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Dr. Muhammad Imran
College of Applied Computer Science, King Saud University, Saudi Arabia

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Research Keywords & Expertise

0 Big Data
0 Big Data Analytics
0 Data Analysis
0 Information Security
0 Internet Of Things (IoT)

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Short Biography

Muhammad Imran is an Associate Professor in the College of Applied Computer Science at King Saud University, Saudi Arabia. He received a Ph.D. in information technology from the University Teknologi PETRONAS, Malaysia, in 2011. His research interest includes IoT, mobile and wireless networks, big data analytics, cloud computing, and information security. He has published more than 250 research articles in peer-reviewed, well-recognized international conferences and journals. He has also served as an Editor in Chief for European Alliance for Innovation (EAI) Transactions on Pervasive Health and Technology, and is currently serving as an associate editor for top ranked international journals, such as IEEE Communications Magazine, IEEE Network, Future Generation Computer Systems, and IEEE Access. He serves as a guest editor for approximately two dozen special issues in journals, such as IEEE Communications Magazine, IEEE Wireless Communications Magazine, Future Generation Computer Systems, IEEE Access, and Computer Networks. He has been consecutively awarded Outstanding Associate Editor of IEEE Access in 2018 and 2019, besides many others.

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Journal article
Published: 08 July 2021 in Applied Soft Computing
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Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique.

ACS Style

Umar Farooq; Muhammad Wasif Shabir; Muhammad Awais Javed; Muhammad Imran. Intelligent energy prediction techniques for fog computing networks. Applied Soft Computing 2021, 111, 107682 .

AMA Style

Umar Farooq, Muhammad Wasif Shabir, Muhammad Awais Javed, Muhammad Imran. Intelligent energy prediction techniques for fog computing networks. Applied Soft Computing. 2021; 111 ():107682.

Chicago/Turabian Style

Umar Farooq; Muhammad Wasif Shabir; Muhammad Awais Javed; Muhammad Imran. 2021. "Intelligent energy prediction techniques for fog computing networks." Applied Soft Computing 111, no. : 107682.

Journal article
Published: 06 July 2021 in IEEE Access
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Non-Technical Loss (NTL) is a major concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets in order to combat NTL. An important characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is in predicting the minority class of suspect activities, the impact of sensitivity of the classifiers to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalance dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques while random forest is the least sensitive to the sampling technique as the change of its precision is observed between 1% – 6% for all sampling techniques.

ACS Style

Khawaja MoyeezUllah Ghori; Muhammad Awais; Akmal Saeed Khattak; Muhammad Imran; Fazal- E- Amin; Laszlo Szathmary. Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset. IEEE Access 2021, 9, 98928 -98938.

AMA Style

Khawaja MoyeezUllah Ghori, Muhammad Awais, Akmal Saeed Khattak, Muhammad Imran, Fazal- E- Amin, Laszlo Szathmary. Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset. IEEE Access. 2021; 9 (99):98928-98938.

Chicago/Turabian Style

Khawaja MoyeezUllah Ghori; Muhammad Awais; Akmal Saeed Khattak; Muhammad Imran; Fazal- E- Amin; Laszlo Szathmary. 2021. "Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset." IEEE Access 9, no. 99: 98928-98938.

Journal article
Published: 15 June 2021 in Physical Communication
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Fog computing is considered a promising technology to reduce latency and network congestion. Meanwhile, Millimeter-wave (mmWave) communication owing to its potential for multi-gigabit of wireless channel capacity could be employed to further improve the performance of fog computing networks. In this context, we study the feasibility of using 28 GHz and 38 GHz mmWaves in fog radio access networks (F-RANs). The multi-slope path loss model is used to calculate the interference impacts because it provides a more accurate approximation of the wireless links. Simulations are carried out for uplink scenario considering the following fog node (FN) deployment models: Poisson point process (PPP), Ginibre point process (GPP), square grid, and ultra-dense network (UDN). The results depict that at low FN densities the massive accumulation of interference components severely impacts the performance. However, the performance can be improved by increasing the FN density and choosing a deployment strategy with high degree of regularity. Based on the results, we verify that it is feasible to use 28 GHz and 38 GHz mmWaves in F-RANs when the density of the interfering users is less than 150 user/km2 where capacities higher than 1 Gbps are achieved for all considered scenarios.

ACS Style

Alaa Bani-Bakr; Kaharudin Dimyati; Mhd Nour Hindia; Wei Ru Wong; Muhammad Ali Imran. Feasibility study of 28 GHz and 38 GHz millimeter-wave technologies for fog radio access networks using multi-slope path loss model. Physical Communication 2021, 47, 101401 .

AMA Style

Alaa Bani-Bakr, Kaharudin Dimyati, Mhd Nour Hindia, Wei Ru Wong, Muhammad Ali Imran. Feasibility study of 28 GHz and 38 GHz millimeter-wave technologies for fog radio access networks using multi-slope path loss model. Physical Communication. 2021; 47 ():101401.

Chicago/Turabian Style

Alaa Bani-Bakr; Kaharudin Dimyati; Mhd Nour Hindia; Wei Ru Wong; Muhammad Ali Imran. 2021. "Feasibility study of 28 GHz and 38 GHz millimeter-wave technologies for fog radio access networks using multi-slope path loss model." Physical Communication 47, no. : 101401.

Journal article
Published: 08 June 2021 in Pervasive and Mobile Computing
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The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.

ACS Style

Xuran Li; Bishenghui Tao; Hong-Ning Dai; Muhammad Imran; Dehuan Wan; Dengwang Li. Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic? Pervasive and Mobile Computing 2021, 75, 101434 -101434.

AMA Style

Xuran Li, Bishenghui Tao, Hong-Ning Dai, Muhammad Imran, Dehuan Wan, Dengwang Li. Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic? Pervasive and Mobile Computing. 2021; 75 ():101434-101434.

Chicago/Turabian Style

Xuran Li; Bishenghui Tao; Hong-Ning Dai; Muhammad Imran; Dehuan Wan; Dengwang Li. 2021. "Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?" Pervasive and Mobile Computing 75, no. : 101434-101434.

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.

Review
Published: 22 May 2021 in EURASIP Journal on Wireless Communications and Networking
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Big data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems.

ACS Style

Memoona J. Anwar; Asif Q. Gill; Farookh K. Hussain; Muhammad Imran. Secure big data ecosystem architecture: challenges and solutions. EURASIP Journal on Wireless Communications and Networking 2021, 2021, 1 -30.

AMA Style

Memoona J. Anwar, Asif Q. Gill, Farookh K. Hussain, Muhammad Imran. Secure big data ecosystem architecture: challenges and solutions. EURASIP Journal on Wireless Communications and Networking. 2021; 2021 (1):1-30.

Chicago/Turabian Style

Memoona J. Anwar; Asif Q. Gill; Farookh K. Hussain; Muhammad Imran. 2021. "Secure big data ecosystem architecture: challenges and solutions." EURASIP Journal on Wireless Communications and Networking 2021, no. 1: 1-30.

Journal article
Published: 20 May 2021 in Computer Standards & Interfaces
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The security vulnerabilities are becoming the major obstacle to prevent the wide adoption of ultra-reliable and low latency communications (URLLC) in 5G and beyond communications. Current security countermeasures based on cryptographic algorithms have a stringent requirement on the centralized key management as well as computational capabilities of end devices while it may not be feasible for URLLC in 5G and beyond communications. In contrast to cryptographic approaches, friendly jamming (FJ) as a promising physical layer security method can enhance wireless communications security while it has less resource requirement on end devices and it can be applied to the full distribution environment. In order to protect wireless communications, FJ signals are introduced to degrade the decoding ability of eavesdroppers who maliciously wiretap confidential information. This article presents a state-of-the-art survey on FJ schemes to enhance network security for IoT networks with consideration of various emerging wireless technologies and different types of networks. First, we present various secrecy performance metrics and introduce the FJ method. The interference caused by FJ signals on legitimate communication is the major challenge of using FJ schemes. In order to overcome this challenge, we next introduce the integration of FJ schemes with various communication technologies, including beamforming, multiple-input multiple-output, full duplex, and relay selection. In addition, we also integrate FJ schemes with different types of communication networks. Finally, a case study of FJ schemes is illustrated and future research directions of FJ schemes have been outlined.

ACS Style

Xuran Li; Hong-Ning Dai; Mahendra K. Shukla; Dengwang Li; Huaqiang Xu; Muhammad Imran. Friendly-jamming schemes to secure ultra-reliable and low-latency communications in 5G and beyond communications. Computer Standards & Interfaces 2021, 78, 103540 .

AMA Style

Xuran Li, Hong-Ning Dai, Mahendra K. Shukla, Dengwang Li, Huaqiang Xu, Muhammad Imran. Friendly-jamming schemes to secure ultra-reliable and low-latency communications in 5G and beyond communications. Computer Standards & Interfaces. 2021; 78 ():103540.

Chicago/Turabian Style

Xuran Li; Hong-Ning Dai; Mahendra K. Shukla; Dengwang Li; Huaqiang Xu; Muhammad Imran. 2021. "Friendly-jamming schemes to secure ultra-reliable and low-latency communications in 5G and beyond communications." Computer Standards & Interfaces 78, no. : 103540.

Journal article
Published: 14 May 2021 in Computer Networks
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Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation.

ACS Style

Arwa Mohamed; Mosab Hamdan; Suleman Khan; Ahmed Abdelaziz; Sharief F. Babiker; Muhammad Imran; M.N. Marsono. Software-defined networks for resource allocation in cloud computing: A survey. Computer Networks 2021, 195, 108151 .

AMA Style

Arwa Mohamed, Mosab Hamdan, Suleman Khan, Ahmed Abdelaziz, Sharief F. Babiker, Muhammad Imran, M.N. Marsono. Software-defined networks for resource allocation in cloud computing: A survey. Computer Networks. 2021; 195 ():108151.

Chicago/Turabian Style

Arwa Mohamed; Mosab Hamdan; Suleman Khan; Ahmed Abdelaziz; Sharief F. Babiker; Muhammad Imran; M.N. Marsono. 2021. "Software-defined networks for resource allocation in cloud computing: A survey." Computer Networks 195, no. : 108151.

Journal article
Published: 08 May 2021 in Ain Shams Engineering Journal
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Real Estate Online Platforms (REOPs) are used for conveying real estate and property-related information to potential users (buyers, renters, or sellers). The information leveraged through REOPs supports these users in reaching conclusive rent or buy decisions. Despite their promised utility, user perception about accepting online information through REOPs is unexplored. Using a comprehensive questionnaire and data collected from 65 users, the current study captures the users’ perception of REOPs. Risk, service, information, system, technology adoption model (RSISTAM) is proposed comprising of seven users’ perceptions: risk (PR), service quality (PSEQ), information quality (PIQ), and system quality (PSYQ) from the information systems success model, and usefulness (PU), ease of use (PEU) and behaviour to accept (BAU) from TAM. The results are analysed using the decision making trial and evaluation laboratory (DEMATEL) approach, which shows that PIQ, PSEQ and PEU are the causes and PR, PSYQ, PU and BAU are the effects. Among the criteria, the order of prominence is PEU > PSEQ > PIQ, and for net effects, the order is PU > BAU > PSYQ > PR. For addressing the causes, the REOP managers must provide more transparent, high quality and voluminous information to the users, focus on the system, services, and information qualities, and add more enjoyable, immersive and easy-to-use content through REOPs. This study contributes to the body of knowledge by exploring user perceptions and proposing methods to improve the quality and reliability of REOPs in line with Real Estate 4.0 and industry 4.0 aims.

ACS Style

Fahim Ullah; Samad M.E. Sepasgozar; Muhammad Jamaluddin Thaheem; Changxin Cynthia Wang; Muhammad Imran. It’s all about perceptions: A DEMATEL approach to exploring user perceptions of real estate online platforms. Ain Shams Engineering Journal 2021, 1 .

AMA Style

Fahim Ullah, Samad M.E. Sepasgozar, Muhammad Jamaluddin Thaheem, Changxin Cynthia Wang, Muhammad Imran. It’s all about perceptions: A DEMATEL approach to exploring user perceptions of real estate online platforms. Ain Shams Engineering Journal. 2021; ():1.

Chicago/Turabian Style

Fahim Ullah; Samad M.E. Sepasgozar; Muhammad Jamaluddin Thaheem; Changxin Cynthia Wang; Muhammad Imran. 2021. "It’s all about perceptions: A DEMATEL approach to exploring user perceptions of real estate online platforms." Ain Shams Engineering Journal , no. : 1.

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.

Research article
Published: 28 March 2021 in International Journal of Intelligent Systems
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Real‐estate advertisements through electronic and print media are bringing considerable fortune to the global real‐estate sector. However, innovative advertisement methods must be adopted if real estate aims to transform into smart real estate. The current study, which is based on a systematic literature review of 58 articles published in the last decade, identifies key media for real‐estate advertisements as print media (e.g., magazines, brochures, newspapers, and digests), electronic media (e.g., websites, social media, and other digital tools), and mixed methods (e.g., billboards, signs and banners, and personalized messaging). This study takes the case of Kingsford suburb in the eastern Sydney area, and investigates the performance of the Australian real‐estate industry in general and lists the key dynamics of properties in Kingsford and its prominent real‐estate agencies. An unmanned aerial vehicle (UAV)‐based smart real‐estate advertisement material delivery system is proposed to deliver advertisement materials and gifts to the potential customers of these agencies. The system paths are optimized through four Java‐run algorithms: greedy, interroute, intraroute, and Tabu. Results based on six cases, three each for rent and sales with varying numbers of customers and UAVs and an 8‐h operating time, indicate that the Tabu algorithm provides the best‐optimized paths in all cases, followed by the interroute, intraroute, and greedy algorithms. However, the inter‐ and intraroute algorithms show superior performance in terms of computation speed. The proposed framework is a practical approach in disrupting the real‐estate advertising sector, thereby helping this sector transform into a smart real estate consistent with industry 4.0 goals.

ACS Style

Fahim Ullah; Fadi Al‐Turjman; Siddra Qayyum; Hina Inam; Muhammad Imran. Advertising through UAVs: Optimized path system for delivering smart real‐estate advertisement materials. International Journal of Intelligent Systems 2021, 36, 3429 -3463.

AMA Style

Fahim Ullah, Fadi Al‐Turjman, Siddra Qayyum, Hina Inam, Muhammad Imran. Advertising through UAVs: Optimized path system for delivering smart real‐estate advertisement materials. International Journal of Intelligent Systems. 2021; 36 (7):3429-3463.

Chicago/Turabian Style

Fahim Ullah; Fadi Al‐Turjman; Siddra Qayyum; Hina Inam; Muhammad Imran. 2021. "Advertising through UAVs: Optimized path system for delivering smart real‐estate advertisement materials." International Journal of Intelligent Systems 36, no. 7: 3429-3463.

Journal article
Published: 21 March 2021 in Computers & Electrical Engineering
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The transmission of audio data via the Internet of Things makes such data vulnerable to tampering. Moreover, the availability of sophisticated tampering tools has allowed mobsters to change the context of audio data by altering their segments. Tampered audio may result in unpleasant situations for any member of society. To avoid such circumstances, a new audio forgery detection system is proposed in this study. This system can be deployed in edge devices to identify impostors and tampering in audio data. The proposed system is implemented using state-of-the-art mel-frequency cepstral coefficient features. Meanwhile, a Gaussian mixture model is used to train and validate the system. To evaluate the proposed system, a dataset of tampered audios is created by mixing recordings from two different speakers. The performance of the proposed system in authenticating genuine audio is between 92.50% and 100%, and that in detecting forged audio is between 99.90 and 100%.

ACS Style

Zeshan Mubeen; Mehtab Afzal; Zulfiqar Ali; Suleman Khan; Muhammad Imran. Detection of impostor and tampered segments in audio by using an intelligent system. Computers & Electrical Engineering 2021, 91, 107122 .

AMA Style

Zeshan Mubeen, Mehtab Afzal, Zulfiqar Ali, Suleman Khan, Muhammad Imran. Detection of impostor and tampered segments in audio by using an intelligent system. Computers & Electrical Engineering. 2021; 91 ():107122.

Chicago/Turabian Style

Zeshan Mubeen; Mehtab Afzal; Zulfiqar Ali; Suleman Khan; Muhammad Imran. 2021. "Detection of impostor and tampered segments in audio by using an intelligent system." Computers & Electrical Engineering 91, no. : 107122.

Research article
Published: 10 March 2021 in Transactions on Emerging Telecommunications Technologies
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Next‐generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the higher throughput and very low latency. In this context, the usage of unmanned aerial vehicle (UAV) is becoming a feasible option for deploying 5G services on demand. At the same time, the immense bandwidth potential of mmWave has strengthened its performance in radio communication. In this article, we provide a consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges. The review focuses on UAV relay architectures, identifies the relevant problems and limitations in the deployment of UAVs using mmWave in both access and backhaul links simultaneously. There is a critical analysis of the optimum placement of the UAVs as a relay with a focus on the mmWave band. The distinctive rich characteristics of the mmWave propagation and scattering are presented. We also synthesis mmWave path loss models. Then, the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of UAV‐based cellular communication networks are discussed. In the end, security and privacy issues in UAV‐based cellular network are spotlighted. It is believed that the literature discussed, and the findings reached in this article are of significant importance to researchers, application engineers and decision‐makers in the designing and deployment of UAV‐supported 5G network.

ACS Style

Shah Khalid Khan; Usman Naseem; Haris Siraj; Imran Razzak; Muhammad Imran. The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges. Transactions on Emerging Telecommunications Technologies 2021, e4241 .

AMA Style

Shah Khalid Khan, Usman Naseem, Haris Siraj, Imran Razzak, Muhammad Imran. The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges. Transactions on Emerging Telecommunications Technologies. 2021; ():e4241.

Chicago/Turabian Style

Shah Khalid Khan; Usman Naseem; Haris Siraj; Imran Razzak; Muhammad Imran. 2021. "The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges." Transactions on Emerging Telecommunications Technologies , no. : e4241.

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.

Research article
Published: 03 February 2021 in Transactions on Emerging Telecommunications Technologies
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In the future smart cities, connected vehicles provide various infotainment and nonsafety services using vehicle to vehicle and vehicle to road side units (RSUs) communications. To enable these infotainment services, content providers cache their popular data on the RSU storage to make it quickly accessible for the vehicles. In this article, we propose a novel content caching protocol used by mobile network operators. The proposed protocol uses knapsack optimization algorithm to allocate the variable‐sized contents to the RSUs based on their utility. Results show that the proposed protocol provides 13% and 70% approximately higher download data than the market matching and random caching, respectively.

ACS Style

Muddasir Rahim; Shaukat Ali; Ahmad Naseem Alvi; Muhammad Awais Javed; Muhammad Imran; Muhammad Ajmal Azad; Dong Chen. An intelligent content caching protocol for connected vehicles. Transactions on Emerging Telecommunications Technologies 2021, 32, e4231 .

AMA Style

Muddasir Rahim, Shaukat Ali, Ahmad Naseem Alvi, Muhammad Awais Javed, Muhammad Imran, Muhammad Ajmal Azad, Dong Chen. An intelligent content caching protocol for connected vehicles. Transactions on Emerging Telecommunications Technologies. 2021; 32 (4):e4231.

Chicago/Turabian Style

Muddasir Rahim; Shaukat Ali; Ahmad Naseem Alvi; Muhammad Awais Javed; Muhammad Imran; Muhammad Ajmal Azad; Dong Chen. 2021. "An intelligent content caching protocol for connected vehicles." Transactions on Emerging Telecommunications Technologies 32, no. 4: e4231.

Journal article
Published: 15 January 2021 in Accident Analysis & Prevention
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Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network–based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user’s opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems.

ACS Style

Farman Ali; Amjad Ali; Muhammad Imran; Rizwan Ali Naqvi; Muhammad Hameed Siddiqi; Kyung-Sup Kwak. Traffic accident detection and condition analysis based on social networking data. Accident Analysis & Prevention 2021, 151, 105973 .

AMA Style

Farman Ali, Amjad Ali, Muhammad Imran, Rizwan Ali Naqvi, Muhammad Hameed Siddiqi, Kyung-Sup Kwak. Traffic accident detection and condition analysis based on social networking data. Accident Analysis & Prevention. 2021; 151 ():105973.

Chicago/Turabian Style

Farman Ali; Amjad Ali; Muhammad Imran; Rizwan Ali Naqvi; Muhammad Hameed Siddiqi; Kyung-Sup Kwak. 2021. "Traffic accident detection and condition analysis based on social networking data." Accident Analysis & Prevention 151, no. : 105973.

Journal article
Published: 13 January 2021 in IEEE Access
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Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.

ACS Style

Jasneet Kaur; M. Arif Khan; Mohsin Iftikhar; Muhammad Imran; Qazi Emad Ul Haq. Machine Learning Techniques for 5G and Beyond. IEEE Access 2021, 9, 23472 -23488.

AMA Style

Jasneet Kaur, M. Arif Khan, Mohsin Iftikhar, Muhammad Imran, Qazi Emad Ul Haq. Machine Learning Techniques for 5G and Beyond. IEEE Access. 2021; 9 ():23472-23488.

Chicago/Turabian Style

Jasneet Kaur; M. Arif Khan; Mohsin Iftikhar; Muhammad Imran; Qazi Emad Ul Haq. 2021. "Machine Learning Techniques for 5G and Beyond." IEEE Access 9, no. : 23472-23488.

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.