Internet Of Things Research Articles

SECURING INTERNET OF THINGS-ASSISTED CONSUMER ELECTRONICS USING BLOCKCHAIN WITH DEEP LEARNING-BASED USER AUTHENTICATION

Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results.

Performance-Security Analysis in O2O Interactions in Future 6G Communications

In traditional mobile networks, trust between subscribers and their serving networks relies on a hardware root-of-trust: the Subscriber Identity Module (SIM). Conversely, trust between service and home networks is established via Trusted Third Parties (TTPs), known as Clearing Houses (CHs). The 6G environment will witness a substantial increase in subscriber numbers, driven by the mass deployment of the Internet of Everything (IoE) and improvements in network performance. Simultaneously, the performance capabilities required of TTPs to manage trustworthy operator-to-operator (O2O) interactions in 6G must align with the demands of the 6G ecosystem. This work focuses on enhancing CH intermediation capabilities to support O2O trustworthy interactions within the 6G context. Given the close connection between performance and trustworthiness, this paper explores these aspects by modeling interactions between communication parties using a Petri Net model. This model is applied to analyze the quantitative relationships among the non-functional requirements of future 6G communication scenarios, considering both traditional and blockchain-based approaches.

Power beacon-assisted energy harvesting symbiotic radio networks: Outage performance

The evolution of next-generation Internet-of-Things (IoT) in recent years exhibits a unique segment that wireless communication paradigms are oriented towards not only improved spectral efficiency transmission but also energy efficiency. This paper addresses these critical issues by proposing a novel communication model, namely power beacon-assisted energy-harvesting symbiotic radio. In particular, the limited energy primary IoT source communicates with its destination by first harvesting energy from a dedicated power beacon and then performing information exchange, while the backscatter device communicates by exploiting the available radio frequency emitted by the primary IoT source. The destination uses successive interference cancellation mechanisms to decode both its received signals. To assess the performance quality of the proposed communication model, we theoretically derive the coexistence outage probability (COP) in terms of highly accurate expressions and upper-bound and lower-bound approximations. Subsequently, we carry out a series of numerical results to verify the developed theory frameworks on the one hand, and on the other hand, analyze the COP performance against the variations of system key parameters (transmit signal-to-noise ratio, the time-splitting coefficient, the energy conversion efficiency factor, the reflection coefficient, and the coexistent decoding threshold). Our numerical results demonstrate that the proposed communication model can potentially work well in practices with reliable communication over 90% (COP is less than 0.1). Additionally, it also demonstrates that optimizing the reflection coefficient at the backscatter device can facilitate achieving minimal COP performance.

Integrating Renewable Energy with Internet of Things (IoT): Pathways to a Smart Green Planet

The global use of IoT-enabled devices powered by renewable energy can create a smart, efficient, and eco-friendly network. By leveraging data analytics and machine learning, these systems can predict energy consumption, optimize resources, and maintain renewable energy assets proactively. IoT device adoption is rapidly increasing, with a projected 13% growth in 2024, bringing the total to 18.8 billion devices globally. However, there is a critical need for advanced applications of IoT technology, improved sensors implementation, and up-to-date software development to control IoT devices which is essential to fully harness renewable energy and combat global warming, ultimately achieving the vision of a smart green planet. This review examines the operational and technical needs of IoT devices, their integration with various renewable energy sources using Raspberry Pi or Arduino platforms equipped with sensors (such as motion, vibration, flow, and gyroscope), and the application of machine learning in IoT devices to showcase the potential of these technologies in fostering a smart, green world. To further this investigation, the study thoroughly examined the pros and cons of integrating various renewable energy sources to develop smart systems, such as smart grids, smart cities, smart transportation, and smart waste collection. Additionally, this study also examines the challenges faced by smart IoT systems, providing critical analysis and identifying promising directions for future advancements.

Digital Transformation in Brazilian Agribusiness: Impact of Agtechs and Iot on Production and Sustainability

Objective: The objective of this study is to investigate the impact of technological innovations, especially the Internet of Things (IoT), on the optimization of agricultural production and the sustainability of Brazilian agribusiness, focusing on AgTechs and the challenges they face. Theoretical Framework: The study analyzes the current context of agribusiness in Brazil, identifying its main characteristics and trends. It examines Agriculture 4.0 and the implementation of innovations, focusing on the potential of IoT in agricultural production with practical examples and benefits. It also characterizes Brazilian AgTechs, exploring growth opportunities and the broad challenges they encounter. Method: Utilizing a qualitative, exploratory approach based on a narrative literature review, relevant articles, theses, and reports from the last 10 years on the use of IoT and technological innovation in Brazilian agribusiness were analyzed. Results and Discussion: The results indicate that IoT offers significant benefits at all stages of production; however, AgTechs face social, technological, economic, and regulatory obstacles, highlighting the need for public policies and funding for large-scale technological adoption. Research Implications: The research suggests that public policies aimed at improving rural infrastructure and encouraging technological adoption are essential, especially for small producers. Originality/Value: This study contributes by exploring the impact of IoT on Brazilian agribusiness, highlighting the role of AgTechs in Agriculture 4.0. The originality lies in the analysis of the challenges faced by startups and the practical application of these innovations. The value of the research is in promoting technological adoption, improving productivity, and sustainability in the agricultural sector.

An IoT Approach Enhanced with AI Capabilities for Predictive Neurology in Smart Homes

<p dir="ltr"><span>The integration of Artificial Intelligence (AI) and Internet of Things (IoT) offers remarkable chances for improving healthcare in smart home environments. In order to anticipate and observe neurological disorders, the study explores the use of IoT devices along with AI analytics. The goal is to identify early signs of any decline in cognitive functions or neurodegenerative conditions such as Alzheimer’s or Parkinson’s. The research emphasises on examining a framework for predictive neurology while it faces challenges associated with real-time data processing and prediction precision. The outcomes should result in a smart home ecosystem development that focuses on healthcare interventions and enhances life quality. This paper explores a multi-layered architecture, including an exploratory phase to analyse user needs, a developmental phase to design and integrate IoT devices, Machine Learning algorithms, and cloud architectures, as well as a validation phase to undergo rigorous testing in simulated environments to evaluate performance metrics.</span></p><div><span><br /></span></div>

Artifical Intelligence-Based Smart Security System Using Internet of Things for Smart Home Applications

This study presents the design and development of an AI-based Smart Security System leveraging IoT technology for smart home applications. This research focuses on exploring and evaluating various artificial intelligence (AI) and Internet of Things (IoT) options, particularly in video processing and smart home security. The system is structured around key components: IoT technology elements, software management of IoT interactions, AI-driven video processing, and user information delivery methods. Each component’s selection is based on a comparative analysis of alternative approaches, emphasizing the advantages of the chosen solutions. This study provides an in-depth discussion of the theoretical framework and implementation strategies used to integrate these technologies into the security system. Results from the system’s deployment and testing are analyzed, highlighting the system’s performance and the challenges faced during integration. This study also addresses how these challenges were mitigated through specific adaptations. Finally, potential future enhancements are suggested to further improve the system, including recommendations on how these upgrades could advance the functionality and effectiveness of AI-based Smart Security Systems in smart home applications.

Route Optimization for Home Healthcare Vehicles

Home health care (HHC) refers to a range of services provided at home after illness or injury, offering the benefits of reduced costs and increased convenience. However, HHC involves moving medical staff to the patient's home, thus generating new challenges to overcome such as vehicle routing problem (VRP). Heuristic methods and exact methods have been proposed for solving VRP problems. In recent years, smart healthcare 4.0 solutions and intelligent transportation systems (ITS) based on internet of things (IoT) technologies have been developed to address emerging challenges while improving service quality and reducing time and costs. The introduction of intelligent system to health care can reduce the labor intensity of medical staff, save costs and enhance the medical experience of patients. In this context, this paper gives a review of some approaches to solve VRPs. A combined approach using exact method, home healthcare, and ITS is developed to determine a reliable route optimization solution for home healthcare vehicles. The solution was optimized considering traffic congestion levels during travel. The proposed solution involves finding the optimal route, then in real time allows to bypass the traffic blockages.

Digital monitoring of heavy equipment: advancing cost optimization and operational efficiency

The monitoring of heavy equipment using digital technologies has become a crucial solution for cost optimization and improving operational efficiency in essential sectors such as construction, mining, and infrastructure. The integration of technologies such as advanced sensors, the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI) enables proactive management of machines, allowing for early failure detection, real-time monitoring, and the adoption of predictive maintenance. These advancements result in significant reductions in downtime, extend the lifespan of equipment, and minimize emergency repair costs. The research by Khan et al. (2022), Ma et al. (2020), and Rohith et al. (2023) demonstrates how technological innovations such as AI, ML, BIM, and GIS are transforming maintenance strategies. These tools not only optimize machine performance but also provide intelligent, data-driven decision support, creating a more efficient and sustainable environment. The implementation of these technologies allows for the integration of new automated systems, which can revolutionize equipment management in various sectors. However, the adoption of these technologies also faces challenges, as indicated by Dagsa et al. (2022) and Shekari and Ray (2024), who highlight the limitations of monitoring systems and potential contractual disputes. While the benefits are evident, the excess of data and contractual complications require managers to adopt balanced solutions to ensure that risks are minimized while benefits are maximized. In summary, digital technologies in heavy equipment monitoring represent a significant advancement, offering a clear path toward long-term sustainability and operational efficiency.

Streamlined Inkjet‐Printing of Stretchable Organic Photodetectors

AbstractThe rapid development of the Internet of Things (IoT), artificial intelligence, robotics, and wearable electronics has created new challenges and increased the demand for advanced sensing technologies. These sensors must not only exhibit tailored performance but also offer new properties like stretchability and flexibility while being compatible with industrial manufacturing. In this work, a streamlined and simplified method is introduced for fabricating stretchable Organic Photodiodes (OPDs) relying solely on industry‐grade inkjet‐printing. It is demonstrated that printed stiff islands effectively decouple strain from the stretched substrate and serve as a chemically resistant and mechanically rigid platform for printed OPDs. Printing difficulties that arose while processing the Ag electrodes on the curved island surfaces are overcome by a two‐step printing process. Finally, the study reports fully printed OPDs exhibiting peak responsivities (λ = 730 nm) of 340 mA W−1 and specific detectivities (D*) of 2.8 · 1011 Jones at 100 Hz, both measured at −2 V. The light sensors remained functional for over 600 mechanical stretching cycles at strains up to 7%.

Self-powered wireless sensing technologies based on triboelectric nanogenerator.

With the rapid development of the Internet of Things (IoT), sustainable energy supply and wireless transmission of distributed wireless sensor nodes have become a challenge. In recent years, self-powered wireless sensing technologies (SWSTs) combining triboelectric nanogenerators (TENG) and wireless solutions have been proposed to address the issues of energy harvesting and wireless transmission. This review systematically summarizes the research advances in SWSTs based on TENG, and compares the advantages and disadvantages of different technologies. In addition, challenges and expectations for future TENG-based SWSTs are discussed as well.&#xD.

Smart Shopping Cart

Recently, many attempts have been made to reduce the time required for payment in various shopping environments. In addition, with advancements in artificial intelligence and IoT technologies, it has become easier to create unmanned environments for shopping, reducing the need for human intervention. This paper proposes a smart shopping cart system based on low-cost IoT equipment and deep learning object detection technology. The system consists of a camera for real-time product detection, an ultrasonic sensor as a trigger, a weight sensor to determine if a product enters or exits the shopping cart, and a smartphone app providing a virtual cart interface. The server uses YOLO, an object detection library, for product recognition. Communication occurs via TCP/IP and HTTP, allowing users to monitor items in their cart and make automatic payments. This system aims to offer a high cost-performance ratio for implementing unmanned stores. Key words: Deep Learning ,Real-time Object Detection, YOLO , Internet of Things , Smart Shopping Cart

Deep learning technology: enabling safe communication via the internet of things

IntroductionThe Internet of Things (IoT) is a new technology that connects billions of devices. Despite offering many advantages, the diversified architecture and wide connectivity of IoT make it vulnerable to various cyberattacks, potentially leading to data breaches and financial loss. Preventing such attacks on the IoT ecosystem is essential to ensuring its security.MethodsThis paper introduces a software-defined network (SDN)-enabled solution for vulnerability discovery in IoT systems, leveraging deep learning. Specifically, the Cuda-deep neural network (Cu-DNN), Cuda-bidirectional long short-term memory (Cu-BLSTM), and Cuda-gated recurrent unit (Cu-DNNGRU) classifiers are utilized for effective threat detection. The approach includes a 10-fold cross-validation process to ensure the impartiality of the findings. The most recent publicly available CICIDS2021 dataset was used to train the hybrid model.ResultsThe proposed method achieves an impressive recall rate of 99.96% and an accuracy of 99.87%, demonstrating its effectiveness. The hybrid model was also compared to benchmark classifiers, including Cuda-Deep Neural Network, Cuda-Gated Recurrent Unit, and long short-term memory (Cu-DNNLSTM and Cu-GRULSTM).DiscussionOur proposed technique outperforms existing classifiers based on various evaluation criteria such as F1-score, speed efficiency, accuracy, and precision. This shows the strength of the approach in threat detection and highlights the potential of combining SDN with deep learning for IoT vulnerability assessment.

Ambient Stable CsCu2I3 Flexible Gas Sensors for Reliable NO2 Detection at Room Temperature.

The advancement of the Internet of Things and artificial intelligence has increased the demand for air quality monitoring to protect human health. Nitrogen dioxide (NO2), a hazardous pollutant, causes inflammatory responses, even at low concentrations, necessitating sensitive gas sensors. Although metal oxide semiconductor sensors are commonly used, their high-operating temperature and reliance on additional heaters limit miniaturization and increase power consumption. Here, a lead-free, transparent, and flexible CsCu2I3 halide perovskite gas sensor was developed, demonstrating high sensitivity (1509% to 5 ppm) and selectivity for NO2 detection at room temperature with full recovery. The sensor exhibited high stability in ambient air and high-humidity environments, which is not achievable with traditional halide perovskite sensors. First-principles density functional theory calculations revealed the mechanisms underlying its stability and sensitivity. This study highlights the potential of halide perovskites for low-power gas sensing at room temperature, addressing critical challenges for commercially viable wearable applications.

IoT and Machine Learning Techniques for Precision Beekeeping: A Review

Integrating Internet of Things (IoT) devices and machine learning (ML) techniques holds immense potential for transforming beekeeping practices. This review paper offers a critical analysis of state-of-the-art IoT-enabled precision beekeeping systems. It examines the diverse sensor technologies deployed for honeybee data acquisition, delving into their strengths and limitations, particularly regarding accuracy, reliability, energy sustainability, transmission range, feasibility, and scalability. Furthermore, this paper dissects prevalent ML models used for bee behaviour analysis, disease detection, and colony monitoring tasks. This paper evaluates their methodologies, performance metrics, and the challenges involved in selecting appropriate machine learning algorithms. It also examines the influence of sensing devices, computational complexity, dataset limitations, validation procedures, evaluation metrics, and the effects of pre-processing techniques on these models’ outcomes. Building upon this analysis, this paper identifies key research gaps and proposes promising avenues for future investigation. The focus is on the synergistic use of IoT and ML to address colony health management challenges and the overall sustainability of the beekeeping industry.

Advancing IoT Security with an Innovative Machine Learning Paradigm for Botnet Attack Detection

INTRODUCTION: In contemporary society, everyday operations are greatly improved by the Internet of Things (IoT), which connects physical devices to provide digital services. IoT technology offers unified services and streamlines activities across various domains, ranging from remote monitoring to sophisticated welfare systems. However, the growing number of IoT devices presents a security concern. Many of these devices are susceptible to exploitation, leading to diverse vulnerabilities. OBJECTIVES: Resource-constrained IoT devices become prime targets for botnet attacks, manifesting in various forms and penetration methods. Despite numerous research efforts introducing multiple approaches for detecting botnet attacks in IoT, existing methods often fail to achieve satisfactory detection rates. METHODS: Additionally, these approaches struggle to comprehensively analyze the diverse communication networks within the expansive realm of IoT devices. This study proposes an innovative machine-learning framework for detecting IoT botnet threats to address these limitations. RESULTS: This conceptual framework exhibits a remarkable capability to identify a spectrum of botnet attacks, showcasing a detection accuracy of 99.5 per cent, significantly surpassing the performance of other prevalent machine-learning approaches. CONCLUSION: Through this research, we aim to enhance the security paradigm of IoT networks, ensuring robust protection against evolving botnet threats in the dynamic landscape of interconnected devices.

A Review of Academic and Patent Progress on Internet of Things (IoT) Technologies for Enhanced Environmental Solutions

Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting the efficacy of these solutions in addressing environmental impacts. This paper addresses these challenges by reviewing recent developments in IoT technologies, encompassing sensor networks, computing frameworks, and application layers for enhanced pollution management. A comprehensive analysis of 74,604 academic publications and 35,000 patent documents spanning from 2008 to 2024 is conducted using a textual analysis that combines quantitative bibliometric methods along with a qualitative analysis based on both scholarly research and patent innovations. This approach allows us to identify key challenges in IoT implementation for environmental monitoring—including integration, interoperability, and scalability issues—and to highlight corresponding architectural solutions. Our findings reveal emerging technology trends that aim to overcome a few of these challenges, and we present a scalable IoT architecture as key discussions that enhances system interoperability and efficiency for pollution monitoring. This framework provides targeted solutions for specific tasks in pollution monitoring while guiding decision-makers to adopt solutions effectively.

Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment

In the present scenario, the Internet of Things (IoT) and edge computing technologies have been developing rapidly, foremost to the development of new tasks in security and privacy. Personal information and privacy leakage have become the main concerns in IoT edge computing surroundings. The promptly developing IoT-connected devices below an integrated Machine Learning (ML) method might threaten data confidentiality. The standard centralized ML-assisted methods have been challenging because they require vast numbers of data in a vital unit. Due to the rising distribution of information in many systems of linked devices, decentralized ML solutions have been required. Federated learning (FL) was proposed as an optimal solution to discover these privacy issues. Still, the heterogeneity of systems in IoT edge computing environments poses an essential task when executing FL. Therefore, this paper develops an Intelligent Deep Federated Learning Model for Enhancing Security (IDFLM-ES) approach in the IoT-enabled edge-computing environment. The presented IDFLM-ES approach aims to identify unwanted intrusions to certify the safety of the IoT environment. To accomplish this, the IDFLM-ES technique introduces a federated hybrid deep belief network (FHDBN) model using FL on time series data produced by the IoT edge devices. Besides, the IDFLM-ES technique uses data normalization and golden jackal optimization (GJO) based feature selection as a pre-processing step. Besides, the IDFLM-ES technique learns the individual and distributed feature representation over distributed databases to enhance model convergence for quick learning. Finally, the dung beetle optimizer (DBO) model is utilized to choose the effectual hyperparameter of the FHDBN model. The simulation value of the IDFLM-ES methodology is verified using a benchmark database. The experimental validation of the IDFLM-ES methodology portrayed a superior accuracy value of 98.24% compared to other models.

Prioritising elements of digitalisation for lean and green SME operations: an ISM-MICMAC study in the Indian context

PurposeThe purpose of this paper is to identify the key elements of digitalization for lean and green operations and develop a conceptual framework for their implementation. The paper focuses on small and medium-sized enterprises (SMEs) and aims to explore the role of digitalisation in enhancing their operational efficiency and sustainability. By identifying key factors and metrics related to digitalisation, the paper seeks to provide insights for strategic management to improve lean and green practices in SMEs.Design/methodology/approachInterpretive structural modeling (ISM) and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) approaches were deployed to classify the major dimensions of digitalisation. These methods were used to analyse the direct and indirect relationships among the identified elements of digitalisation. A comprehensive literature review and expert consultations were conducted to identify 13 key elements relevant to lean and green operations. The experts also assisted in determining the contextual relationships between the variables for the ISM model.FindingsThe analysis classified the 13 identified elements of digitalisation into different levels according to their driving power and dependence. The results from the ISM model indicated three levels of classifications. At level-1, Internet of things (IoT) and smart sensors (IoT & SS), automation and robotics directly influence lean and green operations. At level-2, real-time monitoring and control system and at level-3 fundamental elements of digitalisation such as big data analytics, predictive maintenance, cloud computing, energy management systems (EMSs), additive manufacturing, blockchain, digital workflow automation and digital collaboration platform.Originality/valueAll elements are interrelated and essential for making strategic decisions. This study emphasis the significance of prioritising these attributes to attain long-term excellence through digitalisation. For the industries that seek the reward of lean and green operations for their growth, this paper has great practical utility. Identifying the key factors of digitalisation would help strategic managers in handling lean and green environment of SMEs through these aspects.

A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things.

With bio-medical wearables becoming an essential part of Internet of Medical things (IoMT) for monitoring the health of workers, patients and others in different environments, antenna play a pivotal role in such wearables. In this communication, a novel Horse shoe shaped antenna (HSPA) meant for such wearables is presented. The vitals of the workers, patients etc. are collected and sent to the IoMT platform for ensuring their safety and monitoring their physical wellbeing. In this article, regression-based Machine learning (ML) techniques are used to facilitate the design of Horse shoe shaped patch antenna to predict the frequency of operation, radiation efficiency and Specific Absorption Rate (SAR) values to accelerate its design process for on-body applications. The HSPA designed resonates at 2.45 GHz in the frequency band of 1.75-2.98 GHz with SAR of 1.89 W/kg for an input power of 16.98 dBm, peak gain of 1.91 dBi and radiation efficiency of 62.07% when mounted on the human body. 1080 samples of data comprising of three EM parameters have been generated using a conventional EM tool by varying the physical and electrical parameters of the design. A detailed comparison of the five regression-based ML algorithms is presented, and it is observed that the ML models help in efficient use of resources while designing an antenna for bio-medical applications.