Internet Of Things Systems Research Articles

Application of Metal Halide Perovskite in Internet of Things.

The Internet of Things (IoT) technology connects the real and network worlds by integrating sensors and internet technology, which has greatly changed people's lifestyles, showing its broad application prospects. However, traditional materials for the sensors and power components used in the IoT limit its development for high-precision detection, long-term endurance, and multi-scenario applications. Metal halide perovskite, with unique advantages such as excellent photoelectric properties, an adjustable bandgap, flexibility, and a mild process, exhibits enormous potential to meet the requirements for IoT development. This paper provides a comprehensive review of metal halide perovskite's application in sensors and energy supply modules within IoT systems. Advances in perovskite-based sensors, such as for gas, humidity, photoelectric, and optical sensors, are discussed. The application of indoor photovoltaics based on perovskite in IoT systems is also discussed. Lastly, the application prospects and challenges of perovskite-based devices in the IoT are summarized.

A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT)

AbstractThe proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

Smart Transportation Systems: IOT-enabled Traffic Management & Vehicle-to-infrastructure Communication

Recently, there has been a substantial increase in the number of cars on the road. Escalating traffic congestion is a widespread problem that folks face on a daily basis. The manual control of traffic-by-traffic police has shown to be ineffective. “Furthermore, the signal's set duration has failed to effectively address this issue, regardless of the level of traffic congestion. This research presents a communication system called Vehicle-to-Blockchain (V2B), which utilizes blockchain technology to enable transparent and decentralized communications. The aim is to optimize the incorporation of blockchain technology into V2X (Vehicle-to-Everything) and IoT (Internet of Things) systems, therefore enhancing the efficiency and effectiveness of future transportation systems. We present a range of advanced applications for blockchain technology, such as a car ownership system that utilizes the multi-token standard, a vehicle grading system, the integration of blockchain with the Internet of Things (IoT), and a decentralized ticket administration system for transportation services. The design places a high emphasis on essential elements such as the seamless integration of data, precise precision, and the establishment of secure connections. Furthermore, it includes a decentralized payment system and marketplace for transportation inside smart cities. Our main focus is on the technical execution of smart contracts in these particular scenarios, highlighting their importance in guaranteeing strong and dependable interactions. Our decentralized approach creates a transportation ecosystem that has the ability to adjust and fulfill the evolving requirements of intelligent urban areas. It is groundbreaking, durable, and capable

Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP

The healthcare industry has blossomed into one of the most pivotal and technologically advanced sectors in the past decade. Individuals grapple with the peril of untimely demise from diverse ailments as patients suffer from delayed treatment. The paramount objective is to forge a dependable patient care system utilizing the Internet of Things (IoT), enabling physicians to monitor patients' well-being within medical facilities or even the confines of their homes. The system aids in tracking the patient's SpO2 level, body temperature, pulse rate (beats per minute), room temperature, and humidity, then trains the data with machine learning algorithms for the patient and finally monitors it through the Blynk IoT system. The cloud-stored data can be harnessed to ascertain and supervise one's health and predict forthcoming perils. This study unveils an efficacious decision-making model custom-tailored for Internet of Things (IoT) ventures, and the proposed trained algorithm satiates these requirements, offering efficiency and precision, rendering it appropriate for numerous IoT applications. Finally, the Shapley Additive Explanations (SHAP) is used here for finding out the most influential parameters, and Explainable AI (XAI) is utilized with the help of SHAP values for enhanced information on affecting parameters. The SVC model's hyperparameter is properly adjusted, yielding a testing accuracy of 98.83 % and a training accuracy of 98.71 %. On cross-validation, the lightweight Sklearn model achieved a mean accuracy of almost 99 %. And with a SHAP weightage magnitude of 1.38, 0.91, and 0.44 for Class ‘Good’, Class ‘Poor’, and Class ‘Bad’, respectively, patients SpO2 level is the most significant feature.

Experimental performance of smart irrigation systems using IoT

In agriculture field soil is a crucial determinant of plant development, primarily in irrigated environments. Nowadays there are several ways for assessing soil quality on a density or suspense basis. To develop, all plants need a specific amount of moisture level. PI involves cutting-edge technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and cloud computing. In this method presents an overview of the soft computing methods based on the PI concept and architecture, including the most common wireless technologies. Then, a real-time IoT-based intelligent irrigation system is designed as a proof of concept. Several wireless sensor nodes are deployed to monitor both soil moisture and temperature. Sensed data are transmitted to the gateway through the Messaging Queuing Telemetry Transport (MQTT) communication protocol. Root based findings depict that the proposed work increases crop growth and productivity and decreases water wastage more than other state-of-the-art strategies. ANFIS method to predict the plant growth level based on the fuzzy rule. Wireless communication technology allows for the automation of different devices and real-time analysis of data collected by these sensors. The IoT (Internet of Things) system demonstrates improved plant growth. Furthermore, the application used in the plant's worst conditions and situations has provided the best sources for appropriate plant growth..

Advancements in IoT Routing and Energy Efficiency: A Comprehensive Review of Algorithms and Technologies

The rapid expansion of the Internet of Things (IoT) has led to a significant increase in connected devices, resulting in growing challenges related to efficient data transmission, energy consumption, and network scalability. Routing protocols and energy-efficient algorithms play a pivotal role in addressing these challenges, enabling IoT systems to optimize performance while minimizing power usage. This review provides a comprehensive analysis of recent advancements in IoT routing techniques and energy-efficient solutions, focusing on multi-objective optimization algorithms such as fractional gravitational search, grey wolf optimization, and hybrid salp swarm-differential evolution algorithms. Additionally, we explore context-aware routing, cluster-based methods, and the integration of machine learning to enhance decision-making in dynamic IoT environments. The review also highlights the critical role of energy-efficient routing in IoT applications, such as smart agriculture, healthcare, and smart cities, emphasizing its importance in prolonging network lifetime and reducing overall operational costs. The potential of blockchain technology and AI-driven algorithms to enhance security and energy efficiency in IoT networks is discussed, alongside future directions in green routing solutions. By addressing the complexities of IoT routing and energy management, this review aims to provide insights into the latest research trends and guide future innovations in IoT systems.

Internet of things enabled E-learning system for academic achievement among university students

The rise of the Internet of Things (IoT) represents a burgeoning frontier in educational innovation, particularly in the realm of e-learning. Despite its expanding capabilities, the integration of IoT technologies into e-learning within higher educational institutions (HEIs) faces numerous challenges and constraints. Limited research has delved into the implementation of IoT in e-learning, especially in developing nations. Consequently, this study seeks to explore the determinants influencing the adoption of IoT for e-learning in HEIs. The aim of the innovative ICT classroom is to assist students in acquiring new skills, adapting to new situations, and utilizing technology in a manner that leads to enhanced learning outcomes and extensive knowledge. Our suggested e-learning system, integrated with IoT technology, gauges student engagement and attentiveness throughout online lectures. The proposed technology integrates electroencephalography (EEG) with IoT. The IoT is a collection of technologies that allow embedded real-world objects to connect to the Internet and is widely used by humans to support a variety of activities. IoT should be taught using developmental learning approaches and included in higher education teaching projects. EEG devices are used to track the psychological movements of students taking online courses. Our findings demonstrate how the proposed method can be used to detect the user’s level of concentration and the required level of educational strategy. A collection of informational indexes for student attention. By using a BiLSTM network to predict undergraduate recall and temporary memory, we can accurately predict undergraduate preferred learning styles. The findings of the research demonstrate that IoT systems and devices play a pivotal role in enhancing access to education. They facilitate easier teaching and learning processes while democratizing education. When employed as educational tools, IoT devices contribute significantly to creating highly engaging learning experiences for students.

An efficient intrusion detection system for IoT security using CNN decision forest.

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.

Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey

The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML’s data analytics and predictive capabilities to enhance the intelligence of IoT systems, while blockchain provides a secure, decentralized framework that ensures data integrity and trust. The synergy of these technologies not only optimizes urban management but also fortifies security and privacy in increasingly connected cities. This survey explores the transformative potential of ML-driven blockchain-IoT ecosystems in enabling autonomous, resilient, and sustainable smart city infrastructure. It also discusses the challenges such as scalability, privacy, and ethical considerations, and outlines possible applications and future research directions that are critical for advancing smart city initiatives. Understanding these dynamics is essential for realizing the full potential of smart cities, where technology enhances not only efficiency but also urban sustainability and resilience.

Advanced error correction method for quantum key distribution in IoT systems

The secure distribution of keys is essential to ensuring the privacy and integrity of data in Internet of Things (IoT) systems. Quantum Key Distribution (QKD) protocols, such as BB84, use principles from quantum mechanics to establish secure communication channels. However, errors that occur during the transmission of qubits can compromise the reliability of the shared key. This paper presents a new method for error correction that integrates Convolutional Neural Networks (CNNs) into the BB84 protocol for QKD in IoT systems. By harnessing the exceptional pattern recognition capabilities of CNNs, our approach learns hierarchical representations from preprocessed quantum channel data to effectively identify and correct errors. Extensive simulations demonstrate the high performance of our CNN-based method in reducing the Quantum Bit Error Rate (QBER). This contribution significantly enhances the security and reliability of key distribution, offering a promising solution for robust quantum communication in IoT environments.

Solar energy prediction in IoT system based optimized complex-valued spatio-temporal graph convolutional neural network

The accurate prediction of solar energy generation is significant for efficient energy management in Internet of Things (IoT) devices. However, current forecasting models frequently fail to account for the dynamic nature of weather conditions. Also, traditional methods often have limited accuracy and scalability. This paper proposes Solar Energy Prediction in IoT system based optimized Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (SEP-CVSGCNN-IoT) to overcome the limitations of existing models. Initially, the data are collected from solar panel and weather forecast. The collected data is given to the pre-processing using Data-Adaptive Gaussian Average Filtering (DAGAF) to remove the unwanted data and replace missing data. The pre-processed data is given into Nutcracker Optimization (NCO) algorithm for selecting optimal features. Then, the selected features are given to the Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (CVSGCNN) for solar energy prediction. Finally, Dipper Throated Optimization Algorithm (DTOA) is proposed to enhance the weight parameter of CVSGCNN classifier, which precisely predicts solar energy in IoT. The proposed SEP-CVSGCNN-IoT method provides 18.46%, 26.34, 15.69 and 20.84% higher accuracy and 18.24%, 23.77, 24.34 and 16.29% lower mean absolute error when analyzed with existing techniques, such as deep learning enhanced solar energy prediction and AI-driven IoT (DL-ESEF-AI), towards efficient renewable energy prediction using deep learning (TEE-REP-DL), a new deep learning method for effectual forecasting of short-term PV energy production (DL-EF-SPEP) and metaheuristic-dependent hyper parameter tuning for recurrent deep learning: application to the solar energy generation prediction (HT-RDL-PSEG) respectively.

Fog-assisted de-duplicated data exchange in distributed edge computing networks

The Internet of Things (IoT) generates substantial data through sensors for diverse applications, such as healthcare services. This article addresses the challenge of efficiently utilizing resources in resource-scarce IoT-enabled sensors to enhance data collection, transmission, and storage. Redundant data transmission from sensors covering overlapping areas incurs additional communication and storage costs. Existing schemes, namely Asymmetric Extremum (AE) and Rapid Asymmetric Maximum (RAM), employ fixed and variable-sized windows during chunking. However, these schemes face issues while selecting the index value to decide the variable window size, which may remain zero or very low, resulting in poor deduplication. This article resolves this issue in the proposed Controlled Cut-point Identification Algorithm (CCIA), designed to restrict the variable-sized window to a certain threshold. The index value for deciding the threshold will always be larger than the half size of the fixed window. It helps to find more duplicates, but the upper limit offset is also applied to avoid the unnecessarily large-sized window, which may cause extensive computation costs. The extensive simulations are performed by deploying Windows Communication Foundation services in the Azure cloud. The results demonstrate the superiority of CCIA in various metrics, including chunk number, average chunk size, minimum and maximum chunk number, variable chunking size, and probability of failure for cut point identification. In comparison to its competitors, RAM and AE, CCIA exhibits better performance across key parameters. Specifically, CCIA outperforms in total number of chunks (6.81%, 14.17%), average number of chunks (4.39%, 18.45%), and minimum chunk size (153%, 190%). These results highlight the effectiveness of CCIA in optimizing data transmission and storage within IoT systems, showcasing its potential for improved resource utilization and reduced operational costs.

IoTSLE: Securing IoT systems in low-light environments through finite automata, deep learning and DNA computing based image steganographic model

The Internet of Things (IoT) is a vast network of interconnected devices and systems, including wearables, smart home appliances, industrial machinery, and vehicles, equipped with sensors and connectivity. The data collected by the IoT devices are transmitted over a network, for processing and analyzing that data, so that appropriate actions can be initiated. Security of IoT systems is a major concern, as IoT devices collect and transmit crucial information. But images captured in low-light environments pose a challenge for IoT security by limiting the ability to accurately identify objects and people, increasing the risk of spoofing, and hindering forensic analysis. This paper unfolds a novel framework for IoT security using Steganography in Low-light Environment (IoTSLE) by image enhancement and data concealment. In proposed IoTSLE, initially, the low-light images, captured by the IoT devices in a low-light environment, are enhanced by band learning with recursion and band recomposition. After that, the secret information is concealed within the enhanced image. This concealment is supervised by using a specially designed finite automata for genome sequence encoding and 2-2-2 embedding. The proposed steganography technique is capable of hiding secret information within a 512 × 512 RGB image with the payload of 2097152 bits. The experiments like, PSNR, SSIM, Q-Index, BER, NCC, and NAE etc. are conducted to analyze the imperceptibility and security of IoTSLE. The proposed IoTSLE is useful for various IoT systems in different private and government fields like, defense agencies, digital forensics, agriculture, healthcare industry, cybersecurity firms, smart home, smart city etc.

Privacy‐preserving collaboration in blockchain‐enabled IoT: The synergy of modified homomorphic encryption and federated learning

SummaryThe proliferation of network devices capable of gathering, transmitting, and receiving data over the Internet has spurred the widespread adoption of Internet of Things (IoT) devices, particularly in resource‐oriented applications. Integrating blockchain, IoT, homomorphic encryption, and federated learning requires a balance between computational requirements and real‐time performance. Secure key management is crucial to maintain data privacy and integrity. Compliance with privacy regulations requires careful implementation of privacy‐preserving mechanisms in blockchain‐enabled IoT environments, which can be subjected to various attacks. Addressing these challenges requires interdisciplinary expertise, research, and innovation to develop more efficient and effective privacy‐preserving techniques tailored to the unique characteristics of such environments. This research introduces the Modified Homomorphic Encryption Federated‐based Adaptive Hybrid Dandelion Search (MHEF‐AHDS) algorithm as an effective framework to enhance security in blockchain‐enabled IoT systems. The amalgamation of Modified Homomorphic Encryption (MHE) and Federated Learning (FL) constitutes a potent alliance that addresses privacy concerns within collaborative and decentralized machine learning environments. This facilitates secure and adaptable data collaboration, effectively mitigating privacy risks associated with sensitive information. The integration of quantum machine learning into security applications presents an exciting opportunity for distinctive progress and innovation. Within this work, the Adaptive Hybrid Dandelion optimization algorithm, featuring an Initial search strategy, is employed for hyperparameter optimization thereby elevating the performances of the proposed MHEF‐AHDS method. Furthermore, the integration of smart contracts and Blockchain‐based IoT enhances the overall security of the proposed method. MHEF‐AHDS comprehensively tackles privacy, security, and scalability challenges through robust security measures and privacy enhancements. The performance evaluation of the MHEF‐AHDS method encompasses a thorough analysis based on key metrics such as throughput, latency, scalability, energy consumption, accuracy, precision, recall, and f1‐score. Comparative assessments against existing methods are conducted to gauge the effectiveness of the proposed method in addressing security, privacy, and scalability concerns.

Self‐Sustained Artificial Internet of Things Based on Vibration Energy Harvesting Technology: Toward the Future Eco‐Society

Clean energy has emerged as the focal point of global energy and power development. With the advancement of 5G technology and the Internet of Things (IoT), the demand for sustainable energy supply has become more pressing, leading to widespread attention to vibration energy harvesting technology. This technology enables the conversion of vibrational energy from natural phenomena such as ocean waves and wind, as well as machinery operation and human activities, into electrical energy, thus supporting the expansion of self‐sustained IoT systems. This review provides an overview of the progress in vibration energy harvesting technology and discusses the integration of this technology with self‐powered sensors and artificial intelligence. These integrations are reflected in the enhanced accuracy of environmental monitoring, increased efficiency in intelligent transportation and industrial production, and improved quality of life through intelligent healthcare and smart home. Such applications demonstrate the significant potential of self‐sustained artificial IoT in promoting environmental sustainability and elevating the level of intelligent living. In summary, exploring and applying vibration energy harvesting technology to support the autonomous operation of IoT devices is key to building a more sustainable, intelligent, and interconnected world.

Optimalisasi Waktu Produksi Jamur Tiram Melalui Implementasi Sistem Penyiraman Berbasis IoT di UD Zaida Jamur Tiram

The optimization of oyster mushroom production time through the implementation of an Internet of Things (IoT)-based irrigation system at UD Zaida Jamur Tiram aims to enhance efficiency and production quality. This program integrates IoT technology to automate the irrigation process, allowing real-time control and monitoring of environmental conditions. Methods include the design and installation of an automatic irrigation system, technical training for operators, and performance evaluation through production data analysis. Results show a significant increase in time efficiency and consistency in oyster mushroom quality. The implementation of this IoT system is expected to support the sustainability and competitiveness of the oyster mushroom industry at UD Zaida.

How IGA Istanbul Airport leverages IoT and automation for improved efficiency, safety and passenger experience

iGA Istanbul Airport is the first and only airport established with a wireless Internet of Things (IoT). IoT systems offer an effective solution for receiving data from many points with advantages such as fast implementation, low installation costs and long lifetime. In addition to standard products, special wireless IoT sensors designed for specific needs are also used in iGA Istanbul Airport. This infrastructure provides data for the execution, improvement and development of airport processes. The big data and analysis application created with the data collected from IoT devices and system integrations manages efficiency and predictive maintenance activities. With the effective use of IoT systems, iGA Istanbul Airport has achieved significant gains in increasing customer satisfaction, managing predictive maintenance from a more proactive perspective, reducing environmental impacts and increasing operational efficiency. This paper discusses the use of the IoT infrastructure at iGA Istanbul Airport and the benefits it provides.

Blockchain Technology Communication Technology Model for the IoT

In the rapidly evolving landscape of the Internet of Things (IoT), effective communication and security are paramount. Blockchain technology offers a transformative solution by providing a decentralized, transparent, and immutable ledger for managing and securing IoT interactions. By leveraging blockchain, IoT systems can enhance data integrity, improve trustworthiness, and streamline communication processes. This paper investigates the integration of blockchain technology within the SYMHIOT framework, focusing on the performance evaluation of various scenarios using a Hidden Markov Model (HMM) to manage IoT networks. The study analyzes key metrics such as transaction success rate, blockchain latency, energy consumption, packet delivery ratio (PDR), and throughput across multiple scenarios and time intervals. The results demonstrate that State 1 consistently yields optimal performance, with an average transaction success rate of 94.0%, blockchain latency as low as 115 ms, and energy consumption of 0.42 J. In contrast, State 3 exhibited the most challenging conditions, with a transaction success rate dropping to 85.3%, latency increasing to 140 ms, and energy consumption rising to 0.52 J. The highest packet delivery ratio of 99.0% and throughput of 260 kbps were also observed in State 1. Scenario 4, representing an optimized system configuration, achieved the best overall performance with minimal network delay (9.7 ms) and the lowest blockchain overhead (12.9%). These findings underscore the potential of leveraging blockchain in IoT environments, offering enhanced security, reduced latency, and improved resource efficiency, making it a robust solution for dynamic and resourceconstrained IoT networks.