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Dr. Mohammad Kamrul Hasan is working as a Senior Lecturer, in the Center for Cyber Security, Universiti Kebangsaan Malaysia. His research interest is in the area of: Communication Engineering, Internet of Things, 5G, Vehicular Networks, smart grid networks, Microprocessor and Interfacing, and Cyber Physical Security. He is a Senior Member of the Institute of Electrical and Electronics Engineers, USA. Dr. Mohammad has the strong motivation to contribute to good working and social environment.
Project Goal: Seamless Mobility
Current Stage: Data Collection and Related Assessments
Project Goal: Design and Development of the novel dynamic self defender model to solve the cyber security issues in smart grid IoT network
Current Stage: Data Collection and sampling is completed
Project Goal: Communication Protocol Design
Current Stage: Protocol Modeling
Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.
Taher M. Ghazal; Mohammad Kamrul Hasan; Muhammad Turki Alshurideh; Haitham M. Alzoubi; Munir Ahmad; Syed Shehryar Akbar; Barween Al Kurdi; Iman A. Akour. IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review. Future Internet 2021, 13, 218 .
AMA StyleTaher M. Ghazal, Mohammad Kamrul Hasan, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Munir Ahmad, Syed Shehryar Akbar, Barween Al Kurdi, Iman A. Akour. IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review. Future Internet. 2021; 13 (8):218.
Chicago/Turabian StyleTaher M. Ghazal; Mohammad Kamrul Hasan; Muhammad Turki Alshurideh; Haitham M. Alzoubi; Munir Ahmad; Syed Shehryar Akbar; Barween Al Kurdi; Iman A. Akour. 2021. "IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review." Future Internet 13, no. 8: 218.
Renewable energy is in high demand for a balanced ecosystem. There are different types of energy storage systems available for long-term energy storage, lithium-ion battery is one of the most powerful and being a popular choice of storage. This review paper discusses various aspects of lithium-ion batteries based on a review of 420 published research papers at the initial stage through 101 published research articles that have been finally reviewed. This review paper focuses on several topics, including electrical vehicle (EV) systems, energy management systems, challenges and issues, and the conclusions and recommendations for future work. EV systems discuss all components that are included in producing the lithium-ion battery. The energy storage section contains the batteries, super capacitors, fuel cells, hybrid storage, power, temperature, and heat management. Energy management systems consider battery monitoring for current and voltage, battery charge-discharge control, estimation and protection, cell equalization. This paper's challenges and issues discuss some of the critical aspects of lithium-ion batteries, including temperature and safety, life-cycle and memory effects, environmental effects, and recycling processes. The conclusion and recommendation of this paper indicate the future scope of research. This review paper can provide the lithium-ion battery's insight, overall synopsis and contribution, and further research directions to the EV system.
Mohammad Kamrul Hasan; Mahmud; A.K.M. Ahasan Habib; S.M.A. Motakabber; Shayla Islam. Review of electric vehicle energy storage and management system: Standards, issues, and challenges. Journal of Energy Storage 2021, 41, 102940 .
AMA StyleMohammad Kamrul Hasan, Mahmud, A.K.M. Ahasan Habib, S.M.A. Motakabber, Shayla Islam. Review of electric vehicle energy storage and management system: Standards, issues, and challenges. Journal of Energy Storage. 2021; 41 ():102940.
Chicago/Turabian StyleMohammad Kamrul Hasan; Mahmud; A.K.M. Ahasan Habib; S.M.A. Motakabber; Shayla Islam. 2021. "Review of electric vehicle energy storage and management system: Standards, issues, and challenges." Journal of Energy Storage 41, no. : 102940.
Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.
Imran Memon; Mohammad Hasan; Riaz Shaikh; Jamel Nebhen; Khairul Bakar; Eklas Hossain; Muhammad Tunio. Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks. Electronics 2021, 10, 1068 .
AMA StyleImran Memon, Mohammad Hasan, Riaz Shaikh, Jamel Nebhen, Khairul Bakar, Eklas Hossain, Muhammad Tunio. Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks. Electronics. 2021; 10 (9):1068.
Chicago/Turabian StyleImran Memon; Mohammad Hasan; Riaz Shaikh; Jamel Nebhen; Khairul Bakar; Eklas Hossain; Muhammad Tunio. 2021. "Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks." Electronics 10, no. 9: 1068.
As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.
Mohammad Kamrul Hasan; Muhammad Shafiq; Shayla Islam; Bishwajeet Pandey; Yousef A. Baker El-Ebiary; Nazmus Shaker Nafi; R. Ciro Rodriguez; Doris Esenarro Vargas. Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications. Complexity 2021, 2021, 1 -13.
AMA StyleMohammad Kamrul Hasan, Muhammad Shafiq, Shayla Islam, Bishwajeet Pandey, Yousef A. Baker El-Ebiary, Nazmus Shaker Nafi, R. Ciro Rodriguez, Doris Esenarro Vargas. Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications. Complexity. 2021; 2021 ():1-13.
Chicago/Turabian StyleMohammad Kamrul Hasan; Muhammad Shafiq; Shayla Islam; Bishwajeet Pandey; Yousef A. Baker El-Ebiary; Nazmus Shaker Nafi; R. Ciro Rodriguez; Doris Esenarro Vargas. 2021. "Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications." Complexity 2021, no. : 1-13.
The energy efficiency and spectrum shortage problem of wireless devices has become a concern for researchers worldwide as the number of wireless devices increases at an unparalleled speed. Many new solutions have been proposed to extend mobile devices' battery life, such as wireless energy harvesting from traditional radio frequency signals to design new smart battery chips. This paper considers a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. First, the expression of outage probability is theoretically derived for uplink and downlink scenarios. Moreover, maximum energy efficiency for both uplink and downlink in the cognitive radio network model subject to interference and noise is investigated here. The theoretical analysis is then evaluated. It has been observed that outage probability improves low harvested power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result signifies that energy efficiency is improved using optimum power for uplink and downlink scenarios.
Mohammad Kamrul Kamrul Hasan; Monwar Jahan Chowdhury; Shakil Ahmed; Saifur Rahman Sabuj; Jamel Nibhen; Khairul Azmi Abu Bakar. Design an Optimum Energy Harvesting Model for Bidirectional Cognitive Radio Networks. 2021, 1 .
AMA StyleMohammad Kamrul Kamrul Hasan, Monwar Jahan Chowdhury, Shakil Ahmed, Saifur Rahman Sabuj, Jamel Nibhen, Khairul Azmi Abu Bakar. Design an Optimum Energy Harvesting Model for Bidirectional Cognitive Radio Networks. . 2021; ():1.
Chicago/Turabian StyleMohammad Kamrul Kamrul Hasan; Monwar Jahan Chowdhury; Shakil Ahmed; Saifur Rahman Sabuj; Jamel Nibhen; Khairul Azmi Abu Bakar. 2021. "Design an Optimum Energy Harvesting Model for Bidirectional Cognitive Radio Networks." , no. : 1.
This study presents a novel multilevel inverter structure that can operate in both switched capacitor and asymmetric DC source modes. In the first mode, it can produce seven-level output voltage employing two switched capacitors and one single DC supply. The five-level output voltage is produced while operating the second mode. The voltage ratio between the input and output voltage for the capacitor mode is 1:3 (triple voltage gain). During the first mode, the capacitor of the inverter is self -balanced whereas the inverter can produce higher voltage output in the DC source mode. The proposed inverter reduces the total standing voltage in both modes of operations as it can generate the output voltage without requiring any additional H-bridge circuit. The feasibility and predominate features of the proposed inverter have been established by comparing with existing topologies in terms of power components count. Results obtained from this study are validated using simulation employing sinusoidal pulse width modulation (SPWM). A hardware prototype has also been developed for further validation.
Sheikh Tanzim Meraj; Mohammad Kamrul Hasan; Jahedul Islam; Yousef A. Baker El-Ebiary; Jamel Nebhen; Moinul Hossain; Khorshed Alam; Nguyen Vo. A Diamond Shaped Multilevel Inverter With Dual Mode of Operation. IEEE Access 2021, 9, 59873 -59887.
AMA StyleSheikh Tanzim Meraj, Mohammad Kamrul Hasan, Jahedul Islam, Yousef A. Baker El-Ebiary, Jamel Nebhen, Moinul Hossain, Khorshed Alam, Nguyen Vo. A Diamond Shaped Multilevel Inverter With Dual Mode of Operation. IEEE Access. 2021; 9 (99):59873-59887.
Chicago/Turabian StyleSheikh Tanzim Meraj; Mohammad Kamrul Hasan; Jahedul Islam; Yousef A. Baker El-Ebiary; Jamel Nebhen; Moinul Hossain; Khorshed Alam; Nguyen Vo. 2021. "A Diamond Shaped Multilevel Inverter With Dual Mode of Operation." IEEE Access 9, no. 99: 59873-59887.
Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.
Elmustafa Sayed Ali; Mohammad Kamrul Hasan; Rosilah Hassan; Rashid A. Saeed; Mona Bakri Hassan; Shayla Islam; Nazmus Shaker Nafi; Savitri Bevinakoppa. Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications. Security and Communication Networks 2021, 2021, 1 -23.
AMA StyleElmustafa Sayed Ali, Mohammad Kamrul Hasan, Rosilah Hassan, Rashid A. Saeed, Mona Bakri Hassan, Shayla Islam, Nazmus Shaker Nafi, Savitri Bevinakoppa. Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications. Security and Communication Networks. 2021; 2021 ():1-23.
Chicago/Turabian StyleElmustafa Sayed Ali; Mohammad Kamrul Hasan; Rosilah Hassan; Rashid A. Saeed; Mona Bakri Hassan; Shayla Islam; Nazmus Shaker Nafi; Savitri Bevinakoppa. 2021. "Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications." Security and Communication Networks 2021, no. : 1-23.
Timing synchronization has a vital role in swarm drones’ network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones’ network, master drones’ (MD) neighbor drone’s timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.
Fawaz Alsolami; Fahad A. Alqurashi; Mohammad Kamrul Hasan; Rashid A. Saeed; S. Abdel-Khalek; Anis Ben Ishak. Development of Self-Synchronized Drones’ Network using Cluster-based Swarm Intelligence Approach. IEEE Access 2021, PP, 1 -1.
AMA StyleFawaz Alsolami, Fahad A. Alqurashi, Mohammad Kamrul Hasan, Rashid A. Saeed, S. Abdel-Khalek, Anis Ben Ishak. Development of Self-Synchronized Drones’ Network using Cluster-based Swarm Intelligence Approach. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleFawaz Alsolami; Fahad A. Alqurashi; Mohammad Kamrul Hasan; Rashid A. Saeed; S. Abdel-Khalek; Anis Ben Ishak. 2021. "Development of Self-Synchronized Drones’ Network using Cluster-based Swarm Intelligence Approach." IEEE Access PP, no. 99: 1-1.
Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms.
Ravie Chandren Muniyandi; Mohammad Kamrul Hasan; Mustafa Raad Hammoodi; Ali Maroosi. An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET. Journal of Advanced Transportation 2021, 2021, 1 -17.
AMA StyleRavie Chandren Muniyandi, Mohammad Kamrul Hasan, Mustafa Raad Hammoodi, Ali Maroosi. An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET. Journal of Advanced Transportation. 2021; 2021 ():1-17.
Chicago/Turabian StyleRavie Chandren Muniyandi; Mohammad Kamrul Hasan; Mustafa Raad Hammoodi; Ali Maroosi. 2021. "An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET." Journal of Advanced Transportation 2021, no. : 1-17.
The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques.
Mohammad Kamrul Hasan; Shayla Islam; Rossilawati Sulaiman; Sheroz Khan; Aisha-Hassan Abdalla Hashim; Shabana Habib; Mohammad Islam; Saleh Alyahya; Musse Mohamed Ahmed; Samar Kamil; Arif Hassan. Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications. IEEE Access 2021, 9, 47731 -47742.
AMA StyleMohammad Kamrul Hasan, Shayla Islam, Rossilawati Sulaiman, Sheroz Khan, Aisha-Hassan Abdalla Hashim, Shabana Habib, Mohammad Islam, Saleh Alyahya, Musse Mohamed Ahmed, Samar Kamil, Arif Hassan. Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications. IEEE Access. 2021; 9 (99):47731-47742.
Chicago/Turabian StyleMohammad Kamrul Hasan; Shayla Islam; Rossilawati Sulaiman; Sheroz Khan; Aisha-Hassan Abdalla Hashim; Shabana Habib; Mohammad Islam; Saleh Alyahya; Musse Mohamed Ahmed; Samar Kamil; Arif Hassan. 2021. "Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications." IEEE Access 9, no. 99: 47731-47742.
This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.
Mijanur Rahman; Mohammad Shakeri; Sieh Tiong; Fatema Khatun; Nowshad Amin; Jagadeesh Pasupuleti; Mohammad Hasan. Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks. Sustainability 2021, 13, 2393 .
AMA StyleMijanur Rahman, Mohammad Shakeri, Sieh Tiong, Fatema Khatun, Nowshad Amin, Jagadeesh Pasupuleti, Mohammad Hasan. Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks. Sustainability. 2021; 13 (4):2393.
Chicago/Turabian StyleMijanur Rahman; Mohammad Shakeri; Sieh Tiong; Fatema Khatun; Nowshad Amin; Jagadeesh Pasupuleti; Mohammad Hasan. 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks." Sustainability 13, no. 4: 2393.
Power electronics devices are made from semiconductor switches such as thyristors, MOSFETs, and diodes, along with passive elements of inductors, capacitors, and resistors, and integrated circuits. They are heavily used in power processing for applications in computing, communication, medical electronics, appliance control, and as converters in high power DC and AC transmission in what is now called harmonized AC/DC networks. A converter's operation is described as a periodic sequencing of different modes of operation corresponding to different topologies interfaced to filters made of passive elements. The performance of converters has improved considerably using high switching frequency, which leads to a significant improvement in a power converter's performance. However, the high dv/dt through a fast-switching transient of the MOSFET is associated with parasitic components generating oscillations and voltage spikes having adverse effects on the operation of complementary switches, thereby affecting the safe operation of the power devices. In this paper, the MOSFET gate-driver circuit performance is improved to suppress the H-Bridge inverter's voltage spikes. The proposed technique is a simple improvement to the gate driver based on the IR2112 driver (IC) by adding a capacitor to attenuate the effect of parasitic components and the freewheeling current, suppressing the negative voltage spikes. This paper’s main contribution is to improve the gate driver circuit's capability for suppressing the voltage spikes in the H-Bridge inverter. The improved gate driver circuit is validated experimentally and is compared with the conventional gate driver. The experimental results show that the proposed technique can effectively suppress the MOSFET’s voltage spikes and oscillations.
Ezzidin Aboadla; Sheroz Khan; Kushsairy Kadir; Zulkhairi Yusof; Mohamed Habaebi; Shabana Habib; Muhammad Islam; Mohammad Hasan; Eklas Hossain. Suppressing Voltage Spikes of MOSFET in H-Bridge Inverter Circuit. Electronics 2021, 10, 390 .
AMA StyleEzzidin Aboadla, Sheroz Khan, Kushsairy Kadir, Zulkhairi Yusof, Mohamed Habaebi, Shabana Habib, Muhammad Islam, Mohammad Hasan, Eklas Hossain. Suppressing Voltage Spikes of MOSFET in H-Bridge Inverter Circuit. Electronics. 2021; 10 (4):390.
Chicago/Turabian StyleEzzidin Aboadla; Sheroz Khan; Kushsairy Kadir; Zulkhairi Yusof; Mohamed Habaebi; Shabana Habib; Muhammad Islam; Mohammad Hasan; Eklas Hossain. 2021. "Suppressing Voltage Spikes of MOSFET in H-Bridge Inverter Circuit." Electronics 10, no. 4: 390.
Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi ( $\pi$ ) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved $\pi $ model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.
Mohammad Kamrul Hasan; Musse Mohamud Ahmed; Sherfriz Sherry Musa; Shayla Islam; Siti Norul Huda Sheikh Abdullah; Eklas Hossain; Nazmus Shaker Nafi; Nguyen Vo. An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System. IEEE Access 2021, 9, 14446 -14458.
AMA StyleMohammad Kamrul Hasan, Musse Mohamud Ahmed, Sherfriz Sherry Musa, Shayla Islam, Siti Norul Huda Sheikh Abdullah, Eklas Hossain, Nazmus Shaker Nafi, Nguyen Vo. An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System. IEEE Access. 2021; 9 ():14446-14458.
Chicago/Turabian StyleMohammad Kamrul Hasan; Musse Mohamud Ahmed; Sherfriz Sherry Musa; Shayla Islam; Siti Norul Huda Sheikh Abdullah; Eklas Hossain; Nazmus Shaker Nafi; Nguyen Vo. 2021. "An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System." IEEE Access 9, no. : 14446-14458.
The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.
Nahla Nurelmadina; Mohammad Hasan; Imran Memon; Rashid Saeed; Khairul Zainol Ariffin; Elmustafa Ali; Rania Mokhtar; Shayla Islam; Eklas Hossain; Arif Hassan. A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications. Sustainability 2021, 13, 338 .
AMA StyleNahla Nurelmadina, Mohammad Hasan, Imran Memon, Rashid Saeed, Khairul Zainol Ariffin, Elmustafa Ali, Rania Mokhtar, Shayla Islam, Eklas Hossain, Arif Hassan. A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications. Sustainability. 2021; 13 (1):338.
Chicago/Turabian StyleNahla Nurelmadina; Mohammad Hasan; Imran Memon; Rashid Saeed; Khairul Zainol Ariffin; Elmustafa Ali; Rania Mokhtar; Shayla Islam; Eklas Hossain; Arif Hassan. 2021. "A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications." Sustainability 13, no. 1: 338.
Siti Norul Huda Sheikh Abdullah; Saad M. Ismail; Mohammad Kamrul Hasan; Palaiahnakote Shivakumara. Novel Adaptive Binarization Method for Degraded Document Images. Computers, Materials & Continua 2021, 67, 3815 -3832.
AMA StyleSiti Norul Huda Sheikh Abdullah, Saad M. Ismail, Mohammad Kamrul Hasan, Palaiahnakote Shivakumara. Novel Adaptive Binarization Method for Degraded Document Images. Computers, Materials & Continua. 2021; 67 (3):3815-3832.
Chicago/Turabian StyleSiti Norul Huda Sheikh Abdullah; Saad M. Ismail; Mohammad Kamrul Hasan; Palaiahnakote Shivakumara. 2021. "Novel Adaptive Binarization Method for Degraded Document Images." Computers, Materials & Continua 67, no. 3: 3815-3832.
The prominent electric vehicle technology, energy storage system, and voltage balancing circuits are most important in the automation industry for the global environment and economic issues. The energy storage system has a great demand for their high specific energy and power, high‐temperature tolerance, and long lifetime in the electric vehicle market. For reducing the individual battery or super capacitor cell‐damaging change, capacitive loss over the charging or discharging time and prolong the lifetime on the string, the cell balancing is compulsory. The electric vehicles drive train architecture, overall applicable energy storage system, and the balancing circuit categories as cell‐to‐heat, cell‐to‐cell, cell‐to‐pack, pack‐to‐cell, and cell‐to‐pack‐to‐cell are reviewed. The comparative study has shown the different key factors of market available electric vehicles, different types of energy storage systems, and voltage balancing circuits. The study will help the researcher improve the high efficient energy storage system and balancing circuit that is highly applicable to the electric vehicle.
A. K. M. Ahasan Habib; Mohammad Kamrul Hasan; Mahmud; S. M. A. Motakabber; Muhammad I. Ibrahimya; Shayla Islam. A review: Energy storage system and balancing circuits for electric vehicle application. IET Power Electronics 2020, 14, 1 -13.
AMA StyleA. K. M. Ahasan Habib, Mohammad Kamrul Hasan, Mahmud, S. M. A. Motakabber, Muhammad I. Ibrahimya, Shayla Islam. A review: Energy storage system and balancing circuits for electric vehicle application. IET Power Electronics. 2020; 14 (1):1-13.
Chicago/Turabian StyleA. K. M. Ahasan Habib; Mohammad Kamrul Hasan; Mahmud; S. M. A. Motakabber; Muhammad I. Ibrahimya; Shayla Islam. 2020. "A review: Energy storage system and balancing circuits for electric vehicle application." IET Power Electronics 14, no. 1: 1-13.