IoT Model Research Articles

Hybrid Approaches (ABAC and RBAC) Toward Secure Access Control in Smart Home IoT

Smart homes are interconnected homes in which a wide variety of digital devices with limited resources communicate with multiple users and among themselves using multiple protocols. The deployment of resource-limited devices and the use of a wide range of technologies expand the attack surface and position the smart home as a target for many potential security threats. Access control is among the top security challenges in smart home IoT. Several access control models have been developed or adapted for IoT in general, with a few specifically designed for the smart home IoT domain. Most of these models are built on the role-based access control (RBAC) model or the attribute-based access control (ABAC) model. However, recently some researchers demonstrated that the need arises for a hybrid model combining ABAC and RBAC, thereby incorporating the benefits of both models to better meet IoT access control challenges in general and smart homes requirements in particular. In this paper, we used two approaches to develop two different hybrid models for smart home IoT. We followed a role-centric approach and an attribute-centric approach to develop HyBAC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{RC}$</tex-math></inline-formula> and HyBAC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{AC}$</tex-math></inline-formula> , respectively. We formally define these models and illustrate their features through a use case scenario demonstration. We further provide a proof-of-concept implementation for each model in Amazon Web Services (AWS) IoT platform. Finally, we conduct a theoretical comparison between the two models proposed in this paper in addition to the EGRBAC model (RBAC model for smart home IoT) and HABAC model (ABAC model for smart home IoT), which were previously developed to meet smart homes' challenges.

Retracted: An IoT and Machine Learning-Based Model to Monitor Perishable Food towards Improving Food Safety and Quality

Retracted: An IoT and Machine Learning-Based Model to Monitor Perishable Food towards Improving Food Safety and Quality

A novel CNN gap layer for growth prediction of palm tree plantlings.

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

An IoT Based System for Remote Patient Monitoring

Following a surgical procedure, patients are monitored in an ICU until physically stable, after which are discharged to a ward for further evaluation and recovery. Usually, ward evaluation does not imply continuous physiological parameters monitoring and therefore patient relapse is not uncommon. The present paper describes the steps taken to design and build a low-cost modular monitoring system prototype. This system aims to offer mobile support in order to facilitate faster and better medical interventions in emergency cases and has been developed using low-power dedicated sensor arrays for EKG, SpO2, temperature and movement. The interfaces for these sensors have been developed according to the IoT model: a central control unit exposes a Restful based Web interface that ensures a platform agnostic behaviour and provides a flexible mechanism to integrate new components.

INTERNET OF THINGS AND BIG DATA IN THE CONTEXTS OF EDUCATION AND SCIENCE

In this paper we discuss the IoT applications, challenges, communication models, layers of IoT, features of IoT, Big data V’s models, big data platforms. In the contexts of education and science. We discuss that big data and IoT is an important factor in our lives. It has a significant impact on all fields of science. Every field like computer science, medical, education, entertainments industry etc. Even we communicate over the internet with our beloved once by the help of IoT communication models. Hence, in this paper we discuss IoT and big data in detail

AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things

Addressing the COVID-19 epidemic since December 2019 has emphasized the criticality of timely and error-free identification of infected COVID-19 individuals in medical settings. To effectively combat the epidemic, the utilization of deep TL-enabled automated COVID-19 identification on CXRs is paramount. This study recommended a real-time IoT system employing ensemble deep TL to enable early identification of infected COVID-19 individuals. The system allows for real-time transmission and identification of COVID-19 suspicious individuals. The suggested IoT model incorporates several DL models, including InceptionResNetV2, VGG16, ResNet152V2, and DenseNet201. These models, stored on a cloud server, are utilized in conjunction with medical sensors to gather chest X-ray data and detect infections. A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. The comparative investigation demonstrates that the suggested approach facilitates swift and effective diagnosis of COVID-19 suspicious patients, providing valuable support to radiologists. This work highlights the significance of leveraging deep transfer learning and IoT in achieving early identification of suspected COVID-19 patients. The proposed system, incorporating a deep ensemble model, offers a practical solution for assisting radiologists in efficiently diagnosing COVID-19 cases

Interference Cancellation Scheme Based on Network Coding in Blockchain-Enabled Internet of Things

In the face of an explosive increase in the number of IoT devices, the IoT model using blockchain technology, like the traditional IoT system, has a common problem, that is, the communication quality is seriously affected by various interferences, which directly limits the improvement of the service quality of the blockchain-enabled IoT system. Therefore, it is of great practical significance to study the interference problem in the existing Internet of Things system using blockchain technology and propose methods to reduce interference. This paper compares and analyzes the performance of two-slot and three-slot network coding enhanced systems, and designs a new MAC layer protocol for blockchain-enabled IoT systems based on network coding technology. On this basis, through enhanced information transmission at the MAC layer and network coding at the physical layer, the multi-layer collaborative communication method is used to reduce the interference between the IoT system devices enabled by the blockchain. The simulation results show that the use of network coding technology can effectively reduce the mutual interference between data sending nodes in the blockchain-enabled IoT system.

IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT.

A smart energy IoT model based on the Itsuku PoW technology

Based on the blockchain + software defined network(SDN) technology, we explore and research a new smart energy networks IoT architecture model of distributed trust to solve the “stuck neck” problems such as bandwidth constraints and trust obstruction of the current centralized network architecture of smart energy networks. In order to achieve efficient data flow and sharing and co-governance. Based on the MTP-Argon2 hash function algorithm in the Itsuku PoW technical solution, we add computing power optimization and correction multi-objective optimization operations, and use the network control right separation technology of the FS-Open Security SDN model to establish local database access policies and storage credentials. The traditional hardware devices are separated from the centralized network architecture of the smart energy network into a hybrid network architecture model of distributed trust. The results show that the distributed trust hybrid network architecture model achieves low latency and circumvents network bandwidth limitations, reflecting the energy efficiency gain and security stability of the model, as well as network load robustness.

Doctor unpredicted prescription handwriting prediction using triboelectric smart recognition

Background Handwriting digits and character recognition have become more important in the digitized world due to the practical applications and to pass information. Different recognition systems have been developed and proposed to be used in different fields where high classification efficiency is needed. Still, there has the issue of lowering the recognition rate. Aim The main objective of this study is to construct an energy-efficient IoT model for doctor unpredicted prescription handwriting prediction that allows the medical words for recognition system to ensure efficient medicine service with the help of a smart recognition system. Methods The integration of IoT for recognizing handwriting characters is more efficient and robust than other techniques. This approach of proposing and constructing an energy-efficient model for doctor unpredicted prescription handwriting prediction allows medical word recognition with the help of triboelectric smart recognition. Results The result yields the digital twin for finding the character, health, and usage monitoring systems for individual prescription with the energy-efficient IoT-related ROI per Optical character recognition, Sensitivity analysis, Accuracy, Sensitivity, and specificity. Interpretation The observational investigation predicts that this article discusses the proposed technique’s design and architecture, which proves its methodology’s effectiveness, architecture, and successful implementation of its technology.

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model.

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies-first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions.

Multi-Story Building Model for Efficient IoT Network Design

This article presents a new network model for IoT that is based on a multi-story building structure. The model locates network nodes in a regular, cubic lattice-like structure, resulting in an equation for the signal-to-noise ratio (SNR). The study also determines the relationship between traffic density, network density, and SNR. In addition, the article explores the potential of percolation theory in characterizing network functionality. The findings offer a new approach to network design and planning, allowing for selecting a network topology that meets criteria and requirements while ensuring connectivity and improving efficiency. The developed analytical apparatus provides valuable insights into the properties of the network and its applicability to specific conditions.

Design of information and measurement systems within the Industry 4.0 paradigm

The subject of the study is the process of Information and Measuring System (IMS) designing as a component of the Cyber-Physical System (CPS) in the paradigm of Industry 4.0 (I4.0). The aim of the study is to develop methodological support for the design of IMS and Automatic Control Systems (ACS) as components of production CPS (CPPS), in particular, for Digital Factory. Objectives: to determine the conceptual model of IMS; choose the IoT model for structural synthesis, select the appropriate regulatory (sets of standards and implementation models) and hardware; perform R&amp;D of IMS based on NB-IoT sensors; formalize the procedure for integrating components into the CPPS, develop the Asset Administration Shell. The methods used are: heuristic synthesis methods, experimental planning theory. The following results were obtained. The key role of optimally designed IMS level 4.0 in increased decision-making accuracy in CPPS management and control processes is demonstrated. The quality of control is improved both by quickly obtaining accurate information for updating models in cyber add-ons, and at the physical level, in ACS. The universal model of IMS implementation in CPPS was proposed. The stages of choosing the concept, structure, hardware and communication protocols of the IIoT ecosystem IMS + ACS have been performed. The methodology was tested during the development of the NB-IoT Tech remote monitoring system, which has a decentralized structure for collection data on resources consumed. The integration of the ecosystem as a component of CPPS at the appropriate levels of the architectural model RAMI 4.0 has been performed. Regulatory support has been formed and the functional aspect of the Asset Administrative Shell for CPPS integration has been developed. Conclusions. Scientific novelty: it is proposed to design the IMS as a component of the AAS of the cyber-physical system, according to the implementation methodology of its subsystems at the corresponding levels of RAMI4.0 and the selected IoT model. The new approach, called "soft digitalization", combines the approaches of Industry 3.0 and 4.0, it is designed for the sustainable development of automated systems to the level of cyber-physical systems and is relevant for the recovery of the economy of Ukraine. Practical significance of the results: the IoT-Tech system based on Smart sensors has been developed and tested. This information and measurement system is non-volatile and adapted to measure any parameters in automated systems of various levels of digitization.

Decentralizing Online Food Delivery Services: A Blockchain and IoT Model for Smart Cities

With the worldwide pandemic outbreak, the style of restaurant food consumption underwent a major shift towards online delivery services. This poses an urgent need in trust between main stakeholders of the process: requiring restaurants to correspond to the quality declared, providing high quality and safe to consume meals, and obliging delivery entities to deliver food carefully, scrupulously following the delivery conditions. In this research, we explore a novelty approach that combines IoT, Blockchain technology, and city LoRa network to create a new trusted, decentralized approach for food distribution process in the context of a Smart City. This approach allows controlling the food delivery process using sensors data to control live location, temperature, vibrations, and shakings during the transportation process. We also suggest a fresh perspective on a rating system of delivery entities, where reputation points will be provided both from the side of the restaurant and the final consumer. This will create more trust towards the delivery entity since information will be tamper-proof and immutable due to the nature of Blockchain. This novel system proposal allows rethinking the online food delivery process in the context of Smart City, using the city’s LoRa LPWAN radio frequency technology and Blockchain decentralized solution.

An IoT aware nature inspired Multilayer Hybrid Dropout Deep-learning paradigm for waste image classification and management

AbstractIn this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

Correction to: Intrusion Detection Model for IoT Using Recurrent Kernel Convolutional Neural Network

Correction to: Intrusion Detection Model for IoT Using Recurrent Kernel Convolutional Neural Network

Foreword of Special Issue on : Current Research Trends in Secure Computer Vision, IoT and Machine Learning

Security has been the most perpetual domain, with advancements in other domains and applications like Computer vision, IoT and Machine learning, is today’s most rapidly growing technical domains to facilitate better human life across the globe and is highly dominated with statistics and data analytics using data science, artificial intelligence, and Machine Learning. All these application areas have developed new security requirements. Recent progress in these fields has been driven both by the development of new learning algorithms and theory by the ongoing explosion in the availability of online data and low-cost computation. With more advancements in technology compelled with need of scaling has provided a challenge to security of system and its architecture. Thus there is a diehard need for secure models or architectures in domains of computer vision, IoT models and Data analytics using Machine Learning.

Synergizing IoT, IoE, GSM Technology, and Deep Learning Models for Advanced Security Applications: A Comprehensive Overview

Synergizing IoT, IoE, GSM Technology, and Deep Learning Models for Advanced Security Applications: A Comprehensive Overview

An adaptive distributed access control model for IoT and fog computing environments

An adaptive distributed access control model for IoT and fog computing environments

Fog-based smart building IoT model: development and energy cost estimation

Fog-based smart building IoT model: development and energy cost estimation