Internet Of Things Environment Research Articles

Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network

The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a multi-objective framework to concurrently address key operational objectives such as minimizing energy consumption, maximizing network lifetime, and enhancing data accuracy and throughput. The mathematical model is formulated to dynamically adapt to changing network conditions and sensor inputs, enabling real-time tuning of fuzzy rules and membership functions through backpropagation neural training. This adaptability ensures optimal performance despite the variable nature of IoT environments. Simulation results demonstrate that the MO-ANFIS model significantly outperforms traditional optimization methods, offering a robust, scalable solution for complex, dynamic WSNs in the IoT landscape. The findings suggest promising applications in various domains, including smart cities, environmental monitoring, and healthcare, where IoT integration is pivotal. This research not only bridges the gap between theoretical fuzzy-neural frameworks and practical IoT applications but also sets a foundation for future explorations into intelligent, adaptive network management systems.

An AI-Driven Model to Enhance Sustainability for the Detection of Cyber Threats in IoT Environments

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems.

EADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of Things

The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed during the deployment of these SCs. Many scholars have adopted data aggregation to protect privacy, but these methods are difficult to achieve fine-grained data collection. To this end, this paper proposes an efficient anonymous data collection (EADC) scheme suitable for the IoT environment. The scheme combines matrix algorithms with homomorphic encryption (HE) technology to effectively cut off the connection between users and data during data upload. In addition, the EADC scheme introduces a sophisticated data grouping protocol to improve the overall efficiency of the system. Analysis shows that the scheme can achieve efficient data collection without compromising user privacy.

Enhancing Security in Low-power Wide-area (LPWA) IoT Environments: The Role of HSM, Tamper-proof Technology, and Quantum Cryptography

Low-power wide-area (LPWA) networks are integral to expanding Internet of Things (IoT) applications, offering extensive coverage with low power consumption. However, these networks face significant security challenges due to their widespread deployment and inherent constraints. In order to provide secure services in an LPWA IoT environment, important information stored in IoT devices (encryption keys, device unique numbers, etc.) must be safely protected from external hacking or theft by physical access, and it is necessary to develop tamper-proof technology to enhance physical security. Meanwhile, with so many ruggedized IoT devices processing and transmitting sensitive information, security systems are essential to protect the integrity and privacy of IoT data. This paper explores the critical role of hardware security modules (HSMs), tamper-proof technology, and quantum cryptography in enhancing the physical, network, and data security of LPWA IoT environments. We propose operational strategies for HSMs, tamper-proof technology in ruggedized LPWA IoT settings, and a quantum key distribution (QKD)-based IPsec solution for robust network and data security.

Hybrid Classifier for Detecting Zero-Day Attacks on IoT Networks

Recently, Internet of Things (IoT) networks have been exposed to many electronic attacks, giving rise to concerns about the security of these networks, where their weaknesses and gaps can be exploited to access or steal data. These networks are threatened by several cyberattacks, one of which is the zero-day distributed denial-of-service (DDoS) attack, which is considered one of the dangerous attacks targeting network security. As such, it is necessary to find smart solutions to address such attacks swiftly. To address these attacks, this research proposed a hybrid IDS to detect cyber-attacks on IoT networks via machine learning (ML) algorithms, namely, XGBoost, K-nearest neighbors, and stochastic gradient descent (SGD), while classifiers are combined via an ML ensemble. Grid search CV was used to find the best hyperparameters for each classifier at each classification stage. Random projection was used to select the relevant features for training the model. In the evaluation and performance testing phase of the model, two cybersecurity datasets (CIC-IDS2017 and CIC-DDoS2019) were used to test the efficiency of the model in detecting zero-day threats. The best results were obtained for the CIC-DDoS2019 dataset, where 20 features out of the total selection were used. The model was able to achieve an accuracy of 99.91% and an intrusion detection time of 0.22 seconds. The confusion matrix results also revealed a reduction in false alarms. The results and their comparison with those of recent relevant studies demonstrated the effectiveness of the hybrid model in securing IoT networks from zero-day attacks as well as its superiority in terms of accuracy and intrusion detection time. This study is an important step in enhancing security in the IoT environment by presenting a new hybrid model that is capable of dealing with zero-day attacks that are difficult to detect with traditional models.

Reinforcement learning-based drone-assisted collection system for infection samples in IoT environment

Since infectious disease surveillance and control rely on efficient sample collection, it is important to research the infection sample collection system. The combination of Internet of Things (IoT) and drone technology provides an emerging solution to this issue. This paper designs a drone-assisted collection system for infection samples (DASS) that provides safe, intelligent, and efficient sample collection services. In this system, flexible collector drones gather infection samples from remote users and return to designated transit points to unload. Meanwhile, deliverer drones shuttle between the testing center and transit points, transporting all packaged infection samples to the testing center. However, the moment when users post collection requests is unknown. This dynamism and uncertainty present new challenges for the routing and scheduling of heterogeneous drones. To address this issue, this paper proposes a deep reinforcement learning-based dynamic sample collection (RLDSC) scheme. Considering the differences in infection samples, minimizing age of samples (AoS) is introduced as an objective. Simulation results indicate that the RLDSC scheme outperforms existing solutions in both effectiveness and efficiency.

Exploring the ALNS method for improved cybersecurity: A deep learning approach for attack detection in IoT and IIoT environments

With the emergence of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the flow of data across the world is experiencing a rapid expansion. Unfortunately, this exponential growth is accompanied by a proportional increase in cyber threats, jeopardizing the security and integrity of computer systems. In this context, intrusion detection becomes a necessity to protect networks and systems against potential attacks, ensuring their proper functioning and reliability. In this paper, we propose a deep learning-based model for attack detection. This model utilizes a convolutional neural network to train the datasets, which are first cleaned and preprocessed. The model inputs are selected using an optimization method called adaptive large neighborhood search. The results obtained for the four datasets used - CICIDS2017, Edge-IIoTset, ToN-IoT windows7, and ToN-IoT windows10 - demonstrate the model’s effectiveness for both multi-class and binary classification cases. In the binary case, the accuracy reaches 99.85%, 100%, 99.97%, and 100%, respectively, and in the multi-class case, it stands at 99.81%, 94.98%, 99.92%, and 99.84%, respectively.

Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework

The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.

An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated.

Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection

The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce security threats in IoT devices has become a topic of great importance. One of the most serious threats comes from botnets, which commonly attack IoT devices by interrupting the networks required for the devices to run. There are a number of methods that can be used to improve security by identifying unknown patterns in IoT networks, including deep learning and machine learning approaches. In this study, an algorithm named the genetic algorithm with hybrid deep learning-based anomaly detection (GA-HDLAD) is developed, with the aim of improving security by identifying botnets within the IoT environment. The GA-HDLAD technique addresses the problem of high dimensionality by using a genetic algorithm during feature selection. Hybrid deep learning is used to detect botnets; the approach is a combination of recurrent neural networks (RNNs), feature extraction techniques (FETs), and attention concepts. Botnet attacks commonly involve complex patterns that the hybrid deep learning (HDL) method can detect. Moreover, the use of FETs in the model ensures that features can be effectively extracted from spatial data, while temporal dependencies are captured by RNNs. Simulated annealing (SA) is utilized to select the hyperparameters necessary for the HDL approach. In this study, the GA-HDLAD system is experimentally assessed using a benchmark botnet dataset, and the findings reveal that the system provides superior results in comparison to existing detection methods.

A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT

ABSTRACTBy increasing smart objects as high‐potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data‐centric infrastructure for detecting safe device‐to‐device communications by supporting security and privacy issues. As a 5G‐enabled communication technology, vehicle‐to‐vehicle (V2V) communication provides a wireless exchange information connection between smart vehicles, intelligent devices, and a cloud‐edge computing environment. This connection should be established with a safe and secured run‐time protection system to avoid many critical anomalies and misbehavior problems. Detecting run‐time malicious transformations with data‐centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G‐enabled communication technology. This paper provides a hybrid genetic algorithm‐based ensemble bagged trees (GA‐EBT) algorithm for a data‐centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. In this hybrid algorithm, GA provides a beneficial performance on the training accuracy factor based on a single‐objective fitness function and EBT supports a high‐degree prediction and training process using multiple decision tree models with bootstrapped samples of the training data. For the evaluation of the proposed algorithm, four real test cases are applied for messaging injection attacks in the V2V environments in comparison to the state‐of‐the‐art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve an optimal high rate accuracy factor of 99.999, precision and recall factors of 100%, and an F1‐Score factor of 100% to detect unexpected cyber‐attacks for the V2V communications in the IoT environment.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things.

The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT's security issues. However, existing CP-ABE schemes are inefficient and unsuitable for IoT devices with limited computing resources. To address this problem, this paper proposes an efficient pairing-free CP-ABE scheme for the IoT. The scheme is based on lightweight elliptic curve scalar multiplication and supports multi-authority and verifiable outsourced decryption. The proposed scheme satisfies indistinguishability against chosen-plaintext attacks (CPA) under the elliptic curve decisional Diffie-Hellman (ECDDH) problem. Performance analysis shows that our proposed scheme is more efficient and better suited to the IoT environment compared to existing schemes.

Intelligent User Experience Design in Digital Media Art under Internet of Things Environment

Intelligent User Experience Design in Digital Media Art under Internet of Things Environment

Agent-Based Trust and Reputation Model in Smart IoT Environments

The Internet of Things (IoT) enables smart devices to connect, share and exchange data with each other through the internet. Since an IoT environment is open and dynamic, IoT participants may need to collaborate with unknown entities with no proven track record. To ensure successful collaboration among these entities, it is important to establish a mechanism that ensures all entities operate in a trustworthy manner. We present a trust and reputation model that can be used to select the best service provider in an IoT environment. Our proposed model, IoT-CADM (Comprehensive Agent-based Decision-making Model for IoT) is an agent-based decentralised trust and reputation model that can be used to select the best service provider for a particular service based on multi-context quality of service. IoT-CADM is developed using a smart multi-agent IoT environment where information about entities is collected and evaluated using a trust and reputation algorithm. We evaluated the performance of the proposed model against some other well-known models in a simulated smart factory supply chain system. Our experimental results showed that the proposed IoT-CADM achieved the best performance.

Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment

The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.

Predicting the Impact of Distributed Denial of Service (DDoS) Attacks in Long-Term Evolution for Machine (LTE-M) Networks Using a Continuous-Time Markov Chain (CTMC) Model

The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks.

Research on Intelligent Agricultural Monitoring and Control System Based on Neural Networks in the Internet of Things Environment

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.