is computed by using topology and network policies. Then controller installs flow rules at switches along the path. Subsequently, the packet is forwarded to the switches accordingly. Due to the asynchronous nature of SDN, the packet may reach an intermediate switch before the corresponding flow rules. In this case, the packet is dropped by the switch. To address this problem, we propose a new technique that computes delay of both, the flow rules installation and packet arrival at a switch along the path. In case the packet arrival delay at the switch S1 is less than the delay of corresponding flow rule installation at S1, then the packet is delayed for a minimum duration at the predecessor switch of S1 in such a way that the corresponding flow rule is installed before the packet arrival. Thus, the proposed mechanism ensures flow rule installation before the corresponding packet reaches switches and subsequently reduces packet loss. To compute the delay between any two switches, and a switch and controller, our proposed technique exploits the keep-alive messages exchanged between switch and controller in order to reduce the redundant traffic in the network. We evaluated our proposed technique in terms of packet delivery ratio and average packet end-to-end delay in Mininet emulator. Our proposed technique improves the packet delivery ratio up to 36% and average packet end-to-end delay is reduced to 74% in case of a varying number of flows as compared to an existing mechanism. Moreover, we testify our proposed technique by running the real network traces.

ACS Style

Israr Iqbal Awan; Nadir Shah; Muhammad Imran; Muhammad Shoaib; Nasir Saeed. An improved mechanism for flow rule installation in-band SDN. Journal of Systems Architecture 2019, 96, 1 -19.

AMA Style

Israr Iqbal Awan, Nadir Shah, Muhammad Imran, Muhammad Shoaib, Nasir Saeed. An improved mechanism for flow rule installation in-band SDN. Journal of Systems Architecture. 2019; 96 ():1-19.

Chicago/Turabian Style

Israr Iqbal Awan; Nadir Shah; Muhammad Imran; Muhammad Shoaib; Nasir Saeed. 2019. "An improved mechanism for flow rule installation in-band SDN." Journal of Systems Architecture 96, no. : 1-19.

Journal article
Published: 17 January 2019 in IEEE Access
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Recently, linear wireless sensor networks (LWSNs) have been eliciting increasing attention because of their suitability for applications such as the protection of critical infrastructures. Most of these applications require LWSN to remain operational for a longer period. However, the non-replenishable limited battery power of sensor nodes does not allow them to meet these expectations. Therefore, a shorter network lifetime is one of the most prominent barriers in large-scale deployment of LWSN. Unlike most existing studies, in this study, we analyze the impact of node placement and clustering on LWSN network lifetime. First, we categorize and classify existing node placement and clustering schemes for LWSN and introduce various topologies for disparate applications. Then, we highlight the peculiarities of LWSN applications and discussed their unique characteristics. Several application domains of LWSN are described. We present three node placement strategies (i.e., linear sequential, linear parallel, and grid) and various deployment methods such as random, uniform, decreasing distance, and triangular. Extensive simulation experiments are conducted to analyze the performance of the three state-of-the-art routing protocols in the context of node deployment strategies and methods. Experimental results demonstrate that node deployment strategies and methods significantly affect LWSN lifetime.

ACS Style

Fazli Subhan; Madiha Noreen; Muhammad Imran; Moeenuddin Tariq; Asfandyar Khan; Muhammad Shoaib. Impact of Node Deployment and Routing for Protection of Critical Infrastructures. IEEE Access 2019, 7, 11502 -11514.

AMA Style

Fazli Subhan, Madiha Noreen, Muhammad Imran, Moeenuddin Tariq, Asfandyar Khan, Muhammad Shoaib. Impact of Node Deployment and Routing for Protection of Critical Infrastructures. IEEE Access. 2019; 7 (99):11502-11514.

Chicago/Turabian Style

Fazli Subhan; Madiha Noreen; Muhammad Imran; Moeenuddin Tariq; Asfandyar Khan; Muhammad Shoaib. 2019. "Impact of Node Deployment and Routing for Protection of Critical Infrastructures." IEEE Access 7, no. 99: 11502-11514.

Mobile and wireless health
Published: 05 November 2018 in Journal of Medical Systems
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Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology–Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.

ACS Style

Uzair Iqbal; Teh Ying Wah; Muhammad Habib Ur Rehman; Ghulam Mujtaba; Muhammad Imran; Muhammad Shoaib. Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things. Journal of Medical Systems 2018, 42, 252 .

AMA Style

Uzair Iqbal, Teh Ying Wah, Muhammad Habib Ur Rehman, Ghulam Mujtaba, Muhammad Imran, Muhammad Shoaib. Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things. Journal of Medical Systems. 2018; 42 (12):252.

Chicago/Turabian Style

Uzair Iqbal; Teh Ying Wah; Muhammad Habib Ur Rehman; Ghulam Mujtaba; Muhammad Imran; Muhammad Shoaib. 2018. "Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things." Journal of Medical Systems 42, no. 12: 252.

Journal article
Published: 01 November 2018 in Future Generation Computer Systems
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ACS Style

Zulfiqar Ali; Muhammad Imran; Mansour Alsulaiman; Muhammad Shoaib; Sana Ullah. Chaos-based robust method of zero-watermarking for medical signals. Future Generation Computer Systems 2018, 88, 400 -412.

AMA Style

Zulfiqar Ali, Muhammad Imran, Mansour Alsulaiman, Muhammad Shoaib, Sana Ullah. Chaos-based robust method of zero-watermarking for medical signals. Future Generation Computer Systems. 2018; 88 ():400-412.

Chicago/Turabian Style

Zulfiqar Ali; Muhammad Imran; Mansour Alsulaiman; Muhammad Shoaib; Sana Ullah. 2018. "Chaos-based robust method of zero-watermarking for medical signals." Future Generation Computer Systems 88, no. : 400-412.

Journal article
Published: 22 October 2018 in Computers in Human Behavior
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Recently, mobile crowd sensing (MCS) is captivating growing attention because of their suitability for enormous range of new types of context-aware applications and services. This is attributed to the fact that modern smartphones are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform more complex tasks besides their inherent calling features. Despite a number of merits, MCS confronts new challenges due to network dynamics, the huge volume of data, sensing task coordination, and the user privacy problems. In this paper, a comprehensive review of MCS is presented. First, we highlight the distinguishing features and potential advantages of MCS compared to conventional sensor networks. Then, a taxonomy of MCS is devised based on sensing scale, level of user involvement and responsiveness, sampling rate, and underlying network infrastructure. Afterward, we categorize and classify prominent applications of MCS in environmental, infrastructure, social, and behavioral domains. The core architecture of MCS is also described. Finally, we describe the potential advantages, determine and reiterate the open research challenges of MCS and illustrate possible solutions.

ACS Style

Djallel Eddine Boubiche; Muhammad Imran; Aneela Maqsood; Muhammad Shoaib. Mobile crowd sensing – Taxonomy, applications, challenges, and solutions. Computers in Human Behavior 2018, 101, 352 -370.

AMA Style

Djallel Eddine Boubiche, Muhammad Imran, Aneela Maqsood, Muhammad Shoaib. Mobile crowd sensing – Taxonomy, applications, challenges, and solutions. Computers in Human Behavior. 2018; 101 ():352-370.

Chicago/Turabian Style

Djallel Eddine Boubiche; Muhammad Imran; Aneela Maqsood; Muhammad Shoaib. 2018. "Mobile crowd sensing – Taxonomy, applications, challenges, and solutions." Computers in Human Behavior 101, no. : 352-370.

Special issue article
Published: 14 August 2018 in Transactions on Emerging Telecommunications Technologies
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Uplink multicarrier localized nonorthogonal multiple access (MC‐LNOMA) is a variant of hybrid nonorthogonal multiple access, where subcarrier mapping is performed in localized mode. MC‐LNOMA is one of the most prominent emerging schemes and likely to be employed in the forthcoming fifth‐generation cellular networks due to its massive connectivity, spectral efficiency, better cell coverage capability, and higher data rate. It may employ orthogonal frequency‐division multiple access due to the technical ripeness. However, schemes based on orthogonal frequency‐division multiple access all suffer from the high peak‐to‐average power ratio problem. Therefore, in this paper, a new finite impulse response filter–based discrete cosine transform–precoded uplink MC‐LNOMA scheme is presented for peak‐to‐average power ratio reduction. MATLAB simulations demonstrate the performance supremacy of the proposed scheme compared to contemporary schemes such as discrete cosine transform–precoded uplink MC‐LNOMA and nonprecoded uplink MC‐LNOMA.

ACS Style

Imran Baig; Umer Farooq; Ejaz Ahmed; Muhammad Imran; Muhammad Shoaib. A hybrid precoding- and filtering-based uplink MC-LNOMA scheme for 5G cellular networks with reduced PAPR. Transactions on Emerging Telecommunications Technologies 2018, 29, e3501 .

AMA Style

Imran Baig, Umer Farooq, Ejaz Ahmed, Muhammad Imran, Muhammad Shoaib. A hybrid precoding- and filtering-based uplink MC-LNOMA scheme for 5G cellular networks with reduced PAPR. Transactions on Emerging Telecommunications Technologies. 2018; 29 (10):e3501.

Chicago/Turabian Style

Imran Baig; Umer Farooq; Ejaz Ahmed; Muhammad Imran; Muhammad Shoaib. 2018. "A hybrid precoding- and filtering-based uplink MC-LNOMA scheme for 5G cellular networks with reduced PAPR." Transactions on Emerging Telecommunications Technologies 29, no. 10: e3501.

Journal article
Published: 14 May 2018 in IEEE Access
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Scientific organizations and researchers are eager to apply recent technological advancements, such as sensors and actuators, in different application areas, including environmental monitoring, creation of intelligent buildings, and precision agriculture. Technology-assisted irrigation for agriculture is a major research innovation which eases the work of farmers and prevents water wastage. Wireless sensor networks (WSNs) are used as sensor nodes that directly interact with the physical environment and provide real-time data that are useful in identifying regions in need, particularly in agricultural fields. This paper presents an efficient methodology that employs WSN as a data collection tool and a decision support system (DSS). The proposed DSS can assist farmers in their manual irrigation procedures or automate irrigation activities. Water-deficient sites in both scenarios are identified by using soil moisture and environmental data sensors. However, the proposed system's accuracy is directly proportional to the accuracy of dynamic data generated by the deployed WSN. A simplified outlier-detection algorithm is thus presented and integrated with the proposed DSS to fine-tune the collected data prior to processing. The complexity of the algorithm is O(1) for dynamic datasets generated by sensor nodes and O(n) for static datasets. Different issues in technology-assisted irrigation management and their solutions are also addressed.

ACS Style

Rahim Khan; Ihsan Ali; Muhammad Zakarya; Mushtaq Ahmad; Muhammad Imran; Muhammad Shoaib. Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks. IEEE Access 2018, 6, 25686 -25697.

AMA Style

Rahim Khan, Ihsan Ali, Muhammad Zakarya, Mushtaq Ahmad, Muhammad Imran, Muhammad Shoaib. Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks. IEEE Access. 2018; 6 ():25686-25697.

Chicago/Turabian Style

Rahim Khan; Ihsan Ali; Muhammad Zakarya; Mushtaq Ahmad; Muhammad Imran; Muhammad Shoaib. 2018. "Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks." IEEE Access 6, no. : 25686-25697.