Browsing by Author "Navimipour, Nima Jafari"

Now showing 1 - 20 of 96

Citation - WoS: 22
Citation - Scopus: 34
A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm
(Mdpi, 2023) Hamzei, Marzieh; Khandagh, Saeed; Navimipour, Nima Jafari
The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.
Citation - WoS: 32
Citation - Scopus: 37
Implementation of a Product-Recommender System in an Iot-Based Smart Shopping Using Fuzzy Logic and Apriori Algorithm
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Yan, Shu-Rong; Pirooznia, Sina; Heidari, Arash; Navimipour, Nima Jafari; Unal, Mehmet
The Internet of Things (IoT) has recently become important in accelerating various functions, from manufacturing and business to healthcare and retail. A recommender system can handle the problem of information and data buildup in IoT-based smart commerce systems. These technologies are designed to determine users' preferences and filter out irrelevant information. Identifying items and services that customers might be interested in and then convincing them to buy is one of the essential parts of effective IoT-based smart shopping systems. Due to the relevance of product-recommender systems from both the consumer and shop perspectives, this article presents a new IoT-based smart product-recommender system based on an apriori algorithm and fuzzy logic. The suggested technique employs association rules to display the interdependencies and linkages among many data objects. The most common use of association rule discovery is shopping cart analysis. Customers' buying habits and behavior are studied based on the numerous goods they place in their shopping carts. As a result, the association rules are generated using a fuzzy system. The apriori algorithm then selects the product based on the provided fuzzy association rules. The results revealed that the suggested technique had achieved acceptable results in terms of mean absolute error, root-mean-square error, precision, recall, diversity, novelty, and catalog coverage when compared to cutting-edge methods. Finally, themethod helps increase recommender systems' diversity in IoT-based smart shopping.
Citation - WoS: 49
Citation - Scopus: 56
A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning
(Elsevier, 2023) Heidari, Arash; Navimipour, Nima Jafari; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin
Offloading assists in overcoming the resource constraints of specific elements, making it one of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery capacities can use the edge to offload some of the operations, which can significantly reduce latency and lengthen battery lifetime. Due to their restricted battery capacity, deep learning (DL) techniques are more energy-intensive to utilize in IoT devices. Because many IoT devices lack such modules, numerous research employed energy harvester modules that are not available to IoT devices in real-world circumstances. Using the Markov Decision Process (MDP), we describe the offloading problem in this study. Next, to facilitate partial offloading in IoT devices, we develop a Deep Reinforcement learning (DRL) method that can efficiently learn the policy by adjusting to network dynamics. Convolutional Neural Network (CNN) is then offered and implemented on Mobile Edge Computing (MEC) devices to expedite learning. These two techniques operate together to offer the proper offloading approach throughout the length of the system's operation. Moreover, transfer learning was employed to initialize the Qtable values, which increased the system's effectiveness. The simulation in this article, which employed Cooja and TensorFlow, revealed that the strategy outperformed five benchmarks in terms of latency by 4.1%, IoT device efficiency by 2.9%, energy utilization by 3.6%, and job failure rate by 2.6% on average.
Citation - WoS: 15
Citation - Scopus: 29
Fault-Tolerant Load Balancing in Cloud Computing: a Systematic Literature Review
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Mohammadian, Vahid; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Darwesh, Aso
Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to identify under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, makespan, reliability, resource utilization, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.
Citation - WoS: 108
A reliable method for data aggregation on the industrial internet of things using a hybrid optimization algorithm and density correlation degree
(Springer, 2024) Heidari, Arash; Shishehlou, Houshang; Darbandi, Mehdi; Navimipour, Nima Jafari; Yalcin, Senay
The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees' fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance.
Citation - WoS: 3
Citation - Scopus: 4
A New Fault-Tolerance Majority Voter Circuit for Quantum-Based Nano-Scale Digital Systems
(Springer, 2025) Ahmadpour, Seyed-Sajad; Navimipour, Nima Jafari; Mosleh, Mohammad; Noorallahzadeh, Mojtaba; Kassa, Sankit; Ahmed, Suhaib
Quantum-dot cellular automata (QCA) technology has gained attention lately due to its ability to reduce energy dissipation and minimize circuit area. However, the existing research shows that a critical challenge arises from the lack of circuit resistance in QCA systems when confronted with defects. This issue directly impacts circuit stability and output generation. Moreover, the 3-input majority gate (MV3) is a foundational component within QCA circuits, making its improvement crucial for developing fault-tolerant circuits. One approach is to design MV3 that incorporates essential quantum cells within a single clock cycle. Thus, this paper presents a unique cellular structure for the MV3 gate, utilizing simple quantum cells. The proposed gate, comprising only twelve cells, serves as a building block for QCA circuits. It boasts several key features, including low power consumption, efficient output polarity (+/- 9.93e00-1), and high reliability. Furthermore, to show the efficiency of the suggested gate, it is employed in realizing a 2:1 multiplexer and a full adder/subtractor. Lastly, the proposed MV3 gate is utilized to develop a simultaneous multi-logic gate which is producing several vital digital circuits, such as AND, OR, NOT, NAND, Copy, Subtractor, and Adder. The circuits are designed using QCADesigner and QCAPro, with power estimation included in the process. The comparative analysis reveals that the proposed structures significantly enhance the trade-off between complexity, fault tolerance, and power consumption compared to previous designs.
Citation - WoS: 16
Citation - Scopus: 18
A New Nano-Design of 16-Bit Carry Look-Ahead Adder Based on Quantum Technology
(Iop Publishing Ltd, 2023) Ahmadpour, Seyed-Sajad; Navimipour, Nima Jafari
There is a requirement and a desire to develop reliable and energy-efficient circuit designs that adapt to the expanding field of low-power circuit engineering in the VLSI domain based on nanotechnology. The quantum-dot cellular automata (QCA) technology possesses the potential to supplant the conventional, complementary metal-oxide-semiconductor (CMOS) technology in low-power nano-scale applications due to its diminutive cell dimensions, dependable circuitry architecture, and robust structural integrity. On the other hand, the carry look-ahead adder (CLA) is one of the vital circuits in digital processing utilized in diverse digital applications. In addition, for the design of this essential circuit, the occupied area and the delay play the primary role because using a simple formulation can reduce the occupied area, energy consumption, and the number of gates count. In the previous structures, high delay and use of traditional technology (like CMOS) caused an increase in the number of gate counts and occupied areas. Using QCA technology, simple quantum cells, and a low delay, all the previous shortcomings can be resolved to reduce the number of gate counts and low occupied area in the CLA circuit. This paper proposes a new method that helps the propagation characteristics generate suitable signals to reduce the number of gate counts based on adders in QCA technology. Several new blocks are used to design fast binary adders. Finally, an optimal four and 16-bit CLA circuit will be proposed based on the adder circuit. Furthermore, the execution and experimentation of outcomes are carried out utilizing QCADesigner-2.0.3. The simulation-based comparison of values justified the proposed design's accuracy and efficiency. The simulation results demonstrate that the proposed circuit has a low area and quantum cell.
Citation - WoS: 40
Citation - Scopus: 55
A Comprehensive and Systematic Literature Review on the Big Data Management Techniques in the Internet of Things
(Springer, 2023) NaghibnAff, Arezou; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Sharifi, Arash
The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.
Citation - WoS: 9
Citation - Scopus: 13
Nano-Design of Ultra-Efficient Reversible Block Based on Quantum-Dot Cellular Automata
(Zhejiang Univ Press, 2023) Ahmadpour, Seyed Sajad; Navimipour, Nima Jafari; Mosleh, Mohammad; Yalcin, Senay
Reversible logic has recently gained significant interest due to its inherent ability to reduce energy dissipation, which is the primary need for low-power digital circuits. One of the newest areas of relevant study is reversible logic, which has applications in many areas, including nanotechnology, DNA computing, quantum computing, fault tolerance, and low-power complementary metal-oxide-semiconductor (CMOS). An electrical circuit is classified as reversible if it has an equal number of inputs and outputs, and a one-to-one relationship. A reversible circuit is conservative if the EXOR of the inputs and the EXOR of the outputs are equivalent. In addition, quantum-dot cellular automata (QCA) is one of the state-of-the-art approaches that can be used as an alternative to traditional technologies. Hence, we propose an efficient conservative gate with low power demand and high speed in this paper. First, we present a reversible gate called ANG (Ahmadpour Navimipour Gate). Then, two non-resistant QCA ANG and reversible fault-tolerant ANG structures are implemented in QCA technology. The suggested reversible gate is realized through the Miller algorithm. Subsequently, reversible fault-tolerant ANG is implemented by the 2DW clocking scheme. Furthermore, the power consumption of the suggested ANG is assessed under different energy ranges (0.5Ek, 1.0Ek, and 1.5Ek). Simulations of the structures and analysis of their power consumption are performed using QCADesigner 2.0.03 and QCAPro software. The proposed gate shows great improvements compared to recent designs.
Citation - WoS: 49
Citation - Scopus: 64
Resilient and Dependability Management in Distributed Environments: a Systematic and Comprehensive Literature Review
(Springer, 2023) Amiri, Zahra; Heidari, Arash; Navimipour, Nima Jafari; Unal, Mehmet
With the galloping progress of the Internet of Things (IoT) and related technologies in multiple facets of science, distribution environments, namely cloud, edge, fog, Internet of Drones (IoD), and Internet of Vehicles (IoV), carry special attention due to their providing a resilient infrastructure in which users can be sure of a secure connection among smart devices in the network. By considering particular parameters which overshadow the resiliency in distributed environments, we found several gaps in the investigated review papers that did not comprehensively touch on significantly related topics as we did. So, based on the resilient and dependable management approaches, we put forward a beneficial evaluation in this regard. As a novel taxonomy of distributed environments, we presented a well-organized classification of distributed systems. At the terminal stage, we selected 37 papers in the research process. We classified our categories into seven divisions and separately investigated each one their main ideas, advantages, challenges, and strategies, checking whether they involved security issues or not, simulation environments, datasets, and their environments to draw a cohesive taxonomy of reliable methods in terms of qualitative in distributed computing environments. This well-performed comparison enables us to evaluate all papers comprehensively and analyze their advantages and drawbacks. The SLR review indicated that security, latency, and fault tolerance are the most frequent parameters utilized in studied papers that show they play pivotal roles in the resiliency management of distributed environments. Most of the articles reviewed were published in 2020 and 2021. Besides, we proposed several future works based on existing deficiencies that can be considered for further studies.
Citation - WoS: 82
Citation - Scopus: 112
A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems
(Elsevier, 2023) Heidari, Arash; Javaheri, Danial; Toumaj, Shiva; Navimipour, Nima Jafari; Rezaei, Mahsa; Unal, Mehmet
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
Citation - WoS: 18
Citation - Scopus: 19
A Nano-Scale Design of Arithmetic and Logic Unit for Energy-Efficient Signal Processing Devices Based on a Quantum-Based Technology
(Springer, 2025) Zohaib, Muhammad; Navimipour, Nima Jafari; Aydemir, Mehmet Timur; Ahmadpour, Seyed-Sajad
Signal processing had a significant impact on the development of many elements of modern life, including telecommunications, education, healthcare, industry, and security. The semiconductor industry is the primary driver of signal processing innovation, producing ever-more sophisticated electronic devices and circuits in response to global demand. In addition, the central processing unit (CPU) is described as the "brain" of a computer or all electronic devices and signal processing. CPU is a critical electronic device that includes vital components such as memory, multiplier, adder, etc. Also, one of the essential components of the CPU is the arithmetic and logic unit (ALU), which executes the arithmetic and logical operations within all types of CPU operations, such as addition, multiplication, and subtraction. However, delay, occupied areas, and energy consumption are essential parameters in ALU circuits. Since the recent ALU designs experienced problems like high delay, high occupied area, and high energy consumption, implementing electronic circuits based on new technology can significantly boost the performance of entire signal processing devices, including microcontrollers, microprocessors, and printed devices, with high-speed and low occupied space. Quantum dot cellular automata (QCA) is an effective technology for implementing all electronic circuits and signal processing applications to solve these shortcomings. It is a transistor-less nanotechnology being explored as a successor to established technologies like CMOS and VLSI due to its ultra-low power dissipation, high device density, fast operating speed in THz, and reduced circuit complexity. This research proposes a ground-breaking ALU that upgrades electrical devices such as microcontrollers by applying cutting-edge QCA nanotechnology. The primary goal is to offer a novel ALU architecture that fully utilizes the potential of QCA nanotechnology. Using a new and efficient approach, the fundamental gates are skillfully utilized with a coplanar layout based on a single cell not rotated. Furthermore, this work presents an enhanced 1-bit and 2-bit arithmetic logic unit in quantum dot cellular automata. The recommended design includes logic, arithmetic operations, full adder (FA) design, and multiplexers. Using the powerful simulation tools QCADesigner, all proposed designs are evaluated and verified. The simulation outcomes indicates that the suggested ALU has 42.48 and 64.28% improvements concerning cell count and total occupied area in comparison to the best earlier single-layer and multi-layer designs.
Citation - WoS: 3
Citation - Scopus: 3
Sustainable IoT Solutions: Developing a Quantum-Aware Circuit for Improving Energy Efficiency Based on Atomic Silicon
(Elsevier, 2025) Rasmi, Hadi; Ahmadpour, Seyed Sajad; Seyyedabbasi, Amir; Navimipour, Nima Jafari; Khan, Wasiq
Internet of Things (IoT) can be described as a network of physical objects equipped with sensors, processing power, software, and any other types of technology that allows them to communicate and share data with other devices and systems. The proliferation of IoT is conditional on developing energy-saving blocks of computation with sustained connectivity and real-time information processing capabilities. Traditional technologies like CMOS and VLSI circuits face critical failures at scales below 4 nm, including excessive current leakages, high energy consumption, and thermal instability, which make them less appropriate for future micro-scale IoT chips. To overcome such limitations, a new alternative technology called Atomic Silicon Dangling Bond (ASDB) nanotechnology has been developed, leveraging atomistic accuracy in countering CMOS-related inefficiencies and supporting quantum-inspired computational processes. Since Arithmetic and Logic Unit (ALU) is a primary unit of any digital system like IoT, this work introduces the necessity of quantum-aware ALU development, taking a quantum-inspired computational mechanism and leveraging ASDB's native quantum behavior for increased performance, accuracy, and efficiency in IoT systems. A single-bit ALU for micro-IoT blocks is developed using ASDB nanotechnology with robust computational design to guarantee operational integrity. The design is analyzed through SiQAD simulator in terms of energy consumption, logical accuracy, and area consumption. The proposed ALU in this work demonstrates a reduction in occupied area and quantum cell count, highlighting a significant step toward ultra-dense integration. Furthermore, with an energy consumption reduction of 3.19% compared to the best design, this ALU offers a sustainable and practical solution for lowpower IoT applications in the future.
Kuantum Teknolojisine Dayalı Görüntü Steganografisi
(2025) Salahov, Huseyn; Navimipour, Nima Jafari
Steganografi, bilgilerin bir örtü ortamında gizlenerek tespit edilmeden saklandığı bir veri gizleme tekniğidir. Bu tür tekniklerin performansını değerlendiren önemli bir ölçüt, gizli mesajın tespit edilmesine karşı direnç, yani güvenliktir. Güvenli steganografi tekniklerinden biri, görüntü maskelenmesidir. Bu yöntemde, bir görüntü önce rastgele bir anahtar ile şifrelenerek şifreli bir görüntü elde edilir. Ardından, bu şifreli görüntü, orijinal görüntü kullanılarak tekrar şifrelenir ve anahtarın yerine geçen bir maske üretilir. Bu süreç, anahtarın gizli kalmasını sağlar ve yöntemin güvenliğini artırır. Bu algoritmalar, kırmızı, yeşil ve mavi (RGB) kanalları ayrı ayrı işlenerek renkli görüntüler üzerinde gerçekleştirilecek ve üç şifreli kanal ile üç maske kanalı elde edilecektir. Geleneksel olarak, steganografi, tamamlayıcı metal-oksit-yarı iletken (CMOS) transistörleri ve çok büyük ölçekli tümleşik devre (VLSI) donanımı kullanılarak uygulanır. Ancak, VLSI'nin yonga yoğunluğundan kaynaklanan aşırı ısınma gibi doğal sorunları nedeniyle, kuantum teknolojileri, steganografide VLSI'nin yerini alabilecek yeni nesil teknolojiler olarak değerlendirilmektedir. Alternatif olarak, kuantum nokta hücresel otomataları (QCA), steganografik sistemleri güç analizi saldırılarına karşı korumak için kritik olan yüksek hız, bütünlük ve düşük güç tüketimi sunar. Bu çalışmada, hem şifreleme hem de maske üretimi için kullanılan XOR kapısı temel yapı taşı olan, QCA tabanlı bir görüntü maskesi nano-tasarımı öneriyoruz. Tasarım, QCADesigner 2.0.3 yazılımı kullanılarak geliştirilmiş, şifreleme mantığı ise Python diliyle yazılmıştır. Tasarım, döndürülmemiş hücreler içeren tek katmanlı bir yapı kullanır. Görüntü kalitesini değerlendirmek için Yapısal Benzerlik İndeksi (SSIM) ve Yapısal Farklılık İndeksi (DSSIM) kullanılmıştır. Sonuçlarımız, önceki QCA tabanlı tasarımlara kıyasla hücre sayısında %57,3 ve alanda %40,7 azalma ile iyileşmeler gösterdi. Güvenlik analizleri, diferansiyel saldırılar dışında çeşitli saldırılara karşı artırılmış direnç sağlandığını ortaya koymuştur. Anahtar Sözcükler: Steganografi, Görüntü Maskelenmesi, QCA, XOR Kapısı, Görüntü Şifreleme, RGB, Kriptografi.
Citation - WoS: 5
Citation - Scopus: 5
Blockchain Systems in Embedded Internet of Things: Systematic Literature Review, Challenges Analysis, and Future Direction Suggestions
(Mdpi, 2022) Darbandi, Mehdi; Al-Khafaji, Hamza Mohammed Ridha; Nasab, Seyed Hamid Hosseini; AlHamad, Ahmad Qasim Mohammad; Ergashevich, Beknazarov Zafarjon; Navimipour, Nima Jafari
Internet of Things (IoT) environments can extensively use embedded devices. Without the participation of consumers; tiny IoT devices will function and interact with one another, but their operations must be reliable and secure from various threats. The introduction of cutting-edge data analytics methods for linked IoT devices, including blockchain, may lower costs and boost the use of cloud platforms. In a peer-to-peer network such as blockchain, no one has to be trusted because each peer is in charge of their task, and there is no central server. Because blockchain is tamper-proof, it is connected to IoT to increase security. However, the technology is still developing and faces many challenges, such as power consumption and execution time. This article discusses blockchain technology and embedded devices in distant areas where IoT devices may encounter network shortages and possible cyber threats. This study aims to examine existing research while also outlining prospective areas for future work to use blockchains in smart settings. Finally, the efficiency of the blockchain is evaluated through performance parameters, such as latency, throughput, storage, and bandwidth. The obtained results showed that blockchain technology provides security and privacy for the IoT.
A Nano-Design of Image Masking and Steganography Structure Based on Quantum Technology
(Elsevier, 2025) Salahov, Huseyn; Ahmadpour, Seyed-Sajad; Navimipour, Nima Jafari; Das, Jadav Chandra; Rasmi, Hadi
Secure image storage and transmission require sound encryption methods that resist key exposure while maintaining high image quality. Various encryption approaches have been developed to protect image content and its transmission from unauthorized access. One such method is image masking, where a special mask is generated to conceal information within the original image. Instead of hiding the image visually, the mask creates an intermediate layer that obfuscates the encryption key, eliminating the need to transmit it directly. However, implementing such masking techniques efficiently at the hardware level poses particular challenges. Traditional Complementary Metal-Oxide-Semiconductor (CMOS)-based Very-Large-Scale-Integration (VLSI) systems face scalability issues, excessive heat, and high-power consumption. To overcome these challenges, this study utilizes a nano-scale image masking architecture based on Quantum-dot Cellular Automata (QCA), offering reduced area, lower power dissipation, and faster processing. The core operations utilize a three-input XOR gate, designed as a single-layer QCA structure without rotated cells. While QCA-based approaches improve hardware efficiency, most existing implementations focus only on grayscale images, leaving a gap in colorful image encryption. To address this, the work presents a QCA-based encryption and masking architecture for colored images. The method encrypts an image using a random key to generate a cipher image, which is then XORed with the original image to produce a mask. This process, applied independently to each RGB channel, produces three cipher-mask pairs, embedding steganographic property by concealing key information within the image. The keys are generated using a true random number generator (TRNG) based on cross-coupled loops and crossoriented structures, ensuring high entropy. The design was modeled in QCADesigner 2.0.3, with the encryption/decryption algorithms implemented in Python. Experimental results demonstrated a meaningful reduction in cell count and consumed area compared to the prior designs. Image quality and security analysis confirmed visual fidelity and improved robustness.
Citation - WoS: 1
Citation - Scopus: 1
The role of new ICT-based systems in modern management special issue editors
(Cambridge Univ Press, 2023) Navimipour, Nima Jafari; Wan, Shaohua; Pasumarti, Srinivas Subbarao; Fazio, Maria
In this special issue, we have collected eight articles that offer new points for research on information and communications technology (ICT)-based systems. We focused on the intuitive nature of the relationship between new ICT-based systems and contemporary management, forming an integrative unit of analysis instead of focusing solely on new ICT-based systems and leaving contemporary management as a moderating or mediating factor. This special issue promoted interdisciplinary research at the intersection of new ICT-based systems and contemporary management, including cybernetics systems and knowledge management, service managing and the Internet of things, cloud and marketing management, business process re-engineering and management, knowledge management, and strategic business management, among others.
Citation - WoS: 6
Citation - Scopus: 7
A Radio Frequency Identification Reader Collision Avoidance Protocol for Dense Reader Environments in the Context of Industry 4.0
(Wiley, 2023) Rezaie, Hadiseh; Golsorkhtabaramiri, Mehdi; Navimipour, Nima Jafari
In the new industrial revolution known as Industry 4.0, radio frequency identification (RFID) systems are a key component of automatic detection. These systems have two main elements, namely Reader and Tag. In many Internet of Things (IoT) applications, the RFID system is used with lots of readers working together in a dense environment to read tags. The simultaneous operation of readers with a common sensory range increases the likelihood of reader-to-tag collision and reader-to-reader collision and reduces the number of successful reading and as a result, reduces network performance and average waiting time for each reader increased. Collisions happen when readers are in the interference range and start reading tags simultaneously, so it is necessary to use the right solution to control channel access in these systems. So far, various solutions have been proposed to control readers' access to the communication channel. Some of them have not considered the existing standards for this type of system or have not been efficient enough to be used in the IoT. In this study, we propose a method that, by considering the distance between readers and the number of neighbourhoods, and the possibility of information sharing, allows readers to successfully read more tags with fewer collisions in a certain time frame. The results of the performance study in a real-world environment showed that the suggested method outperformed similar methods in terms of network performance and has much better throughput, making it a superior choice for usage in IoT-based RFID systems.
Citation - WoS: 37
Citation - Scopus: 45
Applications of Deep Learning in Alzheimer's Disease: a Systematic Literature Review of Current Trends, Methodologies, Challenges, Innovations, and Future Directions
(Springer, 2024) Toumaj, Shiva; Heidari, Arash; Shahhosseini, Reza; Navimipour, Nima Jafari
Alzheimer's Disease (AD) constitutes a significant global health issue. In the next 40 years, it is expected to affect 106 million people. Although more and more people are getting AD, there are still no effective drugs to treat it. Insightful information about how important it is to find and treat AD quickly. Recently, Deep Learning (DL) techniques have been used more and more to diagnose AD. They claim better accuracy in drug reuse, medication recognition, and labeling. This essay meticulously examines the works that have talked about using DL with Alzheimer's disease. Some of the methods are Natural Language Processing (NLP), drug reuse, classification, and identification. Concerning these methods, we examine their pros and cons, paying special attention to how easily they can be explained, how safe they are, and how they can be used in medical situations. One important finding is that Convolutional Neural Networks (CNNs) are most often used for AD research and Python is most often used for DL issues. Some security problems, like data protection and model stability, are not looked at enough in the present research, according to us. This study thoroughly examines present methods and also points out areas that need more work, like better data integration and AI systems that can be explained. The findings should help guide more research and speed up the creation of DL-based AD identification tools in the future.
Citation - WoS: 2
Citation - Scopus: 3
Towards a Scalable and Efficient Full- Adder Structure in Atomic Silicon Dangling Band Technology
(Elsevier, 2025) Rasmi, Hadi; Mosleh, Mohammad; Navimipour, Nima Jafari; Kheyrandish, Mohammad
Atomic Silicon Dangling Bond (ASDB) is a promising new nanoscale technology for fabricating logic gates and digital circuits. This technology offers tremendous advantages, such as small size, high speed, and low power consumption. As science and technology progress, ASDB technology may eventually replace the current VLSI technology. This nanoscale technology is still in its early stages of development. Recently, many computing circuits, such as full-adder, have been designed. However, these circuits have a common fundamental problem; they consume a lot of energy and occupy a lot of area, which reduces the performance of complex circuits. This paper proposes a novel ASDB layout for designing an efficient full-adder circuit in ASDB technology. Moreover, a four-bit ASDB ripple carry adder(RCA) is designed using the proposed ASDB full-adder. The proposed ASDB fulladder not only improves the stability of the output but also surpasses the previous works, in terms of energy and accuracy,by 90% and 38%, respectively. Also, it has very favorable conditions in terms of occupied area and is resistant to DB misalignment defects.