Internet Of Things Systems Research Articles

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning.

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations.

Analisis Manajemen Produksi Pertanian Hidroponik Berbasis IoT (Internet of Things) di Wisata Edukasi Arjuna Farm Kecamatan Tamansari Kota Tasikmalaya

The tourism sector in Indonesia is growing rapidly thanks to the abundant natural, social, cultural and regional potential. One educational tourist attraction that is currently developing is Arjuna Farm in Tasikmalaya City, which offers the concept of integrating the agricultural and fisheries sectors with the application of IoT (Internet of Things) technology. This research aims to analyze the application of IoT technology to support hydroponic production management at Arjuna Farm, especially in increasing the effectiveness, efficiency and quality of agricultural products. The research used a case study approach with a qualitative descriptive method, involving direct observation, in-depth interviews with the main managers (Mr. Andi and Mrs. Selvi), as well as analysis of secondary data from various literary sources. The research location was chosen by purposive sampling, located at Jalan Situ Cibereum, Tamanjaya, Tamansari, Tasikmalaya City, during November 2024. The research results show that POAC (Planning, Organizing, Actuating and Controlling) based management is implemented well, supported by IoT systems such as automatic watering, drip irrigation. This technology enables efficient and real-time production management, thereby maintaining the quality and sustainability of harvests, as well as increasing the attractiveness of Arjuna Farm as an educational tourist attraction based on modern technology.

Sistem Monitoring Dan Kontrol Berbasis Internet Of Things Pada Prototypesmart Portable Biomass Powerplant

The use of Internet of Things technology can be implemented in various aspects, one of which is the field of energy generation. The generation of electrical energy, especially biomass fuel, can be one way to achieve more environmentally friendly energy generation. Design prototype smart portable biomass powerplant includes monitoring and control of the generating system. The monitoring and control system is designed to be able to run automatically integrated with the Internet of Things system. To increase the ease of use of the tool, the system is connected in real-time with the internet. Data acquisition with sensors connected to the controller is carried out to observe the condition of the combustion chamber, turbine and generator components. The results of this data acquisition can then be monitored in real-time on a platform that can be done remotely. The control process on the prototype includes protection measures on the device in case of fault so as to provide further damage prevention measures and can be done wirelessly using the same system. Prototype testing results using Arduino mega2560 controller and ACS712 current sensor, voltage sensor, MQ-2 gas sensor and esp32 cam were found to be able to work well and monitor both the condition of the combustion chamber, the power generated by the generator to biomass combustion levels. Then the test also proved to be able to provide remote process control to protect the tool from overheating, overcurrent protection and excessive incomplete combustion process.

A novel approach to elicit distributed requirements for IOT system using SVM classifier

Internet of Things (IoT) is one of the growing technologies embedded in most application systems. It aims to solve real-world problems in different environmental fields such as industry, education, healthcare etc. IoT is becoming an integral part of daily devices and technologies opening a need for efficient and novel solutions to meet functional requirements that are more complex than those in traditional requirement engineering (RE). In addition, remotely smart systems present new challenges and lack in RE process that needs a solution. IoT systems open new research issues in RE such as elicitation, analysis, specification and management of IoT RE. To solve this lack of RE new smart techniques based on AI must be applied in the elicitation RE process. This paper presents a new smart dynamic approach in the RE elicitation phase to build dynamic functional requirements based on AI models. New stakeholder expectation needs from the smart IOT system are collected and stored in the requirements dataset. These new requirements are analyzed and classified into requirement features using the Support Vector Machine classifier. These classified functional requirements are compared to the IoT system, and the positive training requirements are added to the smart functional requirement presented in the IoT system. The proposed approach shows a significant accuracy of 95.64%, where 395 features were classified and detected from 413 entered features. This paper measures the gap between stakeholder expectations and device requirements in smart systems; these proposed measures can be implemented to optimize smart device specifications for manufacturers.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things

Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things

Network intrusion detection in big datasets using Spark environment and incremental learning

<p class="Abstract">Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.</p>

Arduino Data Acquisition System for Monitoring Quality of Life Parameters in a Room

Abstract Data acquisition is the process of collecting information from various sources through sensors or instruments. After obtaining the data, it is usually converted from the analogue format to a digital format. In this paper an application that gathers room-related information is presented. The purpose of such a system would be to be able to monitor certain parameters of interest in a living space. Afterwards this data can be used in various manners. One of the most important results of this data is being able to identify the habitability of a room. It can point out potential health hazards before they cause actual health problems. An additional objective is for the system to be on an accessible budget and to be easy to understand and implement. The hardware system using Arduino is presented in detail, as well as the graphical user interface created in Visual Studio. The system works as intended for the purposes stated above. It measures: temperature, humidity, light intensity, sound, and air quality. It works on its own if it is supplied with a 9-volt power source. The Graphical User Interface can be used to visualise and analyse the collected data. The system can still be improved by adding wireless communication such as Wi-Fi and allowing it to be integrated into Internet of Things systems. The power consumption could be improved if needed for higher sampling frequency demands.

Intrusion Detection in IoT using Gaussian Fuzzy Mutual Information-based Feature Selection

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time monitoring, data collection, and intelligent decision-making. However, the massive volume of data generated by these devices presents significant challenges for data processing and analysis. Intrusion Detection Systems (IDS) for IoT require efficient and accurate identification of malicious activities amidst vast amounts of data. Feature selection is a critical step in this process, aiming to identify the most relevant features that contribute to accurate intrusion detection, thus reducing computational complexity and improving model performance. Traditional Mutual Information-based Feature Selection (MIFS) methods face challenges when applied to IoT data due to their inherent noise, uncertainty, and imprecision. This study introduces a novel Fuzzy Mutual Information-based Feature Selection (Fuzzy-MIFS) method that integrates fuzzy logic with Gaussian membership functions to address these challenges. The proposed method enhances the robustness and effectiveness of the feature selection process, resulting in improved accuracy and efficiency of IDSs in IoT environments. Experimental results demonstrate that the Fuzzy-MIFS method consistently outperformed existing feature selection techniques across various neural network models, such as CNN, LSTM, and DBN, showcasing its superior performance in handling the complexities of IoT data. The results show that Fuzzy-MIFS increased the accuracy from 0.962 to 0.986 for CNN, from 0.96 to 0.968 for LSTM, and from 0.96 to 0.97 for DBN.

Securing the internet of things frontier: a deep learning ensemble for cyber-attack detection in smart environments

<span lang="EN-US">This study presents a novel and innovative approach using deep learning (DL) ensemble technique to improve the security of internet of things (IoT) by identifying intricate cyber-attacks. By utilising advanced DL models like deep neural network (DNN) and long short-term memory (LSTM), our approach significantly enhances the accuracy of categorization compared to basic models. The initial binary classifier achieved an accuracy of 85.2%, while the multi-class classifier achieved an accuracy of 79.7%. Both classifiers continually enhanced, achieving accuracies of 99.34% and 98.26%, respectively, after 100 epochs. Real-time scenario evaluations showed that the average execution time per sample record was 0.9439 ms, confirming its efficiency. The DL ensemble exhibited improved performance in comparison to traditional models, indicating its potential for wider implementation in IoT security. The study not only emphasises significant improvements in accuracy, but also emphasises the method’s ability to perform well across many evaluation measures. This study presents a thorough and pragmatic method for identifying cyber-attacks in IoT settings. The stacked ensemble technique outperforms earlier models and fulfils real-time processing requirements, offering substantial advancements in IoT security. These findings enhance both the theoretical comprehension and practical application, establishing a novel benchmark for protecting intelligent IoT systems.</span>

Exploring lightweight machine learning models for personal internet of things (IOT) device security

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

Analysis of Internet of Things to Enhance Security Using Artificial Intelligence based Algorithm

Exploring creative methods to secure IoT networks is vital due to the enormous security concerns created by the rapid proliferation of the Internet of Things (IoT). To increase the security of the IoT, this study examines the use of artificial intelligence (AI), specifically deep learning (DL) as well as machine learning (ML) techniques. Three state-of-the-art DL algorithms—Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN)—along with three ML methods—CatBoost, LightGBM, and XGBoost—are examined. These algorithms are renowned for their capability to handle big, as well as unbalanced datasets. This work test how well each algorithm can identify anomalies, categorize attacks, and forecast vulnerabilities using an IoT security dataset, such as CICIDS 2017 as well as IoT-23. The research evaluates algorithms by comparing their accuracy and training time. Classification tasks are where CatBoost and LightGBM really good, but when it comes to sequential data and complicated attack patterns, DL algorithms like CNN and LSTM are good. To provide the groundwork for creating AI-driven security solutions optimised for IoT systems, this research sheds light on the benefits and drawbacks of each method.

Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era

The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. In this work, we use the Rapid Review methodology to analyze collaboration in four prevalent computing architectures in the IoE paradigm, namely Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing. To analyze the collaboration, we use the 3C collaboration model, comprising communication, cooperation, and coordination. Our findings highlight the importance of Edge and Cloud Computing for enhancing collaborative coordination, focusing on efficiency and network optimization. Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems. Therefore, this work offers a comparative analysis of four computing architectures, clarifying their capabilities and limitations. Smart Cities are a major beneficiary of these insights. This knowledge can help researchers and practitioners choose the best architecture for IoT and IoE environments. Additionally, by applying the 3C collaboration model, the article provides a framework for improving collaboration in IoT and IoE systems.

AI at the Edge: Enhancing Cybersecurity and Real-Time Decision-Making in Healthcare IoT Systems

The integration of Artificial Intelligence (AI) with edge computing and Internet of Things (IoT) systems has revolutionized healthcare, enabling real-time decision-making and personalized care delivery. However, these advancements also introduce significant cybersecurity challenges that threaten patient privacy and system integrity. This research explores the convergence of AI, edge computing, and IoT in healthcare, focusing on enhancing real-time decision-making while mitigating cybersecurity risks. By leveraging edge computing’s decentralized architecture, this study aims to reduce latency, optimize resource utilization, and ensure data security during transmission and processing. Machine learning models at the edge facilitate real-time analytics and decision-making, while robust cybersecurity frameworks, including encryption, anomaly detection, and secure communication protocols, are proposed to safeguard sensitive healthcare data. The research also examines the ethical implications and scalability of deploying secure AI-driven IoT systems in healthcare. By addressing these challenges, the study aims to create a resilient infrastructure for delivering efficient, secure, and reliable healthcare solutions.

Enhancing IoT security through emotion recognition and blockchain-driven intrusion prevention

As the Internet of Things (IoT) expands, ensuring the security and privacy of interconnected devices poses significant challenges. Traditional intrusion detection and prevention systems (IDPS) for IoT rely primarily on network traffic, anomaly detection, and signature-based approaches. This paper addresses deficiencies in conventional infrastructure security, particularly within Closed-Circuit Television (CCTV) operations, to fortify IoT environments against emerging intrusions and ensure heightened levels of privacy and security. Traditional intrusion detection and prevention systems (IDPSs) for IoT primarily rely on network traffic analysis, anomaly detection, and signature-based approaches. However, there is a promising opportunity to enhance IDPS effectiveness by incorporating CCTV cameras and human-inspired techniques. We present a novel approach to IoT security employing CCTV cameras, Raspberry Pi, and emotion recognition intrusion detection and prevention. Initially, two CCTV cameras are installed and connected to a Raspberry Pi for video recording and preprocessing. Emotions are then detected using a convolutional neural network (CNN). Anomalies are classified according to predefined criteria based on detected emotions: individuals meeting conditions such as fear, multiple failed logins (greater than 2), and activity after 6 PM are classified as intruders, those meeting one or two criteria are labeled suspicious, while others are considered normal (non-intruders). In the event of suspicious activity, an alarm is automatically generated, while for intruders, an internet ban is also applied in addition to an alarm. Our proposed system aims to provide a proactive and context-aware defense mechanism against IoT intrusions by integrating machine learning algorithms and blockchain technology, ensuring the robustness and reliability of IoT security.

An automatic IoT application placement using an improved equilibrium optimizer algorithm in fog computing

PurposeThe rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approachIn this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.FindingsSimulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.Research limitations/implicationsOne of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implicationsFrom a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.Social implicationsOur proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.Originality/valueExisting resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

Blockchain Technology as a Security Enhancement Framework for Internet of Things: A Comprehensive Analysis

The exponential growth of Internet of Things (IoT) devices has introduced significant security challenges that traditional cybersecurity measures struggle to address effectively. This research presents a comprehensive analysis of blockchain technology's integration with IoT systems as a solution to enhance security, privacy, and trust in connected device ecosystems. Through systematic evaluation of implementation architectures, security frameworks, and real-world applications, this study demonstrates how blockchain's inherent characteristics of decentralization, immutability, and cryptographic security can effectively address critical IoT vulnerabilities. The research examines specific applications across supply chain management, healthcare systems, and smart city infrastructure, providing quantitative and qualitative evidence of security improvements. While acknowledging challenges in scalability, energy consumption, and regulatory compliance, the study presents innovative solutions and future directions, including AI integration and privacy-enhancing technologies. The findings indicate that blockchain-IoT integration significantly improves data integrity, device authentication, and access control mechanisms, reducing security incidents by up to 60% in implemented cases. This research contributes to the growing body of knowledge on secure IoT architectures and provides practical insights for organizations considering blockchain implementation in their IoT security frameworks.

Multi-temporal-scale event detection and clustering in IoT systems

Sensor-based Internet of Things (IoT) systems detect events from the data stream and take appropriate actions through event processing. The core of event processing, event rules, are typically defined manually by domain experts. However, there are limitations to domain experts manually setting rules for all the unlabeled events in the runtime of IoT systems. Therefore, there is a need for methods that support the generation of rules for unlabeled events. This study addresses this issue by adding two phases to the existing event processing. The first phase is the detection of unlabeled events from the data stream. Considering the characteristics of IoT systems, we propose Multi-Temporal-Scale Sampling (MulTemS), an extension of anomaly detection techniques that can detect events of various temporal-scales from multivariate time-series data. The second phase is the formation of clusters among similar events. We propose Feature-based Clustering Number prediction and Clustering (FeatCNC), which predicts the number of clusters through feature extraction and performs domain-neutral clustering. Through experiments, we demonstrate that MulTemS can effectively detect events of multiple temporal-scales, and FeatCNC can reliably cluster events across diverse domains. Additionally, we verify that the integration of these two phases results in the better formation of clusters that capture the characteristics of the events.

An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks.

The growing integration of Internet of Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion has exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by their hardware constraints, lack advanced security features, making them easy targets for attackers and compromising overall network integrity. To counteract these security issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution for safeguarding IoT networks. While Behavioral-based IDS have demonstrated their ability to detect threats effectively, they encounter practical challenges due to their reliance on pre-labeled data and the heavy computational power they require, limiting their practical deployment. This research introduces the IoT-FIDS (Flow-based Intrusion Detection System for IoT), a lightweight and efficient anomaly detection framework tailored for IoT environments. Instead of employing traditional machine learning techniques, the IoT-FIDS focuses on identifying unusual behaviors by examining flow-based representations that capture standard device communication patterns, services used, and packet header details. By analyzing only benign traffic, this network-based IDS offers a streamlined and practical approach to securing IoT networks. Our experimental results reveal that the IoT-FIDS can accurately detect most abnormal traffic patterns with minimal false positives, making it a feasible security solution for real-world IoT implementations.

Penerapan IoT dalam Sistem Monitoring Suhu dan Kelembapan pada Lahan Bawah Tanah (Basement) Masjid Al-Barkah

Underground areas, commonly referred to as basements, are often used for essential functions such as parking and electrical distribution spaces. However, unstable temperature and humidity levels due to poor air circulation can affect comfort and safety. Therefore, a system capable of automatically monitoring and controlling temperature and humidity is needed to optimize comfort and energy efficiency. This research employs an Internet of Things (IoT) approach using a DHT11 sensor to detect temperature and humidity in the basement. The data collected by the sensor is processed using a NodeMCU ESP32 microcontroller and then displayed in real-time on a web-based application via the cloud. The system also automatically controls the fan/blower to maintain ideal conditions in the basement. The results of this research show that the implemented IoT system demonstrates high effectiveness in monitoring temperature and humidity in real-time, providing accurate data, enabling energy savings by automatically regulating the fan/blower, and improving air quality and user comfort in the basement.