Internet Of Things Enabled Research Articles

Enhancing Security, Authentication and Traceability for Non Standard Internet of Things (IoT) Enabled Devices by Implementing Mandatory IoT Enabled Devices Having Hardware and Software Authentication Cum Registration Mechanism Before Their Usage to Every IoT Enabled Devices Manufacturer Cum Supplier as Well as User

Internet of things (IoT) [1] has emerged a new field of huge numbers of interconnect devices in term of connecting various gadgets with devices together for sharing data between them as well as working together to form a cluster of sensors with actuators for better monitoring and controlling of various processes of agriculture and industrial automation. A differential non standard approach by various vendors and developers of IoT based application has led to impose great challenge and threat in terms of data security and integrity as well as cost on traceability for Government agencies.

IoT Enabled Mushroom Farm Automation with Machine Learning

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

Design of a Compact Wearable Antenna for IoT Enabled Bio-Telemetry in Diagnostic Health Monitoring System

The structure developed involves the etching of Moore’s curve onto a conventional rectangular patch and subsequently encasing it in a rectangular loop along with a defected ground structure. The proposed antenna exhibits dual-band Since the Internet of Things (IoT) enabled medical diagnostic system requires unprecedented precision and automation for better health care delivery, therefore in such systems, the performance of antennas for efficiently transferring data (vital functions of the human body) is an important task. To address these concerns, a mechanically robust, novel hybrid wearable on-body antenna inspired by Moore’s fractal based geometry and conventional rectangular loop for a diagnostic health monitoring system is proposed. with bandwidth ranging from 1.38 - 1.8 GHz and 2.25 - 4.88 GHz respectively, covering WMTS, ISM, and Personal Communication band besides covering a portion of UWB band also. The two operating bands of the antenna offer a fractional bandwidth of 38.5% and 73.7% respectively. It is observed that the average specific absorption rate (SAR) over the multi-layer phantom is 0.025 W/kg for an input power of ≈ 24 dBm. In comparison to the recently reported wearable antennas, the design proposed has a compact footprint of 0.135 λo x 0.093λo x 0.004λo besides offering a dual- band of operation, peak gain of 2.2 dBi, overall radiated efficiency of 95% and SAR below 0.025 W/kg. The design is fabricated using Rogers 5880 substrate (semi-flex) of thickness 0.75 mm.

IoT Enabled 3D Printer Device for Bio models

IoT and embedded electronics is getting popular due to its versatile applications in various fields for domestic, commercial and industrial usage. However electronics or electrical systems that are primarily built using electronics, needs enclosures to protect it from the environment as well as for maintaining its privacy concerns. 3D printers are valuable to rapidly prototype/fabricate/build enclosures for custom applications. The steps, involved in rapid prototyping using 3D printers start from understanding the specification and measurement requirements e.g. colour, look, dimensions, and tolerances. Cura is a popular slicing software used in 3D printing that converts your 3D models (STL files) into a format (G-code) that your 3D printer can understand. Integrating IoT (Internet of Things) with 3D printing using Modified OctoPrint. It can greatly enhance the functionality and accessibility of your 3D printing setup. OctoPrint is an open-source software that allows you to control and monitor your 3D printer remotely via a web interface. By adding IoT capabilities, you can further automate and monitor your printing processes, making them more efficient and comfort.

IoT Enabled Design of an Analog-front End for the In-vitro Tooth Cavity pH sensor and Testing under Laboratory Conditions

The tooth's pH has an impact on the tooth cavity, which mostly develops as a result of cariogenic bacteria interacting with the carbohydrates in meals. For this purpose, pH sensor has been developed along with the in-house developed analog front end. Developed pH sensor shows change in the resistance when exposed to the tooth’s pH. This change in the resistance is measured with the help of a voltage divider circuit followed by a unity gain buffer. Unity gain buffer is used to avoid the loading effect. Further, the output of and buffer is given to the microcontroller and subsequently to an ESP module for Internet of Things (IoT) communication

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

Towards Detection and Attribution of Cyber Attacks in IoT Enabled Cyber-Physical Systems

Internet of Things (IoT) enabled cyber physical systems such as Industrial equipment’s and operational IT to send and receive data over internet. This equipment’s will have sensors to sense equipment condition and report to centralized server using internet connection. Sometime some malicious users may attack or hack such sensors and then alter their data and this false data will be report to centralized server and false action will be taken. Due to false data many countries equipment and production system got failed and many algorithms was developed to detect attack, but all these algorithms suffer from data imbalance (one class my contains huge records (for example NORMAL records and other class like attack may contains few records which lead to imbalance problem and detection algorithms may failed to predict accurately). To deal with data imbalance, existing algorithms were using OVER and UNDER sampling which will generate new records for FEWER class only. Securing Internet of Things (IoT)-enabled cyberphysical systems (CPS) can be challenging, as security solutions developed for general information / operational technology (IT / OT) systems may not be as effective in a CPS setting. Thus, this paper presents a two-level ensemble attack detection and attribution framework designed for CPS, and more specifically in an industrial control system (ICS). At the first level, a decision tree combined with a novel ensemble deep representation learning model is developed for detecting attacks imbalanced ICS environments. At the second level, an ensemble deep neural network is designed for attack attribution.The connection between ICS or IIoT-based systems with public networks, however, increases their attack surfaces and risks of being targeted by cyber criminals.To overcome from this issue, we are introducing novel technique without using any under or oversampling algorithms. The proposed technique consists of 2 parts. Auto encoder: It will get trained on imbalanced dataset and then extract features from it and these extracted features will get trained with DECISION TREE algorithm to predict label for known or unknown attacks. Decision tree get trained on reduced number of features obtained from PCA (principal component analysis) algorithm. Deep Neural Network (DNN): In this level, DNN algorithm get trained on known and unknown attacks. Key Word: Cyber Physical Systems (CPS), Industrial Control System (ICS).

IoT Enable Grass Cutter Robot - Car

Abstract: The IoT Enabled Grass Cutter Robo-Car project represents a transformative leap in the realm of lawn maintenance, merging cutting-edge robotics with the expansive capabilities of the Internet of Things (IoT). At its core, this project introduces a state-of-the-art Robo-Car, meticulously engineered to be remotely controlled through an Android application. The crux of the communication infrastructure is a web server, with the ESP8266 serving as the linchpin by receiving signals from the server. These signals orchestrate the nuanced operations of a L298N motor driver, steering the precise movement of 100 RPM motors, and a relay module that commands the activation of the grass cutter motor. With a vision to redefine the landscape of lawn care, this project stands as an exemplar of efficiency, convenience, and remote accessibility in the realm of smart agriculture.

Blockchain for Cybersecurity in Edge Networks

The Blockchain is one of the most promising and artistic cybersecurity solutions. It has been practiced in a variety of reinforcements, including healthcare, transportation, and Internet of Things (IoT) applications. However, Blockchain has a colossal scalability challenge, limiting its ability to control services with high transaction volumes. Edge computing, on the other hand, was designed to allow cloud services and resources to be deployed at the network's edge, although it now faces issues in terms of decentralized security and management. The unification of edge computing and Blockchain within one solution jar provides a vast scale of storage systems, database servers, and authenticity computation towards the end in a safe fashion. Despite the potential of interconnected edge computing and Blockchain environments, difficulties such as scalability, resource management, function integration, self-organization, and new security concerns must be addressed before broad adoption. This article provides an overview of the secure IoT framework, paradigms, enablers, and security problems of combining Blockchain and intelligent edge computing. Finally, broader viewpoints for future research directions are investigated.

Deep Learning based DDoS Attack Detection in Internet of Things: An Optimized CNN-BiLSTM Architecture with Transfer Learning and Regularization Techniques

In recent days, with the rapid advancement of technology in informatics systems, the Internet of Things (IoT) becomes crucial in many aspects of daily life. IoT applications have gained popularity due to the availability of various IoT enabler gadgets, such as smartwatches, smartphones, and so on. However, the vulnerability of IoT devices has led to security challenges, including Distributed Denial-of-Service (DDoS) attacks. These limitations result from the dynamic communication between IoT devices due to their limited data storage and processing resources. The primary research challenge is to create a model that can recognize legitimate traffic while effectively protecting the network against various classes of DDoS attacks. This article proposes a CNN-BiLSTM DDoS detection model by combining three deep-learning algorithms. The models are evaluated using the CICIDS2017 dataset against commonly used performance criteria which the models perform well, achieving an accuracy of around 99.76%, except for the CNN model, which achieves an accuracy of 98.82%. The proposed model performs best, achieving an accuracy of 99.9%.

IoT Enabled Boat for Cleaning Water Surface

This project introduces an IoT-enabled boat designed for the purpose of cleaning water surfaces. The system utilizes two gear motors to drive a conveyor belt for efficient collection of floating debris. An Arduino Uno, in conjunction with two IR sensors, provides precise control over the conveyor system, ensuring effective garbage collection. Additionally, the boat's overall movement is managed through a controller employing two gear motors and an Arduino IoT Cloud platform. The conveyor system, powered by the gear motors, facilitates the seamless collection of debris present on the water surface. The Arduino Uno, acting as the central processing unit, interprets signals from two IR sensors strategically placed to optimize the efficiency of the garbage collection process. This intelligent control system allows for the autonomous navigation of the boat and precise maneuvering around obstacles. Key Words: IoT (Internet of Things), Water Surface Cleaning, Gear Motors, Conveyor System, Arduino Uno, IR Sensors, Boat Controller, Arduino IoT Cloud, Garbage Collection, Autonomous Navigation, Remote Monitoring, Environmental Cleanup, Sustainability, Pollution Control, Scalable Solution, Real-time Feedback, Smart Technologies, Automation, Water Pollution, Adaptive Systems.

Maritime Communications—Current State and the Future Potential with SDN and SDR

The rise of the Internet of Things (IoT) has opened up exciting possibilities for new applications. One such novel application is the modernization of maritime communications. Effective maritime communication is vital for ensuring the safety of crew members, vessels, and cargo. The maritime industry is responsible for the transportation of a significant portion of global trade, and as such, the efficient and secure transfer of information is essential to maintain the flow of goods and services. With the increasing complexity of maritime operations, technological advancements such as unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and the Internet of Ships (IoS) have been introduced to enhance communication and operational efficiency. However, these technologies also bring new challenges in terms of security and network management. Compromised IT systems, with escalated privileges, can potentially enable easy and ready access to operational technology (OT) systems and networks with the same privileges, with an increased risk of zero-day attacks. In this paper, we first provide a review of the current state and modalities of maritime communications. We then review the current adoption of software-defined radios (SDRs) and software-defined networks (SDNs) in the maritime industry and evaluate their impact as maritime IoT enablers. Finally, as a key contribution of this paper, we propose a unified SDN–SDR-driven cross-layer communications framework that leverages the existing SATCOM communications infrastructure, for improved and resilient maritime communications in highly dynamic and resource-constrained environments.

Brief Overview on Wireless Sensor Network Technologies in the Internet of Things (IoT)

Wireless systems based on IoT-based has evolved rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has expanded across several different applications in real-time. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. Wireless Sensor Network technologies play a vital role in enabling the IoT. In this analysis, we delve deep into the essential elements, functionalities, and applications of WSN technologies within the context of IoT. We explore the diverse sensor nodes, communication protocols, and network architectures that constitute WSNs, highlighting their crucial function in gathering, transmitting, and processing data from the physical environment. The Internet of Things (IoT), which enables seamless connectivity among a variety of physical objects, has completely changed how we interact with the world around us. Wireless Sensor Network (WSN) technologies, which is the central nervous system of this interconnected ecosystem are a fundamental IoT enabler. This paper overview the essential elements, functionalities, and applications of WSN technologies in context of Internet of Things (IoT). We cover the various sensor nodes, communication protocols, and network architectures that make up WSNs in this thorough analysis, highlighting their functions in gathering, transmitting, and processing data from the physical environment. We explore the particular difficulties. WSN technologies find applications across various domains within the IoT landscape. From smart cities and precision agriculture to healthcare and industrial automation, WSNs play a crucial role in transforming these sectors. We showcase real-world case studies that illustrate the practical implementation and impact of WSNs in these domains, providing concrete examples of their effectiveness. Our goal is to provide you with engaging and content that adds value to your understanding of the Internet of Things and Wireless Sensor Networks.

Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach

Water is a limited and invaluable resource that is essential for human survival. Negligence and unregulated water use have brought about a global water crisis. Proper management with a relevant decision and information integration approach can aid water to continue as a renewable resource. The water and wastewater industry must shift from outmoded, inefficient techniques to more sustainable, data-driven solutions to address water concerns and improve public health. The Internet of Things (IoT) has emerged as an innovative strategy for decision and information integration to drive an open-loop Water Value Chain (WVC) efficiently. The IoT-driven network allows objects to connect and communicate, gather data in real-time, analyze data and develop reasonable decision – making insights instantaneously. This study aims to find the enablers of IoT for an open-loop WVC. It examines 25 factors for IoT implementation in the open-loop WVC. The 25 factors are clustered into seven enablers using Principal Component Analysis (PCA). These principal components are analyzed by employing a Multi-Criteria Decision Making (MCDM) approach, i.e., the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which helps to find the cause-effect relationship to prioritize the enablers. The fuzzy set theory is used to address the uncertainty and vagueness in experts' opinions and data deficiency problems. The study reveals that the Ecosystem of an IoT network, IoT network configuration and adaptation and data mobility in an IoT network are the most prominent enablers to consider for the implementation of IoT in an open loop WVC. The study may be helpful for regulatory agencies and enterprises in water distribution and processing for identifying and prioritizing the potential enablers of IoT in an open-loop WVC.

A Smart IoT Enabled End-to-End 3D Object Detection System for Autonomous Vehicles

Integration of advanced signal processing, image processing, deep learning, edge computing, and the Internet of Things (IoT) into vehicles allows intelligent automated vehicles to navigate autonomously in different environments. It is crucial for reliable and safe driving that an autonomous vehicle can accurately, effectively, and efficiently recognize, perceive, and observe the surrounding environments. Autonomous vehicles comprise advanced sensor technologies such as RGB cameras and LiDaR that produce an extensive data set in the form of RGB images and 3D measurement points, also recognized as a point cloud. It is necessary to understand and interpret collected data information efficiently and to identify other road users, such as pedestrians and vehicles. Thus, we introduced a smart IoT-enabled deep learning based end-to-end 3D object detection system that works in real-time, emphasizing autonomous driving situations. The detection model is based on YOLOv3; firstly, the model is utilized for 2D object detection and then modified for 3D object detection purposes. The presented model uses point cloud, and RGB image data as input and outputs detected bounding boxes with confidence scores and class labels. Experiments are carried out on the Lyft data set; results reveal that the YOLOv3 model achieves high accuracy and outperforms from other state-of-the-art detection models in terms of effectiveness and accuracy. The overall accuracy of the model is 96% and 97% for 2D and 3D object detection, respectively.

IoT Enabled Model with Virtual Assistant for Smart Home Applications

The Internet of Things (IoT) has emerged as a prominent technological opportunity, offering numerous professional prospects. It revolves around establishing wireless communication standards between devices and appliances to enhance daily human life. This particular project aims to create a secure and automated home environment by monitoring and controlling various home appliances using Wi-Fi as the primary communication protocol. Traditionally, wireless communication techniques like Wi-Fi and Bluetooth have been utilized for home automation. However, these methods have limitations such as short range and high costs. To overcome these challenges, this paper proposes the techniques of comfy home automation involve the efficient management of appliances like lights and fans, which not only provide convenience but also save time, reduce the need for manual intervention, enhance home security, and send SMS alerts to the owner in case of fire accidents or gas leakages through a dedicated mobile application. Additionally, it aims to train the virtual assistant to engage in enjoyable interactions with the homeowner, further enhancing the user experience. To implement this, this work utilizes the "NodeMCUESP8266" module, which will serve as the foundation for our system. The integration of this technology will prove especially beneficial for individuals who are physically challenged, elderly, or frequently away from home due to their busy schedules. By leveraging the power of IoT and mobile applications, this paper suggests a smart home automation solution that caters to the specific needs and preferences of homeowners, ultimately enhancing their quality of life.

Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System.

The Internet of Things (IoT) in the healthcare system is rapidly changing from the conventional hospital and concentrated specialist behavior to a distributed, patient-centric approach. With the advancement of new techniques, a patient needs sophisticated healthcare requirements. IoT-enabled intelligent health monitoring system with sensors and devices is a patient analysis technique to monitor the patient 24h a day. IoT is swapping the architecture and has improved the application of different complex systems. Healthcare devices are one of the most remarkable applications of the IoT. Many patient monitoring techniques are available in the IoT platform. This review presents an IoT-enabled intelligent health monitoring system by analyzing the papers reported between 2016 and 2023. This survey also discusses the concept of big data in IoT networks and the IoT computing technology known as edge computing. This review concentrated on sensors and smart devices used in intelligent IoT based health monitoring systems with merits and demerits. This survey gives a brief study based on sensors and smart devices used in IoT smart healthcare systems.

IoT Enabled Soil Moisture and Heat Level Prediction Using Chimp Shuffled Shepherd Optimization-Based Deep LSTM for Plant Health Monitoring

Plant health monitoring is a very significant task in any agriculture-based environment. The Internet of Things (IoT) plays an important role in the monitoring of plant diseases. IoT is required to obtain data through sensor nodes for finding soil moisture and heat level. Even though different methods are available to monitor the health of plants, observing heat level and soil moisture still results a complex task. Thus, this paper introduces a novel chimp shuffled shepherd optimization (ChSSO) by the integration of chimp optimization algorithm (ChOA) and shuffled shepherd optimization (SSOA) to perform the selection of cluster head (CH) and routing process. The proposed ChSSO is trained using the deep LSTM which is developed for predicting soil moisture and heat level conditions in IoT network to monitor the health of plants. The proposed method obtained higher performance by the metrics, like testing accuracy and precision of 0.937, and 0.926 for 100 nodes and the values of 0.940, and 0.940 for 150 nodes using the LDAS dataset.

Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks

Abstract With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.