The rapid growth of high-performance computing (HPC) and supercomputing applications necessitates hardware architectures that provide both high computational performance and strong energy efficiency under real-time and massively parallel workloads. However, conventional complementary metal-oxide semiconductor (CMOS) technologies face fundamental challenges, including excessive power consumption, leakage currents, and severe scaling limitations, which restrict their suitability for future exascale systems. To overcome these limitations, emerging nanotechnologies such as Quantum-dot Cellular Automata (QCA) have gained significant attention due to their ultra low-power consumption and high device density. In this work, we present a high-performance and low-power Quantum-Dot Multiply–Accumulate (Q-Dot MAC) unit, where MAC denotes a fundamental arithmetic operation combining multiplication and accumulation, extensively used in scientific computing, and signal processing. The proposed QCA-based architecture is specifically designed to satisfy the high-frequency (HF) operational demands of modern HPC environments, enabling sustained high-throughput computation. The main objective of this design is to realize a compact, energy-efficient, and physically stable MAC unit suitable for large-scale deployment in energy-constrained supercomputing platforms. Exploiting the inherent parallelism and high-density layout characteristics of QCA, the proposed MAC architecture efficiently executes key computational kernels required in HPC workloads, including large-scale matrix multiplication, convolution operations, and scientific simulations. The proposed QCA-based circuits demonstrate significant performance and area efficiency improvements compared with the best existing designs in the literature. Specifically, the half adder (HA) achieves a 20.51

The Internet of Things (IoT) is transforming modern computing by enabling massive numbers of interconnected devices for smart healthcare, transportation, environmental monitoring, and industrial automation. These devices require ultra-low power consumption, compact architectures, and high reliability, since they often operate on limited energy budgets and in harsh environments. Conventional Complementary Metal-Oxide-Semiconductor (CMOS) technology is increasingly unable to meet these demands due to scaling bottlenecks, leakage currents, high heat generation, and process variability, limiting its applicability for sustainable IoT systems. In order to address these issues, Quantum-Dot Cellular Automata (QCA) has emerged as a post-CMOS nanoscale computing paradigm in which binary information is encoded through electron configurations rather than current flow. This approach enables ultra-low energy dissipation and improved tolerance to fabrication-related defects when compared to conventional CMOS designs. In this paper, we present an original fault-tolerant 3-input majority voter (MV3) with seven quantum cells. The proposed MV3 gate exhibits 100% tolerance under the evaluated single-cell omission defect scenarios considered in this study, demonstrates fault resistance of up to 90% under extra-cell deposition conditions, and reduces power dissipation by up to 94.38% compared with the best previously reported designs. Building on this foundation, we reconstruct several MV3-based IoT processor components, including a 2:1 multiplexer, an adder, a 4-bit ALU, a 4-bit accumulator, memory, and a 4-bit CPU architecture using the proposed MV3 gate as the main logic primitive. QCADesigner and QCAPro, analyses confirm the functional correctness, thermal behavior, and energy characteristics of the proposed primitive and selected module-level circuits, while the complete 4-bit CPU provides a layout-level proof of concept for MV3-based IoT-oriented QCA processing.

Digital signal processing (DSP) is a crucial engineering field dedicated to the processing and analysis of digital signals. DSP is particularly significant in critical sectors such as telecommunications, medical imaging, and secure communications, where it demands high accuracy, reliability, and real-time performance. In addition, the fault-tolerant (F-T) Arithmetic and Logic Unit (ALU) provides a fundamental building block of DSP architectures, enabling the accurate implementation of arithmetic and logical functions that are essential for advanced computational tasks. However, traditional ALUs were designed using complementary metal-oxide semiconductors (CMOS) and very large-scale integration (VLSI), which led to several challenges, such as high energy consumption, high occupied area, and slow operating speed. These limitations can be effectively addressed through nanotechnology, specifically quantum-dot cellular automata (QCA), which offers high speed, reduces occupying area, and has low power consumption. Accordingly, this paper proposes a QCA-based ALU circuit for DSP applications. The proposed designs integrate an F-T full adder (FA), a QCA-based multiplexer (MUX), and an ALU circuit to enhance performance and efficiency for DSP applications. The validation and verification of all suggested designs are performed using the simulation tool QCADesigner.

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.

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.

ABSTRACTRouting protocols are pivotal in Vehicular Ad hoc Networks (VANETs), serving as the backbone for efficient routing discovery, particularly within the realm of Intelligent Transportation Systems (ITS). However, ensuring their seamless functionality within VANET environments necessitates rigorous verification and formal modeling. Colored Timed Petri Nets (CTPNs) stand out as a valuable mathematical and formal method for this purpose. This study shows a new way to describe the Ad hoc On‐Demand Distance Vector (AODV) routing system in VANETs using CTPNs. There are nine pages of detailed analysis using this new modeling method, which allows you to examine success across many levels of a hierarchy. This study provides a strong foundation for building and testing the AODV routing system in VANETs, showing how well it functions in real‐life situations. It is interesting to see how the results of the CTPN–based model and simulations compare. Notably, the model finds routes in an average of 32 s, while tests show that it takes 56 s. Additionally, the model's overall number of sent and received packets closely matches the results from the exercise. Furthermore, the suggested plan shows a yield of 41%. Strict T‐tests indicate that the modeling results are highly reliable.

Many devices consist of low-power processor. Quantum-dot-cellular-automata (QCA) based processor designs provide enhanced performance compared with conventional metal-oxide-semiconductor (MOS) based processors. Nanocomputing-based processors are often energy-efficient. We have developed Nanotechnology QCA-based different subcomponents of processor such as 2-to-4 decoder, 3-to-8 decoder, Delay Flip-flop (D-FF), and sequence counter. A potential energy proof has been measured in the 2-to-4 decoder design. The synthesis approach algorithm has been presented for all designs. Further, the potential energy calculation results show for 2-to-4 decoder. According to the synthesis results 2-to-4 decoder has improved 82.3% cell count, 86% area, and 85% latency over previous work. Comparing the primitive results with the prior one, results improved by 64% and 76% in terms of cell count and area in the design of the 3-to-8 decoder. Among the different components of the processor is D-FF, which has an improvement of 66.37% in cell counts and 62.5% in area over the prior design. Primitive results have improved, including latency, cell count, and area, showing the proposed processor design is comparable to lowpower devices and high speed. In terms of balance power, the proposed subcomponent of the processor will benefit low power device.

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.

The growth of the Internet of Things (IoT)-related innovations has resulted in the invention of numerous IoT objects. However, the resource limitations of individual items remain a challenge that can be overcome through offloading. A key limitation of previous research is the absence of an integrated offloading framework that can operate securely in offline/online environments. The security and calculated online/offline offloading issues in a multi-user IoT-fog-cloud system with blockchain are investigated in this article at the same time. First, we provide a reliable access control system utilizing blockchain to enhance offloading security. This technique can guard cloud resources against unauthorized offloading practices. Next, we define a computation offloading issue by optimizing the offloading decisions, allocating computing resources and radio bandwidth, and intelligent contract use to address the computation management of authorized mobile devices. This optimization challenge focuses on the long-term system costs of latency, energy use, and intelligent contract charge among all mobile devices. We create a new Deep Reinforcement Learning (DRL) technique employing a double-dueling Q-network to address the suggested offloading problem. We provide a Markov Decision Process (MDP)-based DRL solution to the IoT offloading-enabled blockchain dilemma. The supposed system works in both online and offline settings, and when operating online, we use the Post Decision State (PDS) method. The contributions of this work include a new integrated offloading framework that can operate in offline/online environments while preserving security and a novel approach that incorporates fog platforms into IoT blockchain-enabled networks for improved system efficiency. Our method outperforms four benchmarks in cost by 5.1

Timely detection of cancer is essential for enhancing patient outcomes. Artificial Intelligence (AI), especially Deep Learning (DL), demonstrates significant potential in cancer diagnostics; however, its opaque nature presents notable concerns. Explainable AI (XAI) mitigates these issues by improving transparency and interpretability. This study provides a systematic review of recent applications of XAI in cancer detection, categorizing the techniques according to cancer type, including breast, skin, lung, colorectal, brain, and others. It emphasizes interpretability methods, dataset utilization, simulation environments, and security considerations. The results indicate that Convolutional Neural Networks (CNNs) account for 31 % of model usage, SHAP is the predominant interpretability framework at 44.4 %, and Python is the leading programming language at 32.1 %. Only 7.4 % of studies address security issues. This study identifies significant challenges and gaps, guiding future research in trustworthy and interpretable AI within oncology.

ABSTRACT The Internet of things (IoT) and social networks integrate into a new area called the social Internet of things (SIoT). The SIoT is characterized as a social network of things that has enhanced intelligence and social awareness. Essential criteria for both IoT and SIoT networks involve effective service provisioning and the determination of device methods. The discovery of services and selecting the optimal solution to composite them are service provisioning challenges of the SIoT environment. Addressing these challenges requires efficient optimization methods. Traditional optimization algorithms have strengths and weaknesses. For example, a genetic algorithm (GA) can find global optima but suffer from diversity disappearing prematurely, whereas a backtracking search algorithm (BSA) offers better global exploration but converges more slowly. This article proposes a new hybrid optimization algorithm called the improved genetic backtracking search algorithm based on community detection (IGBSA‐CD) to overview these limitations. This approach improves the GA's ability and integrates with the advantages of BSA to identify suitable devices to fulfill user requirements by applying the optimized service provision (discovery, selection, and composition) in detected communities. It is based on a new community detection algorithm to reduce the space for service discovery. The experimental results show that the suggested community detection algorithm surpasses current clustering techniques in execution time and cluster quality. IGBSA‐CD more rapidly produces solutions that are near‐optimal with average success rates of over 96.3% for different sample sizes. The fitness values for each sample size and task also exhibit similar convergence, which stabilizes at 0.2–0.3 after multiple generations. The average response time of IGBSA‐CD presents that it is efficient in all three tasks is 0.04 s. It also has a consistently lower response time, even when the task is complex. Furthermore, IGBSA‐CD outperforms other optimization approaches in response time and offers superior quality and adaptability within different tasks and sample sizes.

This paper addresses critical issues such as leakage and heating in Internet of Things (IoT) circuits by exploring alternatives beyond CMOS technology. Atomic silicon dangling bond (ASDB) technology emerges as a promising substitute for executing nanoscale logic circuits, particularly for IoT applications requiring compactness, efficiency, and energy optimization. We propose a Hammer-shaped design for ASDB basic gates to enhance circuit stability and optimality, which is vital for the reliable operation of IoT systems. we demonstrate a new ASDB one-bit comparator circuit to highlight the practical application of the proposed design, which is crucial for real-time data processing in smart homes, industrial automation, health monitoring, connected vehicles, environmental sensors, and smart grids. By integrating high-performance comparator circuits, IoT networks gain improved accuracy and reduced latency, enabling advancements in energy management and wearable electronics. Simulation results highlight significant improvements, including a 33% enhancement in occurrence, 27.% in energy efficiency, 56% resistance to DB omission, and 51% in extra DB deposition.

The Internet of Things (IoT) is an infrastructure of interconnected devices that gather, monitor, analyze, and distribute data. IoT is an inevitable technology for smart city infrastructure to ensure seamless communication across multiple nodes. IoT, with its ubiquitous application in every sector, ranging from health-care to transportation, energy, education, and agriculture, comes with serious challenges as well. Among the most significant ones is security since the majority of IoT devices do not encrypt normal data transmissions, making it easier for the network to breach and leak data. Traditional technologies such as CMOS and VLSI have the added disadvantage of consuming high energy, further creating avenues for security threats for IoT systems. To counter such problems, we require a new solution to replace traditional technologies with a secure IoT. In contrast to traditional solutions, quantum-based approaches offer promising solutions by significantly reducing the energy footprint of IoT systems. Quantum-dot Cellular Automata (QCA) is one such approach and is an advanced nano-technology that exploits quantum principles to achieve complex computations with the advantages of high speed, less occupied area, and low power consumption. By reducing the energy requirements to a minimum, QCA technology makes IoT devices secure. This paper presents a QCA-based Arithmetic Logic Unit (ALU) as a solution to IoT security problems. The proposed ALU includes more than 12 logical and arithmetic operations and is designed using majority gates, XOR gates, multiplexers, and full adders. The proposed architecture, simulated in QCADesigner 2.0.3, achieves an improvement of 60.45% and 66.66% in cell count and total occupied area, respectively, compared to the best of the existing designs, proving to be effective and efficient.

The IoT devices with embedded mobile devices create the Internet of Mobile Things (IoMT) paradigm. Mobility is not supported by the routing protocol for low-power and lossy networks (RPL) created for static networks. IoMT has raised routing challenges such as link failure, instability, energy depletion, packet loss, and handover delay in the network. In this context, IoMT Fuzzy-based RPL (IoMT-FRPL) is proposed in this paper to enhance routing performance. Receiving Signal Strength Indicator (RSSI), Euclidean distance, Hop Count, and Expected Transmission Count (ETX) metrics are built into the fuzzy interface system for the mobile nodes in the network to conserve energy. The IoMT-FRPL consists of the following three key steps: The first steps are data transmission and motion investigation, the second is fuzzy-based prediction of a new static parent for the mobile node, and the third is verifying the unique attachment point. When conventional RPL, mRPL, and EMA-RPL were compared to IoMT-performance FRPL's in Cooja/Contiki 2.7, the simulation results revealed improvements in energy consumption, handover delay, packet delivery rate (PDR), and signaling cost.

Social IoT (SIoT) refers to socializing in the Internet of Things (IoT), where things generate social relationships. Due to the development of objects and issues such as delayed response, slow search, and composite service process, distributed object service discovery, selection, and composition based on the social structure have become essential challenges in the SIoT. Therefore, it is necessary to provide an efficient method for evaluating the effectiveness of service discovery in identifying suitable devices to offer requested services and the best composition strategy for combining requested services. This paper presents a new community detection algorithm that detects IoT devices with social connections in the SIoT network to facilitate service discovery and composition by reducing search space. Additionally, it introduced a new service provisioning algorithm to optimize service discovery and composition, called An Improved Genetic Algorithm based on Community Detection (IGA-CD). Its effectiveness in detected communities is better than other methods in computation modularity, execution time, and cluster assignment quality methods by determining the network’s ideal devices. Experimental results demonstrate the efficacy of the proposed algorithms, which outperform other approaches in terms of scalability, efficiency, and flexibility. The IGA-CD average execution time is 0.129 seconds, which proves its efficiency and faster composition.

In the domain of data-centric networks, Link Prediction (LP) is instrumental in discerning potential or absent connections among entities within complex networks. By employing graph data structures, LP techniques enable a detailed analysis of entity interactions across varied sectors, contributing significantly to overcoming challenges in data filtering and integrity restoration, primarily when the network does not provide embedded data. Although LP methods are widely applicable, especially in recommender systems, their efficacy in current social networks needs to be thoroughly investigated. This study introduces an innovative LP approach using Deep Neural Networks (DNNs). We compare our method against a comprehensive set of established techniques, including traditional score-based methods, classical baselines, and recent deep learning approaches like Graph Neural Networks (GNNs). Our DNN-based solution incorporates a robust feature extraction process and a binary classifier, optimized for accurate prediction of missing links within networks. We performed extensive experimental evaluations on diverse datasets, including co-authorship networks, e-commerce, and social media networks. The study encompasses a comparative analysis with traditional LP techniques, namely Common Neighbors, Resource Allocation Index, Jaccard’s Coefficient, and Adamic/Adar Index, as well as other selected baseline and deep-learning methods. Our findings demonstrate that the DNN-based approach significantly enhances predictive accuracy, outperforming the conventional baseline methods in link prediction.

Nature-inspired algorithms revolve around the intersection of nature-inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature-inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real-world implementation issues, and the efficacy of nature-inspired algorithms in IoT-based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature-inspired algorithms in IoT-based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature-inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature-inspired algorithms in the dynamic landscape of IoT-based healthcare services. Providing a complete overview of the current issues associated with nature-inspired algorithms in IoT-based healthcare services. Providing a thorough overview of present methodologies for IoT-based healthcare services in research studies; Evaluating each region that tailored nature-inspired algorithms with many perspectives such as advantages, restrictions, datasets, security involvement, and simulation stings; Outlining the critical aspects that motivate the cited approaches to enhance future research; Illustrating descriptions of certain IoT-based healthcare services used in various studies. image

Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. This article is categorized under: Technologies > Machine Learning Algorithmic Development > Multimedia Application Areas > Science and Technology

Quantum-dot Cellular Automata (QCA) has emerged as a revolutionary technology for nano-scale computing circuits and a promising alternative to conventional transistor-based technologies. However, the susceptibility to defects during circuit synthesis is a pivotal challenge, undermining its potential. This study seeks to introduce an innovative and robust fault-tolerant 3-input majority voter gate comprising 16 simple cells. The primary objective is to enhance the gate's resilience against two specific defects: one-cell omission and extra-cell deposition. Preliminary assessments indicate that the introduced gate achieves remarkable tolerance rates of 100% for one-cell omission and 89.47% for extra-cell deposition defects. A comprehensive evaluation is used based on the QCADesigner 2.0.3 simulator to validate the gate's performance, supplemented by physical proofs. Furthermore, leveraging the novel gate structure, this paper extends its application to the design of fault-tolerant flip-flops and multiplexer circuits. These building blocks are then employed to construct three distinct fault-tolerant sequential circuits.