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Prof. Ahmad S. Almogren
King saud university, college of computer and information sciences

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0 Computer Science
0 Cybersecurity
0 Computer Network
0 Medical informatic

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Journal article
Published: 16 February 2021 in Sustainable Cities and Society
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Internet of Things (IoT) consists of a huge number of sensors along with physical things to gather and forward data intelligently. Green IoT applications based on Wireless Sensor Networks (WSNs) are developed in various domains, such as medical, engineering, industry, and smart cities to grow the production. To increase the performance of sustainable cities, communicating nodes are interconnected autonomously to observe the environment, where they need to be more energy-efficient. Edge computing operates in a distributed manner and improves the response time with the least latency through various edge servers. Although the integration of edge computing and Green IoT significantly improves the network performance in terms of computation and data storage, low powered sensors have constraints in terms of battery power, low transmission range, and security aspects. Therefore, adopting an emerging solution is needed to offer energy services with secure data delivery for sustainable cities. This paper presents an intelligent and secure edge-enabled computing (ISEC) model for sustainable cities using Green IoT, which aims to develop the communication strategy with decreasing the liability in terms of energy management and data security for data transportation. The proposed model generates optimal features using deep learning for data routing, which may help to train the sensors for predicting the finest routes toward edge servers. Moreover, the integration of distributed hashing with chaining strategy eases security solutions with efficient computing system. The experimental results reveal the improved performance of the proposed ISEC model against other solutions for energy consumption by 21 %, network throughput by 15 %, end-to-end delay by 12 %, route interruption by 36 %, and network overhead by 52 %.

ACS Style

Khalid Haseeb; Ikram Ud Din; Ahmad Almogren; Imran Ahmed; Mohsen Guizani. Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things. Sustainable Cities and Society 2021, 68, 102779 .

AMA Style

Khalid Haseeb, Ikram Ud Din, Ahmad Almogren, Imran Ahmed, Mohsen Guizani. Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things. Sustainable Cities and Society. 2021; 68 ():102779.

Chicago/Turabian Style

Khalid Haseeb; Ikram Ud Din; Ahmad Almogren; Imran Ahmed; Mohsen Guizani. 2021. "Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things." Sustainable Cities and Society 68, no. : 102779.

Research article
Published: 15 February 2021 in Scientific Programming
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Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.

ACS Style

Nauman Ali Khan; Wuyang Zhou; Mudassar Ali Khan; Ahmad Almogren; Ikram Ud Din. Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks. Scientific Programming 2021, 2021, 1 -10.

AMA Style

Nauman Ali Khan, Wuyang Zhou, Mudassar Ali Khan, Ahmad Almogren, Ikram Ud Din. Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks. Scientific Programming. 2021; 2021 ():1-10.

Chicago/Turabian Style

Nauman Ali Khan; Wuyang Zhou; Mudassar Ali Khan; Ahmad Almogren; Ikram Ud Din. 2021. "Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks." Scientific Programming 2021, no. : 1-10.

Journal article
Published: 11 January 2021 in IEEE Internet of Things Journal
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The pervasiveness of newly introduced Internet of Things (IoT) devices has opened up new opportunities in healthcare systems, for example in facilitating remote patient monitoring. There are, however, security and privacy considerations in the transmission of data from these devices to the back-end server, and across heterogeneous IoT networks. In this study, we propose a novel privacy-preserving scheme which is based on blockchain and swarm exchange techniques to facilitate seamless and secure transmission of user data (e.g. electronic health record-related information) through secured swarm nodes of peer-to-peer communications. BIoTHR refers to the proposed scheme on the private blockchain assisted electronic health record management using IoT. Specifically, new blockchain and swarm exchange infrastructures are suggested as a backbone of the proposed scheme to ensure secure and reliable data transmission and timely monitoring of data sent across IoT networks. An autonomous encryption-decryption mechanism is also utilized, along with a dynamic and modular server assistance technology to deploy EHR transmission in a secure manner. Moreover, several swarm-listen, announcement, peer open and peer closing algorithms are incorporated to employ the actual power of pervasive EHR transmission for better e-healthcare service provisioning. The proposed scheme is developed using the open-source tools of GnuPG, IPFS, and Golang. Proposed study simulates a number of heterogeneous IoT-based health sensor nodes namely body temperature, pulse rate, oxygen saturation i.e. SPO2, galvanic skin response, and blood glucose in blockchain assisted swarm exchange framework. The results reveal that the proposed scheme, in terms of Blockchain-IoT, swarm exchange and EHR transmission, outperforms several peer techniques.

ACS Style

Partha Pratim Ray; Biky Chowhan; Neeraj Kumar; Ahmad Almogren. BIoTHR: Electronic Health Record Servicing Scheme in IoT-Blockchain Ecosystem. IEEE Internet of Things Journal 2021, 8, 10857 -10872.

AMA Style

Partha Pratim Ray, Biky Chowhan, Neeraj Kumar, Ahmad Almogren. BIoTHR: Electronic Health Record Servicing Scheme in IoT-Blockchain Ecosystem. IEEE Internet of Things Journal. 2021; 8 (13):10857-10872.

Chicago/Turabian Style

Partha Pratim Ray; Biky Chowhan; Neeraj Kumar; Ahmad Almogren. 2021. "BIoTHR: Electronic Health Record Servicing Scheme in IoT-Blockchain Ecosystem." IEEE Internet of Things Journal 8, no. 13: 10857-10872.

Journal article
Published: 31 December 2020 in Information Processing & Management
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In Vehicular Ad hoc Networks (VANETs), a large amount of data is shared between vehicles and Road Side Units (RSUs) in real-time. VANETs improve traffic efficiency and reliability by timely sharing road events and traffic information. However, there is a need to tackle the issues of both less data storage capability and selfish behavior of the vehicles. The conventional data storage mechanisms involve a third party for data management and are non-transparent, unreliable, untrustworthy, and insecure. Therefore, a blockchain based data storage system is presented in this paper to overcome the aforementioned issues. The proposed system exploits benefits of an Interplanetary File System (IPFS). Due to the resource constraints of vehicles, the blockchain is implemented on the RSUs. The RSUs get the aggregated packets sent by the vehicles. The packets contain the events’ information that occur in the vehicles’ surroundings. After verifying a packet, RSUs store the information related to the event in IPFS and reputation value of the sender vehicle in the blockchain. The reputation value of a vehicle is calculated based upon the correctness of an event it signs or initiates. Moreover, an incentive mechanism is also proposed to provide monetary incentives to the replier vehicles who respond to the events’ information. The incentives are provided by the initiators after verification of the repliers’ signatures. The initiator is a vehicle who initializes the event. The transactions performed during the incentive process are stored in the blockchain. Finally, Oyente tool is used to analyze the security of the proposed smart contract. A comparison of the proposed scheme with the logistic regression scheme is also presented.

ACS Style

Adia Khalid; Muhammad Sohaib Iftikhar; Ahmad Almogren; Rabiya Khalid; Muhammad Khalil Afzal; Nadeem Javaid. A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs. Information Processing & Management 2020, 58, 102464 .

AMA Style

Adia Khalid, Muhammad Sohaib Iftikhar, Ahmad Almogren, Rabiya Khalid, Muhammad Khalil Afzal, Nadeem Javaid. A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs. Information Processing & Management. 2020; 58 (2):102464.

Chicago/Turabian Style

Adia Khalid; Muhammad Sohaib Iftikhar; Ahmad Almogren; Rabiya Khalid; Muhammad Khalil Afzal; Nadeem Javaid. 2020. "A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs." Information Processing & Management 58, no. 2: 102464.

Journal article
Published: 30 December 2020 in IEEE Access
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The drastic increase in real-time vehicle generated data of various types has imparted a great concept of data trading in vehicular networks. Whereas immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, encounters trading disputes and conflicting interests among trading parties. To address these challenges, we exploit consortium blockchain to maintain transparency and trust in trading activities. Smart contracts are used to tackle trading disputes and illegal actions. Data duplication problem occurs when a dishonest user sell previously traded data multiple times for financial gain. Therefore, data duplication validation is done through previously stored hash-list at roadside units (RSUs) employed with bloom filters for efficient data lookup. Removing data duplication at an earlier stage reduces storage cost. Moreover, an elliptic curve bilinear pairing based digital signature scheme is used to ensure the reliability and integrity of traded data. To ensure persistent availability of traded data, InterPlanetary File System (IPFS) is used, which provides fault-tolerant and a reliable data storage without any single point of failure. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used that hides the energy trading trends. The new account generation for an EV depends upon its traded volume of energy. The experimental results verify the efficiency of the proposed data and energy trading scheme in IoEV with the reliable and secure data storage.

ACS Style

Ayesha Sadiq; Muhammad Umar Javed; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Nadeem Javaid. Blockchain Based Data and Energy Trading in Internet of Electric Vehicles. IEEE Access 2020, 9, 7000 -7020.

AMA Style

Ayesha Sadiq, Muhammad Umar Javed, Rabiya Khalid, Ahmad Almogren, Muhammad Shafiq, Nadeem Javaid. Blockchain Based Data and Energy Trading in Internet of Electric Vehicles. IEEE Access. 2020; 9 ():7000-7020.

Chicago/Turabian Style

Ayesha Sadiq; Muhammad Umar Javed; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Nadeem Javaid. 2020. "Blockchain Based Data and Energy Trading in Internet of Electric Vehicles." IEEE Access 9, no. : 7000-7020.

Corrigendum
Published: 27 December 2020 in Scientific Programming
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In the article titled “Security Measurement in Industrial IoT with Cloud Computing Perspective: Taxonomy, Issues, and Future Directions” [1], there was an error in the third and sixth affiliations. The corrected affiliation list is shown above. In addition, the Acknowledgements section should be updated as follows: “The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs and partially supported by the Faculty of Computer Science and Information Technology, University of Malaya, under Postgraduate Research Grant PG035-2016A.” Copyright © 2020 Sahar Shah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ACS Style

Sahar Shah; Mahnoor Khan; Ahmad Almogren; Ihsan Ali; Lianwen Deng; Heng Luo; Muazzam A. Khan. Corrigendum to “Security Measurement in Industrial IoT with Cloud Computing Perspective: Taxonomy, Issues, and Future Directions”. Scientific Programming 2020, 2020, 1 -1.

AMA Style

Sahar Shah, Mahnoor Khan, Ahmad Almogren, Ihsan Ali, Lianwen Deng, Heng Luo, Muazzam A. Khan. Corrigendum to “Security Measurement in Industrial IoT with Cloud Computing Perspective: Taxonomy, Issues, and Future Directions”. Scientific Programming. 2020; 2020 ():1-1.

Chicago/Turabian Style

Sahar Shah; Mahnoor Khan; Ahmad Almogren; Ihsan Ali; Lianwen Deng; Heng Luo; Muazzam A. Khan. 2020. "Corrigendum to “Security Measurement in Industrial IoT with Cloud Computing Perspective: Taxonomy, Issues, and Future Directions”." Scientific Programming 2020, no. : 1-1.

Journal article
Published: 23 December 2020 in IEEE Access
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In Vehicular Ad-hoc Networks (VANET), vehicles act like mobile nodes for fetching, sharing, and disseminating important information related to vehicle safety, warning messages, emergency events, and passenger infotainment. Due to continuous information sharing of vehicles with their surrounding nodes, Road Side Units (RSUs), and infrastructures, the existing host-centric IP-based network cannot fulfill the requirements of VANETs. Therefore, Information Centric Networking (ICN) architectures are the introduced to comprehensively address the problems of Internet of Things (IoT)-based VANETs, known as VANET-IoT. This paper introduces a new ICN-based proactive left-right-front (LRF) caching strategy for VANETs, which maximizes the performance of VANETs by placing content proactively at the right nodes. The proposed strategy also provides a mechanism for the timely dissemination of safety-related messages. LRF is compared with other caching strategies in the NS-3 simulator, which outperforms those schemes in terms of cache utilization, hop ratios, and resolved interest ratios with respect to 100 MB, 500 MB, and 1 GB cache sizes.

ACS Style

Ikram Ud Din; Bilal Ahmad; Ahmad Almogren; Hisham Almajed; Irfan Mohiuddin; Joel J. P. C. Rodrigues. Left-Right-Front Caching Strategy for Vehicular Networks in ICN-Based Internet of Things. IEEE Access 2020, 9, 595 -605.

AMA Style

Ikram Ud Din, Bilal Ahmad, Ahmad Almogren, Hisham Almajed, Irfan Mohiuddin, Joel J. P. C. Rodrigues. Left-Right-Front Caching Strategy for Vehicular Networks in ICN-Based Internet of Things. IEEE Access. 2020; 9 ():595-605.

Chicago/Turabian Style

Ikram Ud Din; Bilal Ahmad; Ahmad Almogren; Hisham Almajed; Irfan Mohiuddin; Joel J. P. C. Rodrigues. 2020. "Left-Right-Front Caching Strategy for Vehicular Networks in ICN-Based Internet of Things." IEEE Access 9, no. : 595-605.

Journal article
Published: 04 December 2020 in IEEE Access
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Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts’ involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches.

ACS Style

Zeeshan Aslam; Fahad Ahmed; Ahmad Almogren; Muhammad Shafiq; Mansour Zuair; Nadeem Javaid. An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems. IEEE Access 2020, 8, 221767 -221782.

AMA Style

Zeeshan Aslam, Fahad Ahmed, Ahmad Almogren, Muhammad Shafiq, Mansour Zuair, Nadeem Javaid. An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems. IEEE Access. 2020; 8 (99):221767-221782.

Chicago/Turabian Style

Zeeshan Aslam; Fahad Ahmed; Ahmad Almogren; Muhammad Shafiq; Mansour Zuair; Nadeem Javaid. 2020. "An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems." IEEE Access 8, no. 99: 221767-221782.

Research article
Published: 03 December 2020 in Journal of Healthcare Engineering
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For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

ACS Style

Nighat Bibi; Misba Sikandar; Ikram Ud Din; Ahmad Almogren; Sikandar Ali. IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. Journal of Healthcare Engineering 2020, 2020, 1 -12.

AMA Style

Nighat Bibi, Misba Sikandar, Ikram Ud Din, Ahmad Almogren, Sikandar Ali. IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. Journal of Healthcare Engineering. 2020; 2020 ():1-12.

Chicago/Turabian Style

Nighat Bibi; Misba Sikandar; Ikram Ud Din; Ahmad Almogren; Sikandar Ali. 2020. "IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning." Journal of Healthcare Engineering 2020, no. : 1-12.

Research article
Published: 03 December 2020 in Scientific Programming
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Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the system first removes the skewness of the text page, and then the page is converted into lines and ligatures. The algorithm is evaluated on manually generated Urdu printed and handwritten dataset. The proposed algorithm is tested separately on handwritten and printed text, showing 96.7% and 98.3% line accuracy, respectively. Furthermore, the proposed line segmentation algorithm correctly extracts the lines when tested on Arabic text.

ACS Style

Saud Malik; Ahthasham Sajid; Arshad Ahmad; Ahmad Almogren; Bashir Hayat; Muhammad Awais; Kyong Hoon Kim. An Efficient Skewed Line Segmentation Technique for Cursive Script OCR. Scientific Programming 2020, 2020, 1 -12.

AMA Style

Saud Malik, Ahthasham Sajid, Arshad Ahmad, Ahmad Almogren, Bashir Hayat, Muhammad Awais, Kyong Hoon Kim. An Efficient Skewed Line Segmentation Technique for Cursive Script OCR. Scientific Programming. 2020; 2020 ():1-12.

Chicago/Turabian Style

Saud Malik; Ahthasham Sajid; Arshad Ahmad; Ahmad Almogren; Bashir Hayat; Muhammad Awais; Kyong Hoon Kim. 2020. "An Efficient Skewed Line Segmentation Technique for Cursive Script OCR." Scientific Programming 2020, no. : 1-12.

Journal article
Published: 01 December 2020 in Computer Standards & Interfaces
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Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient.

ACS Style

Malik Ali Judge; Awais Manzoor; Hasan Ali Khattak; Ikram Ud Din; Ahmad Almogren; Muhammad Adnan. Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things. Computer Standards & Interfaces 2020, 77, 103500 .

AMA Style

Malik Ali Judge, Awais Manzoor, Hasan Ali Khattak, Ikram Ud Din, Ahmad Almogren, Muhammad Adnan. Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things. Computer Standards & Interfaces. 2020; 77 ():103500.

Chicago/Turabian Style

Malik Ali Judge; Awais Manzoor; Hasan Ali Khattak; Ikram Ud Din; Ahmad Almogren; Muhammad Adnan. 2020. "Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things." Computer Standards & Interfaces 77, no. : 103500.

Journal article
Published: 30 November 2020 in Sensors
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Recently, many platforms have outsourced tasks to numerous smartphone devices known as Mobile Crowd-sourcing System (MCS). The data is collected and transferred to the platform for further analysis and processing. These data needs to maintain confidentiality while moving from smartphones to the platform. Moreover, the limitations of computation resources in smartphones need to be addressed to balance the confidentiality of the data and the capabilities of the devices. For this reason, elliptic curve cryptography (ECC) is accepted, widespread, and suitable for use in limited resources environments such as smartphone devices. ECC reduces energy consumption and maximizes devices’ efficiency by using small crypto keys with the same strength of the required cryptography of other cryptosystems. Thus, ECC is the preferred approach for many environments, including the MCS, Internet of Things (IoT) and wireless sensor networks (WSNs). Many implementations of ECC increase the process of encryption and/or increase the space overhead by, for instance, incorrectly mapping points to EC with extra padding bits. Moreover, the wrong mapping method used in ECC results in increasing the computation efforts. This study provides comprehensive details about the mapping techniques used in the ECC mapping phase, and presents performance results about widely used elliptic curves. In addition, it suggests an optimal enhanced mapping method and size of padding bit to secure communications that guarantee the successful mapping of points to EC and reduce the size of padding bits.

ACS Style

Hisham Almajed; Ahmad Almogren; Mohammed Alabdulkareem. iTrust—A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography. Sensors 2020, 20, 6841 .

AMA Style

Hisham Almajed, Ahmad Almogren, Mohammed Alabdulkareem. iTrust—A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography. Sensors. 2020; 20 (23):6841.

Chicago/Turabian Style

Hisham Almajed; Ahmad Almogren; Mohammed Alabdulkareem. 2020. "iTrust—A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography." Sensors 20, no. 23: 6841.

Research article
Published: 30 November 2020 in Complexity
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Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree (CDT), Cost-Sensitive Decision Forest (CS-Forest), Decision Stump (DS), Forest by Penalizing Attributes (Forest-PA), Hoeffding Tree (HT), Decision Tree (J48), Logistic Model Tree (LMT), Random Forest (RF), Random Tree (RT), and REP-Tree (REP-T). Performance of each technique is evaluated using different measures, i.e., mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), root relative squared error (RRSE), specificity, precision, recall, F-measure (FM), G-measure (GM), Matthew’s correlation coefficient (MCC), and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified.

ACS Style

Rashid Naseem; Bilal Khan; Arshad Ahmad; Ahmad Almogren; Saima Jabeen; Bashir Hayat; Muhammad Arif Shah. Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects. Complexity 2020, 2020, 1 -21.

AMA Style

Rashid Naseem, Bilal Khan, Arshad Ahmad, Ahmad Almogren, Saima Jabeen, Bashir Hayat, Muhammad Arif Shah. Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects. Complexity. 2020; 2020 ():1-21.

Chicago/Turabian Style

Rashid Naseem; Bilal Khan; Arshad Ahmad; Ahmad Almogren; Saima Jabeen; Bashir Hayat; Muhammad Arif Shah. 2020. "Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects." Complexity 2020, no. : 1-21.

Research article
Published: 30 November 2020 in Scientific Programming
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Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks. The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT). Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection. We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks. Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla. Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location. This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework. It highlights the secret relationship of users, which does not exist in real data. The proposed framework consists of two major parts. Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties. Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values. Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures. In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity. The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.

ACS Style

Nauman Ali Khan; Sihai Zhang; Wuyang Zhou; Ahmad Almogren; Ikram Ud Din; Muhammad Asif. Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties. Scientific Programming 2020, 2020, 1 -16.

AMA Style

Nauman Ali Khan, Sihai Zhang, Wuyang Zhou, Ahmad Almogren, Ikram Ud Din, Muhammad Asif. Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties. Scientific Programming. 2020; 2020 ():1-16.

Chicago/Turabian Style

Nauman Ali Khan; Sihai Zhang; Wuyang Zhou; Ahmad Almogren; Ikram Ud Din; Muhammad Asif. 2020. "Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties." Scientific Programming 2020, no. : 1-16.

Review
Published: 24 November 2020 in IEEE Access
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The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure the coverage of all important studies. This methodology was detailed and repeatable. The review outlined the existing proposed contributions, datasets utilised, network forensic methods utilised and research focus of the primary selected studies. The demographic characteristics of primary studies were also outlined.The result of this review revealed that research in this domain is gaining momentum, particularly in the last 3 years (2018-2020). Nine key contributions were also identified, with Evaluation, System, and Model being the most conducted.

ACS Style

Ihsan Ali; Abdelmuttlib Ibrahim Abdalla Ahmed; Ahmad Almogren; Muhammad Ahsan Raza; Syed Attique Shah; Anwar Khan; Abdullah Gani. Systematic Literature Review on IoT-Based Botnet Attack. IEEE Access 2020, 8, 212220 -212232.

AMA Style

Ihsan Ali, Abdelmuttlib Ibrahim Abdalla Ahmed, Ahmad Almogren, Muhammad Ahsan Raza, Syed Attique Shah, Anwar Khan, Abdullah Gani. Systematic Literature Review on IoT-Based Botnet Attack. IEEE Access. 2020; 8 (99):212220-212232.

Chicago/Turabian Style

Ihsan Ali; Abdelmuttlib Ibrahim Abdalla Ahmed; Ahmad Almogren; Muhammad Ahsan Raza; Syed Attique Shah; Anwar Khan; Abdullah Gani. 2020. "Systematic Literature Review on IoT-Based Botnet Attack." IEEE Access 8, no. 99: 212220-212232.

Conference paper
Published: 08 November 2020 in 2020 International Conference on Decision Aid Sciences and Application (DASA)
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Business processes are indicating how to handle and deal with different business situations. Companies focus on the flexibility of information technology architecture as their main business strategy for decision making. Their systems should satisfy the adaptability criteria. In this paper, we propose a framework to measure the quality of the existing workflow by using the quality of services criteria. Based on the results of quality of services measurements, an enhancement of the framework should be obtained using the concepts of graph theory such as min-cut and max-cut algorithms and some re-factoring techniques. To reduce the bugs that could occur and to reach the long lifespan for an organization workflow, we proposed, designed, and implemented a new mainframe that takes a workflow as an input to check whether it meets the specific quality of services measurements. If the workflow is not qualified, it will be enhanced automatically using our proposed algorithms to meet the quality of services rules and check for the consistency within the system. The main frameworks components are the quality measurement, determination, parsing, and code adaptation. We presented and discussed a real case study to help in understanding, illustrating and analyzing the proposed framework behavior and its approach applicability, in addition to, analyzing different related scenarios.

ACS Style

Adel Almalki; Irfan Mohiuddin; Ahmad S. AlMogren; Ahmed Ghoneim. Building A New Blueprint for Operating Workflow Efficiently. 2020 International Conference on Decision Aid Sciences and Application (DASA) 2020, 238 -244.

AMA Style

Adel Almalki, Irfan Mohiuddin, Ahmad S. AlMogren, Ahmed Ghoneim. Building A New Blueprint for Operating Workflow Efficiently. 2020 International Conference on Decision Aid Sciences and Application (DASA). 2020; ():238-244.

Chicago/Turabian Style

Adel Almalki; Irfan Mohiuddin; Ahmad S. AlMogren; Ahmed Ghoneim. 2020. "Building A New Blueprint for Operating Workflow Efficiently." 2020 International Conference on Decision Aid Sciences and Application (DASA) , no. : 238-244.

Journal article
Published: 03 November 2020 in Energies
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Smartness and agility are two quality measures that are pragmatic to achieve a flexible, maintainable, and adaptable system in any business. The automotive industry also requires an enhanced performance matrix and refinement in the development strategies for manufacturing. The current development models used in automotive manufacturing are not optimal enough; thus, the overall expenditure is not properly managed. Therefore, it is essential to come up with flexible, agile techniques incorporating traceability methods. It overcomes the traditional manufacturing approaches that are usually inflexible, costly, and lack timely customer feedback. The article focuses on significant Requirements Management (RM) activities, including traceability mechanism, smart manufacturing process, and performance evaluation of the proposed methods in the automotive domain. We propose a manufacturing framework that follows smart agile principles along with proper traceability management. Our proposed approach overcomes the complexities generated by traditional manufacturing processes in automotive industries. It gives an insight into the future manufacturing processes in the automotive industries.

ACS Style

Gullelala Jadoon; Ikram Ud Din; Ahmad Almogren; Hisham Almajed. Smart and Agile Manufacturing Framework, A Case Study for Automotive Industry. Energies 2020, 13, 5766 .

AMA Style

Gullelala Jadoon, Ikram Ud Din, Ahmad Almogren, Hisham Almajed. Smart and Agile Manufacturing Framework, A Case Study for Automotive Industry. Energies. 2020; 13 (21):5766.

Chicago/Turabian Style

Gullelala Jadoon; Ikram Ud Din; Ahmad Almogren; Hisham Almajed. 2020. "Smart and Agile Manufacturing Framework, A Case Study for Automotive Industry." Energies 13, no. 21: 5766.

Journal article
Published: 03 November 2020 in Sensors
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The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

ACS Style

Muhammad Dawood Khan; Zahid Ullah; Arshad Ahmad; Bashir Hayat; Ahmad Almogren; Kyong Hoon Kim; Muhammad Ilyas; Muhammad Ali. Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. Sensors 2020, 20, 6267 .

AMA Style

Muhammad Dawood Khan, Zahid Ullah, Arshad Ahmad, Bashir Hayat, Ahmad Almogren, Kyong Hoon Kim, Muhammad Ilyas, Muhammad Ali. Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. Sensors. 2020; 20 (21):6267.

Chicago/Turabian Style

Muhammad Dawood Khan; Zahid Ullah; Arshad Ahmad; Bashir Hayat; Ahmad Almogren; Kyong Hoon Kim; Muhammad Ilyas; Muhammad Ali. 2020. "Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN." Sensors 20, no. 21: 6267.

Journal article
Published: 29 October 2020 in Sensors
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Recent growth in the Internet of Things (IoT) has raised security concerns over the confidentiality of data exchanged between IoT devices and the edge. Many IoT systems adopt asymmetric cryptography to secure their data and communications. A drawback of asymmetric cryptography is the sizeable computation and space requirements. However, elliptic curve cryptography (ECC) is widely used in constrained environments for asymmetric cryptography due its superiority in generating a powerful encryption mechanism with small key sizes. ECC increases device performance and lowers power consumption, meaning it is suitable for diverse applications ranging from the IoT to wireless sensor network (WSN) devices. To ensure the confidentiality and security of data and communications, it is necessary to implement ECC robustly. A special area of focus in this regard is the mapping phase. This study’s objective was to propose a tested and trusted scheme that offers authenticated encryption (AE) via enhancing the mapping phase of a plain text to an elliptic curve to resist several encryption attacks such as Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA). The proposed scheme also undertakes evaluation and analysis related to security requirements for specific encryption attributes. Finally, results from a comparison of the proposed scheme and other schemes are presented, evaluating each one’s security characteristics and performance measurements. Our scheme is efficient in a way that makes so suitable to the IoT, and in particular to the Industrial IoT and the new Urbanization where the demands for services are huge.

ACS Style

Hisham Almajed; Ahmad Almogren. A Secure and Efficient ECC-Based Scheme for Edge Computing and Internet of Things. Sensors 2020, 20, 6158 .

AMA Style

Hisham Almajed, Ahmad Almogren. A Secure and Efficient ECC-Based Scheme for Edge Computing and Internet of Things. Sensors. 2020; 20 (21):6158.

Chicago/Turabian Style

Hisham Almajed; Ahmad Almogren. 2020. "A Secure and Efficient ECC-Based Scheme for Edge Computing and Internet of Things." Sensors 20, no. 21: 6158.

Journal article
Published: 29 October 2020 in Sensors
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Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities.

ACS Style

Kamran Awan; Ikram Ud Din; Ahmad Almogren; Hisham Almajed. AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things. Sensors 2020, 20, 6174 .

AMA Style

Kamran Awan, Ikram Ud Din, Ahmad Almogren, Hisham Almajed. AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things. Sensors. 2020; 20 (21):6174.

Chicago/Turabian Style

Kamran Awan; Ikram Ud Din; Ahmad Almogren; Hisham Almajed. 2020. "AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things." Sensors 20, no. 21: 6174.