Internet Of Things Connectivity Research Articles

IoT- Based Drowsiness Monitoring and Crash Detection System to Enhance Road Safety

This project is a solution designed to address the critical issue of driver drowsiness and enhance road safety. The system leverages the power of the Internet of Things (IoT) to continuously monitor driver alertness and promptly detect potential accidents. It comprises a network of sensors strategically placed within the vehicle, including facial recognition cameras and accelerometers. These sensors work together to capture and analyze a driver's physiological and behavioural data in real-time. Facial recognition technology detects signs of drowsiness such as drooping eyelids and yawning. Through a secure IoT connection, this data is transmitted to a central processing unit within the vehicle. Advanced algorithms process this information, assessing the driver's state of alertness. In the event of detected drowsiness, the system initiates a series of proactive interventions, such as sounding alarms, vibrating the driver's seat, or even communicating with an external monitoring centre for further assistance. Moreover, the system's crash detection capabilities are enabled by the accelerometers, which monitor sudden changes in the vehicle's motion. If a collision or accident is detected, the system promptly sends an alert, including the vehicle's GPS coordinates, to emergency services or designated contacts, facilitating rapid response and potentially saving lives. Keywords:- Drowsiness, Crash Detection, Raspberry Pi, IoT

IoT-driven accessibility: A refreshable OCR-Braille solution for visually impaired and deaf-blind users through WSN

The spectacular concept of integrating OCR with the Internet of Things (IoT) and Wireless Sensor Networks (WSN), presented in this study, intends to increase the autonomy and social inclusion of people with visual impairments and deaf-blindness. This application converts text from photographs into real-time Braille representations by utilizing the capabilities of Optical Character Recognition (OCR) technology, Braille displays, and IoT. Braille is a tactile form of writing which assists the visually impaired by representing letters and numbers with raised dots. Text recognition and conversion through OCR helps people with vision issues by converting printed text into accessible formats like audio or Braille, assisting people in with recognizing things around them. Individuals with visual impairments are given autonomous access to critical information by smoothly integrating modern technologies, boosting their self-confidence and promoting active social involvement. This OCR-Braille approach's primary objective is to minimize the knowledge gap and promote social equality. Users' ease of access to labels, educational resources, and digital information about their environment fosters a sense of empowerment and independence. This ground-breaking study not only eliminates the accessibility gap but also provides the groundwork for a society that is better linked and accessible. Individuals have access to the tools they need for independently navigating their surroundings through simple IoT connectivity and WSN. This research lays the path for a future for assistive technology to catalyze equality and independence, independent of one's visual ability, by fusing assistive technology and IoT connectivity.

Impact of Internet of Things on Home Appliances

The way household appliances operate and communicate with consumers has completely changed as a result of the Internet of Things (IoT). This abstract explores how the Internet of Things affects household appliances, emphasizing how it affects user experience, convenience, and energy efficiency. With the use of smartphones and other smart devices and IoT connectivity, users can effortlessly integrate and control their appliances, giving them the ability to monitor and operate them remotely from any location. Tasks like receiving notifications, changing settings, and even starting appliance functions when away from home are made possible by this convenience. Furthermore, by using automation and data-driven insights, IoT makes it possible for home appliances to operate more energy efficiently. With sensors and connectivity, smart appliances can adjust their performance according to usage patterns and environmental factors in real time, saving energy and money on utility bills. IoT-enabled appliances can also take part in demand response initiatives, which helps with overall energy saving efforts. IoT-enabled appliances greatly improve user experience with their customizable features and user-friendly interfaces. The performance and lifespan of appliances are increased by machine learning algorithms that examine user behavior and preferences to make personalized suggestions, optimize settings, and foresee maintenance requirements. Keywords: Communication, Smart devices, Connectivity, Remotely, Sensors, Automation, Customizable features, User- friendly, Energy efficiently

Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.

Smart Hydroponic Systems: Optimizing Nutrient Levels with IoT Connectivity

Maintaining precise nutrient levels is essential for the thriving success of hydroponic systems. This comprehensive study delves into the intricate interplay of climatic conditions and nutrients, meticulously analysing various parameters like Total Dissolved Solids (TDS), pH, and electrical conductivity (EC) within a sophisticated smart hydroponic vertical Nutrient Film Technique (NFT) setup designed for leafy greens. The innovative system integrates a wireless sensor network and Internet of Things (IoT) connectivity, employing ESP32 microcontrollers as the primary unit alongside complementary hardware components for continuous and real-time monitoring of critical nutrient parameters. The investigation delves into the dynamic relationship between environmental factors and the growth patterns of spinach plants. Through a thorough examination of the extensive dataset gathered via deployed sensors, the study unveils variations in environmental parameters throughout various stages of plant development, including germination and root establishment. The study aims to elucidate the significance of each parameter in optimizing plant growth in hydroponic environments by analysing data associations. Results show higher Total Dissolved Solids (TDS) values correlate with better plant growth, while reduced pH levels harm plants. These findings are crucial for interpreting disease symptoms, aiding farmers in identifying issues early and optimizing yields. Understanding how nutrient values affect plant health enables proactive problem-solving. This research provides valuable insights into smart hydroponic systems, informing precision agriculture decisions.

Smart Hydroponic Systems: Optimizing Nutrient Levels with IoT Connectivity

Maintaining precise nutrient levels is essential for the thriving success of hydroponic systems. This comprehensive study delves into the intricate interplay of climatic conditions and nutrients, meticulously analysing various parameters like Total Dissolved Solids (TDS), pH, and electrical conductivity (EC) within a sophisticated smart hydroponic vertical Nutrient Film Technique (NFT) setup designed for leafy greens. The innovative system integrates a wireless sensor network and Internet of Things (IoT) connectivity, employing ESP32 microcontrollers as the primary unit alongside complementary hardware components for continuous and real-time monitoring of critical nutrient parameters. The investigation delves into the dynamic relationship between environmental factors and the growth patterns of spinach plants. Through a thorough examination of the extensive dataset gathered via deployed sensors, the study unveils variations in environmental parameters throughout various stages of plant development, including germination and root establishment. The study aims to elucidate the significance of each parameter in optimizing plant growth in hydroponic environments by analysing data associations. Results show higher Total Dissolved Solids (TDS) values correlate with better plant growth, while reduced pH levels harm plants. These findings are crucial for interpreting disease symptoms, aiding farmers in identifying issues early and optimizing yields. Understanding how nutrient values affect plant health enables proactive problem-solving. This research provides valuable insights into smart hydroponic systems, informing precision agriculture decisions.

Solar based Smart Sanitizer Dispenser System with Internet of Things (IoT) Technology

In response to the growing demand for efficient and sustainable hygiene solutions, this project introduces a Solar-Powered Smart Sanitizer Dispenser System leveraging IoT technology. The system aims to address the need for touchless and accessible hand sanitization in various environments, including public spaces, healthcare facilities, and commercial establishments. By integrating solar power as the primary energy source, the system offers autonomy and eco-friendliness, reducing dependency on conventional electricity grids and minimizing environmental impact. The core components of the system include a smart dispenser unit equipped with sensors for detecting hand proximity, a reservoir for storing sanitizer solution, and a microcontroller unit responsible for data processing and IoT connectivity. Through the IoT interface, users can remotely monitor sanitizer levels, receive real-time usage statistics, and manage dispenser settings, enhancing operational efficiency and maintenance. Furthermore, the incorporation of solar panels ensures continuous operation even in off-grid locations, making the system suitable for deployment in remote or outdoor settings where access to electricity may be limited. Maximizing the efficacy of solar power conversion and extending operational uptime.

Exploring Universal Filtered Multi Carrier Waveform for Last Meter Connectivity in 6G: A Street-Lighting-Driven Approach with Enhanced Simulator for IoT Application Dimensioning

In the dynamic landscape of 6G and smart cities, visible light communication (VLC) assumes critical significance for Internet of Things (IoT) applications spanning diverse sectors. The escalating demand for bandwidth and data underscores the need for innovative solutions, positioning VLC as a complementary technology within the electromagnetic spectrum. This paper focuses on the relevance of VLC in the 6G paradigm, shedding light on its applicability across smart cities and industries. The paper highlights the growing efficiency of lighting LEDs in infrastructure, facilitating the seamless integration of VLC. The study then emphasizes VLC’s robustness in outdoor settings, demonstrating effective communication up to 10 m. This resilience positions VLC as a key player in addressing the very last meter of wireless communication, offering a seamless solution for IoT connectivity. By introducing a freely available open-source simulator combined with an alternative waveform, UFMC, the study empowers researchers to dimension applications effectively, showcasing VLC’s potential to improve wireless communication in the evolving landscape of 6G and smart cities.

AN ANALYSIS OF THE HETEROGENEOUS IOT DEVICE NETWORK INTERACTION IN A CYBER-PHYSICAL SYSTEM

The article is devoted to the study of existing technologies regarding Internet of Things (IoT) device interaction in a heterogeneous network. Since each smart home appliance can be controlled by a customer who aims to find a cost-effective and easy-to-connect product for their own connected home, there are certain functional limitations for devices from distinct manufacturers that may decrease the intention to merge them all into a single network. A variety of proprietary protocols and communication standards embedded by vendors make their products unable to interact with other vendor devices if the connection standard used is not identical. Also, an IoT product design refers to its own functionality, mainly excluding the possibility of integration into other existing infrastructure. As IoT equipment emerges on the market, the complexity of its connection to a heterogeneous network corresponds to the firmware and the standard unification according to modern demands. It means that potential users might face the necessity of overcoming these issues to achieve high performance in terms of network interoperability. In general, an IoT gateway operating as a middleware might have the potential to enable a network with distinct communication models to operate without failure or data loss. This task requires the received data to be converted into the format in which the data is intended. This paper includes a comparative analysis of existing IoT device interaction standards, connection protocols, and data transfer technologies, evaluating their features for an effective adoption of the proposed network architecture, which can be used to improve the interoperability of heterogeneous IoT devices.

Proactive defense mechanism: Enhancing IoT security through diversity-based moving target defense and cyber deception

The Internet of Things (IoT) has become increasingly prevalent in various aspects of our lives, enabling billions of devices to connect and communicate seamlessly. However, the intricate nature of IoT connections and device vulnerabilities exposes the devices to security threats. To address the security challenges, we propose a proactive defense framework that leverages a model-based approach for security analysis and facilitates the defense strategies. Our proposed approach incorporates proactive defense mechanisms that combine Moving Target Defense techniques with cyber deception. The proposed approach involves the use of a decoy nodes as a deception technique and operating system based diversity as a moving target defense strategy to change the attack surface area of IoT networks. Additionally, we introduce a technique known as Important Measure-based Operating System Diversity to reduce defense cost. The effectiveness of the defense mechanisms was evaluated by using a graphical security model in a Software Defined Networking-based IoT network. Simulation results demonstrate the effectiveness of our approach in mitigating the impact of attacks while maintaining high performance levels in IoT networks.

Penguin search with Harris-Hawk optimization algorithm to improve clustering performance in wireless network

<span>Integrating optimal search algorithm concepts across the wireless core and cluster structure enables next-generation wireless networks to effectively provide reliable low-delay communications and connectivity for internet of things (IoT) devices. This article describes penguin search with the Harris-Hawk optimization algorithm (PHHO) to improve clustering performance in wireless networks. The penguin search optimization algorithm (PSO) algorithm computes the fitness value for feature selection from the database. Harris-Hawk optimization (HHO) algorithm to reduce the time and energy required for network transmission. This mechanism builds the clusters based on node communication range. The node direction, node mobility, node bandwidth availability, and energy parameters to decide the cluster head (CH) by applying the HHO algorithm. This approach uses a PSO algorithm fitness function to select the feature subset to minimize error and overhead in the network. Using a network simulator (NS)-3, this method assesses and chooses the most efficient way for data transmission, and the result is compared to a baseline mechanism.</span>

Multiple Access Design for Symbiotic Radios: Facilitating Massive IoT Connections With Cellular Networks

Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

Optimized LoRaWAN Architectures: Enhancing Energy Efficiency and Long-Range Connectivity in IoT Networks for Sustainable, Low-Power Solutions and Future Integrations with Edge Computing and 5G

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

Pengembangan Media Pembelajaran Matematika Berbasis IoT dengan Pendekatan Problem Based Learning

This research aims to develop an Internet of Things (IoT)-based mathematics learning medium using a Problem-Based Learning (PBL) approach and evaluate its effectiveness in enhancing students' problem-solving skills. The research method employed is Research and Development (R&D), consisting of stages such as needs analysis, media design, prototype development, field trial, and evaluation. The research subjects consist of 11th-grade students at MAN 1 Probolinggo. The IoT-based mathematics learning medium is designed to facilitate dynamic interaction between students and real-world objects through IoT connectivity. This medium allows students to experience direct learning and actively participate in solving mathematical problems in real-life contexts. The results of this research anticipate that the use of IoT-based learning media with a PBL approach significantly improves problem-solving skills compared to conventional learning methods. Furthermore, it is expected that positive responses from students and teachers to this medium can be integrated into existing mathematics curricula. Therefore, the development of IoT-based mathematics learning media with a PBL approach is expected to be an effective alternative in enhancing students' mathematical problem-solving skills. This medium enables students to actively engage in learning, fosters creativity, and connects mathematical concepts with the real world. Additionally, the results of this research are expected to make a positive contribution to the development of innovative and technology-relevant mathematics teaching methods.

EnviroSense Smart Waste System: Revolutionizing Waste Management Through IoT

Abstract: The EnviroSense Smart Waste System is a slice-edge system that uses the power of the Internet of Things (IoT) to transfigure traditional trash operation processes. As urbanization accelerates and environmental enterprises grow a smart and effective waste operation system becomes increasingly important. The EnviroSense Smart Waste System tackles these issues by combining ultramodern detectors, networking, and data analytics to make a complete and long-term waste operation system. The system's introductory operation entails the installation of smart detectors into waste lockers and holders, allowing for real-time monitoring of scrap situations and composition. These detectors use IoT connection to shoot data to a centralized platform, where it's reused by advanced analytics algorithms. The technology gives practicable data, allowing original governments, waste operation enterprises, and other associations to make better opinions.

IoT Integrated Accelerometer Design and Simulation for Smart Helmets

This article delves into the transformative potential of micro-electromechanical systems (MEMS) accelerometers, particularly their role in revolutionizing helmet safety. Accelerometers, ubiquitous in vehicle manufacturing, computer technology, and audio-video systems, play a crucial role in measuring acceleration. This work focuses on the design and simulation of a rotating accelerometer integrated into football helmets. The introduction of rotational acceleration addresses specific challenges in detecting and mitigating brain injuries that linear accelerometers may encounter. Additionally, the article explores the concept of piezoresistive football helmets, designed to resist force and reduce the impact of concussions. Piezoresistive helmets, designed to resist force and minimize the impact of concussions, when augmented with Internet of Things (IoT) capabilities can create a comprehensive smart safety solution. The incorporation of IoT sensors and connectivity into piezoresistive helmets enables real-time monitoring and data analysis of impact forces. This integration not only enhances player safety by providing immediate insights into potential risks but also opens avenues for injury prevention strategies. Leveraging COMSOL simulation software, this research not only conceives but also realizes the innovative integration of rotational accelerometers and piezoresistive materials in football helmet design, advancing safety standards in sports technology.

Context-Aware Device Classification and Clustering for Smarter and Secure Connectivity in Internet of Things

With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variety of heterogeneous devices, ever-changing network structures, and diverse contextual elements. Managing IoT devices effectively is a complex task in maintaining network security.This study introduces a context-driven approach for IoT Device Classification and Clustering, aiming to address the unique characteristics of IoT systems and the limitations of existing access control methods. The proposed context-based model utilizes contextual information such as device attributes, location, time, and communication patterns to dynamically establish clusters and cluster leaders. By incorporating contextual factors, the model provides a more accurate and adaptable clustering mechanism that aligns with the dynamic nature of IoT systems. Consequently, network administrators can configure dynamic access policies for these clusters.

Flood Early Warning System Prototype Based on Ultrasonic Sensor and Internet of Things

Floods that come all of a sudden cause many people to be unable to prepare themselves to deal with it, so material losses to health problems cannot be avoided. Therefore, a system is needed to provide early warning to the public before a flood occurs. As technology develops, water levels in an area can be monitored to anticipate flooding using the Internet of Things (IoT). IoT can help to monitor and warn of floods in real-time and continuously. In this research, the system will be placed in areas that often cause flooding. Monitoring results from sensor readings will be stored in the cloud database. The water level category is divided into 4 levels, namely “Safe”, “Standby”, “Careful”, and “Danger”. The system uses IoT and a database to send water level status to users as notifications on applications. The buzzer will sound as a warning sign when the water level enters the “Danger” status. The system test results show that the sensor has a very good level of accuracy with an error percentage of 0.242131%, and IoT connectivity can reach a distance of up to 20 meters.

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website.

Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things

The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developments enable the healthcare sector to interact with and care for its patients effectively. Every technology that relies on the IoT can have a serious security challenge. Critical IoT connectivity data may be exposed, changed, or even made unavailable to authenticated users in the case of such attacks. Consequently, protecting IoT/IoMT systems from cyber-attacks has become essential. Thus, this paper proposes a machine-learning- and a deep-learning-based approach to creating an effective model in the IoMT system to classify and predict unforeseen cyber-attacks/threats. First, the dataset is preprocessed efficiently, and the Harris Hawk Optimization (HHO) algorithm is employed to select the optimized feature. Finally, machine learning and deep learning algorithms are applied to detect cyber-attack in IoMT. Results reveal that the proposed approach achieved an accuracy of 99.85%, outperforming other techniques and existing studies.