Internet Of Things Research Articles

FS2M: fuzzy smart IoT device pairing protocol via speak to microphone

Purpose This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context. Design/methodology/approach This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography. Findings This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency. Originality/value In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

Safeguarding User-Centric Privacy in Smart Homes

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

Exploring the impact of digital knowledge, integration and performance on sustainable accounting, reporting and assurance

Purpose The purpose of this study is to perform bibliometric analysis to systematically and comprehensively examine the current landscape of digital knowledge, integration and performance in the transformation of sustainability accounting, reporting and assurance. Design/methodology/approach This research uses a systematic literature review, following the Scientific Procedures and Rationales for Systematic Literature Review protocol and uses various bibliometric and performance analytical methods. These include annual scientific production analysis, journal analysis, keyword cooccurrence analysis, keyword clustering, knowledge gap analysis and future research direction identification to evaluate the existing literature thoroughly. Findings The analysis reveals significant insights into the transformative impact of digital technologies on sustainability practices. Annual scientific production and journal analyses highlight key contributors to the adoption of digital technologies in sustainability accounting, reporting and assurance. Keyword cooccurrence analyses have identified key themes in sustainability accounting, reporting and assurance, highlighting the transformative role of digital technologies such as artificial intelligence (AI), blockchain, Internet of Things (IoT) and big data. These technologies enhance corporate accountability, transparency and sustainability by automating processes and improving data accuracy. The integration of these technologies supports environmental, social and governance (ESG) reporting, circular economy initiatives and strategic decision-making, fostering economic, social and environmental sustainability. Cluster-by-coupling analyses delve into nine broader revealing that IoT improves ESG report accuracy, eXtensible Business Reporting Language structures ESG data and AI enhances life cycle assessments and reporting authenticity. In addition, digital transformation impacts environmental performance, big data optimizes resource use and edge computing improves eco-efficiency. Furthermore, this study identifies avenues for future research to advance the understanding and implementation of digital technology in sustainability accounting, reporting and assurance practices. Research limitations/implications Academically, this research enriches the understanding of how digital technologies shape sustainability practices and identifies gaps in digital knowledge and integration. Practically, it provides actionable insights for organizations to improve sustainability reporting and performance by effectively leveraging these technologies. Policy-wise, the findings advocate for frameworks supporting the effective implementation of these technologies, ensuring alignment with global sustainability goals. Originality/value This study offers a detailed analysis of the performance and intellectual framework of research on implementing digital technology in sustainability accounting, reporting and assurance. It highlights the evolving research landscape and emphasizes the need for further investigation into how emerging technologies can be leveraged to achieve sustainability goals.

Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic Analysis

The expanding use of Internet-of-Things (IoT) has driven machine learning (ML)-based traffic analysis. 5G networks’ standards, requiring low-latency communications for time-critical services, pose new challenges to traffic analysis. They necessitate fast analysis and response, preventing service disruption or security impact on network infrastructure. Distributed intelligence on IoT edge has been studied to analyze traffic, but introduces delays and raises privacy concerns. Federated learning can address privacy concerns, but does not meet latency requirements. In this article, we propose FLIP4: an efficient federated learning-based framework for in-network traffic analysis. Our solution introduces a lightweight federated tree-based model, offloaded and running within network devices. FLIP4 consumes less resources than previous solutions and reduces communication overheads, making it well-suited for IoT edge traffic analysis. It ensures prompt mitigation and minimal impact on services in the presence of false alerts using two approaches (metering and dropping), thereby balancing learning accuracy and privacy requirements.

Regulation mechanisms of smart tourism development in Kazakhstan

The process of digitalization within the realm of tourism is not merely a trend but rather a significant catalyst that is rapidly propelling the comprehensive transformation of the tourism industry into a new era of technological advancement. This intricate process fundamentally involves the seamless integration and application of cutting-edge digital technologies across various tourism-related activities and services. The advent of innovative solutions that harness the immense capabilities of artificial intelligence, the analytical power of big data, the security features of blockchain, and the interconnectedness provided by the Internet of Things primarily serves to enhance the overall quality of services offered, optimize pricing strategies to align with market demands, and improve risk management protocols within the industry. This paper methods uses 100 Scopus indexed papers about Smart Tourism Development in Kazakhstan. It is imperative to underscore the fact that the ongoing digitalization process, while offering numerous advantages, simultaneously imposes rigorous new requirements concerning the qualifications and competencies of staff members, as well as the paramount importance of data security measures and the protection of consumer rights in the digital environment. The effective management of this digital transformation necessitates a holistic and integrated approach that encompasses not only the development of robust infrastructure but also the enhancement of digital literacy among employees and the establishment of a dynamic and innovative ecosystem that encourages creativity and adaptability.

AI-Driven Adaptive Ventilation Systems For Real-Time Pollution Control In Industrial And Urban Settings: A Systematic Review

The escalating urbanization and industrial activities in cities have significantly impacted air quality, posing health risks and environmental challenges that demand innovative solutions. This review systematically explores the integration of artificial intelligence (AI) and Internet of Things (IoT) sensors within smart cities, focusing on their role in real-time air quality monitoring and dynamic response mechanisms. By adhering to PRISMA guidelines, we analyze recent advancements in AI-driven automated control systems, which utilize IoT sensors to continuously monitor pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and particulate matter (PM). The data gathered by these sensors feed into AI algorithms that facilitate immediate, adaptive responses, such as modifying traffic light sequences to alleviate congestion and notifying nearby facilities to adjust emissions during high pollution periods. This review synthesizes findings on the effectiveness, limitations, and scalability of these systems, highlighting key challenges like sensor data accuracy, privacy considerations, and the infrastructure required for city-wide deployment. The paper concludes by emphasizing the transformative potential of AI and IoT in fostering sustainable urban environments and presents recommendations for future research and policy improvements to optimize smart city air quality management.

Automated Inventory Management Systems with IoT Integration to Optimize Stock Levels and Reduce Carrying Costs for SMEs: A Comprehensive Review

Automated inventory management systems integrated with Internet of Things (IoT) technology represent a transformative approach for Small and Medium-sized Enterprises (SMEs) in optimizing their stock levels and reducing carrying costs. A literature review also shows that there is progress in developing automated solutions, such as IoT sensors, real- time data analytics, and cloud-based applications that improve inventory control. A study shows that the adoption of IoT in automation has seen improvement in the accuracy of inventory, a reduction in stockouts, and carrying costs among SMEs. Research suggests operational improvements including reduced downtime from real time tracking, reverse logistics systems with intuitive demand forecasting for ideal stock replenishment and automation of reordering systems while effectively managing working capital. Materials and Methods: Research methodology encompassed a comprehensive analysis of peer-reviewed literature, case studies, and empirical research focusing on automated inventory management systems with IoT integration in SMEs. Criteria for choosing the literature included articles focusing on the outcomes of IoT implementation, technical integration, and performance of inventory systems. Data extraction main concern was on the efficiencies of the SC such as inventory accuracy, carrying costs, stockouts, and ROI. Descriptive methods used involved comparisons between pre-implementation and post- implementation data, use of statistical tools for measurement of performance enhancement and assessment of factors important in the deployment of the system. Results Implementation of IoT-integrated automated inventory management systems demonstrated significant improvements across multiple performance metrics. Studies reported average inventory accuracy improvements of 25-35%, reduction in carrying costs [1] by 20-30%, and decrease in stockout incidents by 35-45%. Real-time monitoring capabilities led to improved demand forecasting accuracy by 40%, while automated reordering systems reduced manual processing time by 60%. Cloud-based platforms enabled better inventory visibility and control, resulting in working capital optimization of 15-25%. SMEs implementing integrated systems reported enhanced supplier collaboration, reduced lead times, and improved customer satisfaction levels. Cost-benefit analyses indicated positive ROI within 12-18 months of system deployment. Discussion Analysis reveals several key factors contributing to successful implementation of automated inventory management systems with IoT integration. Critical success factors include proper system architecture design, effective change management strategies, and comprehensive staff training programs. Integration challenges primarily revolve around initial investment costs, technical expertise requirements, and system interoperability concerns. Studies show that the greater gains are experienced by SMEs with higher ITOR and those engaged in intricate supply chain operations. Literature review also points to differences in implementation strategies across different sectors and in their recovery, with manufacturing and retail sectors having the highest levels of adoption and improvement. Conclusion IoT-integrated automated inventory management solutions should be adopted by SMEs and are affirmed by ample data to help optimize stock status and minimize carrying costs. The tangible benefits that could be noted are accuracy in inventory, less operational costs, visibility of supply chain, and optimal use of working capital. In appointing the factors affecting the implementation success, it requires the assessment of the technical feasibility and organisational readiness as well as the management of change strategies. Research shows that dynamic technology factors will continue to improve the system’s performance and increased availability to SMEs. Due to advancement in IoT technology, artificial intelligence and cloud computing, there are potential factors that may boost the performance and cost of automatically Managed inventory systems in the future.

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

APLICABILIDADE DE PROGRAMAS DE ENVIRONMENTAL, SOCIAL AND GOVERNANCE (ESG) NO SETOR DO E-COMMERCE NA INDÚSTRIA 4.0

The rise of Industry 4.0 marks a new phase in the digital transformation of industrial processes, encompassing advanced technologies like the Internet of Things (IoT), big data, artificial intelligence (AI), and intelligent automation. In the e-commerce sector, these innovations have facilitated unprecedented operational efficiency, yet the growth in digital capabilities has also led to heightened expectations for sustainable and ethical business practices, aligned with Environmental, Social, and Governance (ESG) frameworks. This study explores the impact of integrating ESG practices within ecommerce, leveraging Industry 4.0 technologies to enhance operational sustainability and transparency. A systematic review of literature and data was conducted, revealing that robust ESG policies in e-commerce contribute to competitive advantage by attracting investment, fostering consumer loyalty, and meeting regulatory standards. Findings show that Industry 4.0 technologies can aid in environmental management by reducing carbon emissions, waste, and energy consumption through optimized logistics and sustainable packaging. The implementation of circular economy principles further demonstrates how e-commerce companies can reduce their environmental footprint, ensuring alignment with consumer demands for accountability. Moreover, sound governance and digital security play crucial roles in maintaining customer trust and protecting sensitive data, underscoring the broader relevance of ESG practices. This study concludes that the integration of ESG with Industry 4.0 technologies is essential for the long-term success of e-commerce, as it fosters a responsible, transparent, and sustainable business model that balances economic growth with social and environmental stewardship.

Advancements in Hydroponic Systems: A Comprehensive Review

Hydroponics, the practice of growing plants without soil using mineral nutrient solutions in water, represents a significant shift in agricultural methodologies. Modern hydroponic systems have evolved substantially since the early 20th century, with advancements in system design, automation, nutrient management, and environmental control enhancing efficiency, scalability, and applicability. This review examines recent innovations that have addressed traditional challenges and created new opportunities for hydroponic cultivation. Hydroponics offers advantages over traditional soil-based agriculture, including precise nutrient control, reduced water usage, and the elimination of soil-borne diseases and pests. These benefits are critical in addressing modern agricultural challenges such as soil degradation, water scarcity, and the need for increased food production for a growing global population. Hydroponic systems can be implemented in diverse environments, making them adaptable solutions for enhancing food security and sustainability. Key developments in various hydroponic systems are highlighted, including the Nutrient Film Technique (NFT), Deep Water Culture (DWC), Ebb and Flow, Aeroponics, Wick Systems, and Drip Systems. Innovations in these systems focus on optimizing nutrient delivery, oxygenation, and integrating sensors for precise control, improving overall performance and yield. Technological advancements, such as automation and control systems, sensors, and monitoring technologies, have revolutionized hydroponic farming by enabling real-time data collection and environmental management. The integration of the Internet of Things (IoT) and smart farming practices, combined with data analytics and machine learning, has further optimized system performance and decision-making. LED lighting and vertical farming techniques have maximized space utilization and improved crop yields, particularly in urban environments. Advances in nutrient solutions, disease management, and water quality have optimized plant health and resource efficiency. Economic and environmental considerations, including cost-benefit analyses and comparisons with traditional agriculture, highlight the potential for hydroponics to offer better returns on investment and reduce environmental impact. Despite the significant progress, challenges such as high initial setup costs and technical complexities remain. Future research opportunities and interdisciplinary collaborations are essential to address these challenges and drive further innovation in hydroponic farming.

Organic Nonvolatile 2T Memory Cell Employing a NOT-Gate-Like Architecture Toward Binary Output Level With Enhanced Noise Tolerance.

Organic nonvolatile memory has been considered a low-cost memory technology for flexible electronics and Internet-of-things (IoT). However, a major concern is the nonuniformity of memory units, which is primarily caused by random grain boundaries, interface defects, and charge traps, making it difficult to develop high-density reliable memory arrays. This nonuniformity problem would induce read error, which is directly caused by the narrow distribution margin of memory states and low noise tolerance in conventional organic memory cells. To break this limitation, a novel 2T memory cell employing a NOT-gate-like architecture achieving self-enhancing noise tolerance is presented. This unique cell consists of a pair of commonly-gated memory transistors with contradictory "write-and-erase" features. It functions as a voltage divider, producing a well-distinguished binary voltage output capability. The concept and design model of this brand-new 2T memory cell is thoroughly discussed. It is originally characterized by noise-tolerant memory cells irrespective of device nonuniformity. The noise tolerance range of this 2T memory cell is also investigated. The binary voltage-readable memory state with a large noise tolerance range is obtained. Moreover, the conceptual design of the 1T2T FeRAM cell is further developed for low-cost voltage-readable memory technology in wearable electronic applications.

Variance‐driven security optimisation in industrial IoT sensors

AbstractThe Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance‐based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML‐IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

A Study on the Effectiveness of Classifiers in Diagnosing Diabetes Using Machine and Deep Learning Models

Machine learning and data mining have a crucial role in assisting in the early detection of diabetes, potentially resulting in improved health outcomes and more effective management for individuals at risk. This process is frequently termed diabetes prediction or diabetes risk assessment. This paper considers diabetes another-related dataset for applying data mining techniques to find suitable variables for future predictions. Machine learning algorithms can be harnessed in Industrial Internet of Things (IIoT) applications to unlock the advantages of cost reduction, enhanced efficiency, and improved performance. In the modern era, we've all witnessed the benefits of machine learning techniques, from streaming movie services suggesting titles based on viewing habits to identifying fraudulent activity through customer spending patterns. These algorithms excel at handling vast and intricate datasets, uncovering intriguing patterns and trends, including anomalies. This paper considers diabetes another-related dataset data data like age, gender, family diabetes, highbp, physically active, bmi, smoking, alcohol, sleep, sound sleep, regular medicine, junkfood, stress, bplevel, pregancies, pdiabetes, uriationfreq, diabetic. The machine learning approaches which is used to analysis and predict the dataset using Logistic, Multilayer Perceptron, SMO, Decision Stump, Hoeffding Tree, J48, and LMT. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Blockchain-based energy consumption approaches in IoT.

Blockchain is a decentralized electronic account book that is secure, open, and impervious to manipulation, and records transactions across numerous computers. Internet of Things (IoT) deployed sensors are limited in bandwidth, processing power, and battery life. Energy efficiency is a vital factor and a decisive matter for energy-bound IoT-based networks. This paper provides an idea of the working principle of blockchain technology in IoT-enabled energy systems. It investigates the fundamentals of blockchain technology, clarifying its decentralized nature, cryptographic mechanisms, and consensus algorithms that ensure data immutability and transparency. It explores how blockchain technology can be integrated with various IoT fields for energy efficiency. The paper investigates the blockchain-based energy consumption approaches in IoT qualitatively, and quantitatively with future research directions and challenges. It is seen that the hybrid blockchain model is suitable for energy efficiency in IoT.

An innovative framework to securely transfer data through the Internet of Things using advanced generative adversarial networks

The Internet of Things (IoT) is developing so quickly that cloud-centric computing finds it challenging to keep up with the demands of usability and low latency. Edge computing unifies computing, networking, storage and applications into a distributed open system. At the edge of the IoT, it provides intelligent services. The edge network is made up of several wired and wireless networks, and edge nodes have constrained amounts of memory and processing power. The edge network is vulnerable to several types of cyberattacks because of these factors. Large-scale network data gathering and detection for IoT security is also challenging for an IoT edge node to provide. Data analytics for intrusion detection guarantees high accuracy of intrusion detection systems (IDSs), but the implementation of such algorithms on IoT might be a challenge due to the limited resources on edge nodes. Inspired in part by these challenges, we suggest a sophisticated IDS based on a generative adversarial network (GAN). This article suggests a novel method for detecting intrusions in the IoTs networks that make use of Colony Predator Algorithm (CPA) for optimization of the detection process. Through the use of GANs to create real-looking data samples, the suggested technique helps to have reliable anomaly and intrusion detection. The integration improves the training process, making it more efficient and enabling the CPA system to better differentiate between benign and malicious activities, which exponentially raises the system's efficiency.

Implementation of Sensors in IOT Application

In the past few decades, the importance of the Internet of things (IOT) has emerged in various fields. It is a technology that connects the Internet to all devices that are incapable to connect to the Internet. Therefore, this paper aims to provide convenient realization of sensors in the IOT system. The idea based on the start of the IOT, as it is a modern idea that combines ease of work and development, in the company of itself and benefitting of limited communications such as short message service (SMS) and broad communications such as wireless fidelity (WIFI), toward ensure robust and applicable sensors that can be serve the community in several aspects, predominantly people with special needs. The system uses Arduino, event stream processing (ESP), sensors and global system for mobile (GSM) to control devices. The system is simulated by using an electronic circuit via Google assistant and messages application.

Secure Search of Smart Home Data Based on Access Control Encryption

Abstract: In recent years, the increasing variety of smart home devices, such as smart locks, surveillance cameras, and various sensors, has led to a significant rise in related data. To facilitate the sharing of smart home data, manufacturers store these data on the Internet of Things (IoT). However, since IoT servers are not fully trustworthy and the data often contain sensitive commercial information, any leakage could result in a commercial crisis for manufacturers. To address security concerns, manufacturers encrypt the data before storing them on the IoT. A challenge arises: how can users search for target data in encrypted form? This paper proposes a new search encryption scheme for smart home data, addressing the limitations of existing attribute-based encryption (ABE) searchable encryption schemes. The proposed scheme integrates access control trees and bilinear mapping technology, supporting multiple access policies including AND gates, OR gates, and threshold gates. Experimental results demonstrate a comprehensive analysis of the attribute-based searchable encryption protocol under a multi-authority architecture, covering four dimensions: correctness, security, complexity, and real-world application scenarios. In specific scenarios, when a data user, out of privacy concerns, is unwilling to reveal all attribute information to a single authority during a query for shared datathus avoiding identity disclosureor when the users identity information is managed by multiple independent authorities, this protocol proves highly effective. It ensures smooth data sharing while maintaining data security for both data owners and users, fully safeguarding data privacy and shareability.

The design of intelligent highway transportation system in smart city based on the internet of things.

The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway.

Optimized robust learning framework based on big data for forecasting cardiovascular crises.

Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework's processing time is 436s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework's potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy.

A Sturdy Shield: Enhancing 6G Fronthaul with Quantum-Security

Abstract: With the emergence of smart devices and the Internet of Things (IoT), 5G has been developed to meet the growing demand for transmission speeds and bandwidth. 5G overcomes the limitations of previous generations of technology, offering significant improvements in speed, latency, and stability. However, with the advancement of computational capabilities and the advent of quantum computers, security issues have become a pressing concern. Current technologies in 5G fronthaul rely on intrinsic security technologies, which are difficult to defend against quantum computer-based attacks. Consequently, this paper proposes a future mobile fronthaul architecture, such as 6G, which builds upon the 5G fronthaul network architecture while introducing quantum communication based on the BB84 protocol. This architecture enhances privacy protection and improves resistance to eavesdropping while maintaining the original performance. Additionally, the paper models the architecture to explore its noise resistance, using forward and backward spontaneous Raman scattering power as indicators, and demonstrates the system's feasibility under the influence of high-power 6G signals. The potential applications of this architecture are also examined. The results indicate that the quantum-secure 6G fronthaul network represents a significant breakthrough. It not only meets the stringent security requirements of modern communication but also enables a wide range of applications, paving the way for a safer and more interconnected digital future.