Internet Of Things Systems Research Articles

Continuous Solar Energy Conversion Windows Integrating Zinc Anode-Based Electrochromic Device and IoT System.

Integration of solar cells and electrochromic windows offers crucial contributions to green buildings. Solar-charging zinc anode-based electrochromic devices (ZECDs) present opportunities for addressing the solar intermittency issue. However, the limited energy storage capacity of ZECDs results in wasted harnessing of solar energy as well as overcharging. Herein, spectral-selective dual-band ZECDs that continuously transport solar energy to indoor appliances by remotely controlling the repeated bleached-tinted cycles during the daytime, are reported. Hexagonal phase cesium-doped tungsten bronze (h-Cs0.32WO3, CWO) nanocrystals are adopted for dual-band ZECDs due to their independent control ability of near-infrared (NIR) and visible (VIS) light transmittance (∆T=73.0%, 700nm; ∆T = 83.7%, 1200nm) and excellent cycling stability (0.8% optical contrast decay at 1200nm after 10000 cycles). The prototype device (i.e., CWO//Zn//CWO) delivers extraordinary thermal insulation capability, displaying a 10°C difference between "bright" and "dark" modes. Furthermore, an Internet of Things (IoT) controller to control the NIR and VIS lights of the CWO//Zn//CWO window wirelessly with a smartphone, empowering the continuous discharging of the solar-charged window during the daytime remotely, is developed. Such windows represent an intriguing potential technology whose future impact on green buildings may be substantial.

Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.

Architecture and iot security systems based on fog computing

The subject of the study is is the security architecture of the Internet of Things (IoT) based on fog computing, which allows providing efficient and secure services for many IoT users. The goal is to investigate the security architecture for IoT systems based on fog computing. To achieve the goal, the following tasks were solved: the concept of fog computing is proposed, its architecture is considered and a comparative analysis of fog and cloud computing architectures is made; the principles of designing and implementing the architecture of a fog computing system are outlined; multi-level security measures based on fog computing are investigated; and the areas of use of fog computing-based Internet of Things networks are described. When performing the tasks, such research methods were used as: theoretical analysis of literature sources; analysis of the principles of designing and implementing the security architecture of the Internet of Things; analysis of security measures at different levels of the architecture. The following results were obtained: the architecture of fog computing is considered and compared with the cloud architecture; the principles of designing and implementing the architecture of fog computing systems are formulated; multi-level IoT security measures based on fog computing are proposed. Conclusions: research on IoT security systems based on fog computing has important theoretical implications. The fog computing architecture, in contrast to the cloud architecture, better meets the demand for high traffic and low latency of mobile applications, providing more advantages for systems that require real-time information processing. When designing and implementing the architecture of fog computing systems, the factors of memory capacity, latency, and utility should be taken into account to effectively integrate fog technologies with IoT. To ensure a high level of system security, multi-level security measures should be implemented using both software and hardware solutions.

Comparative analysis of agricultural IoT systems: Case studies IoF2020 and CyberGreen

The Internet of Things (IoT) has revolutionized the agro-industrial sensor field, introducing new monitoring systems that go beyond traditional machine-to-machine (M2M) communication. This article reviews various IoT systems dedicated to data acquisition in the agro-industrial sector, with a particular emphasis on their monitoring capabilities and limitations in challenging environments such as greenhouses. The research involves testing different commercial systems using IoT stations. Given the complexity of monitoring climate and irrigation variables within greenhouses, these systems must withstand extreme weather conditions while ensuring robustness and quality in data collection. The article highlights the requirements and characteristics essential for effective monitoring, thereby providing valuable insights into the selection and optimization of data loggers in agricultural settings.

Attention-transfer-based path loss prediction in asymmetric massive MIMO IoT systems

The asymmetric massive multiple-input–multiple-output (MIMO) array improves system capacity and provides wide-area coverage for the Internet of Things (IoT). In this paper, we propose a novel attention-based model for path loss (PL) prediction in asymmetric massive MIMO IoT systems. To represent the propagation characteristics, the propagation image that considers the detailed environment, beamwidth pattern, and propagation-statistics feature is designed. Benefiting from the shuffle attention computation, the proposed model, termed a shuffle-attention-based convolutional neural network (SAN), can effectively extract the detailed features of the propagation scenario from the image. Besides, we design the beamwidth-scenario transfer learning (BWSTL) algorithm to assist the SAN model in predicting PL in the new asymmetric massive MIMO IoT systems, where the beamwidth configuration and propagation scenario are different. It is shown that the proposed model outperforms the empirical model and other state-of-the-art artificial intelligence-based models. Aided by the BWSTL algorithm, the SAN model can be transferred to new propagation conditions with limited samples, which is beneficial to the fast deployment in the new asymmetric massive MIMO IoT systems.

A Smart Monitoring System for Comatose Patients

In coma, the state of unconsciousness, the patient needs constant monitoring of several parameters because it is a critical condition. The vital parameters of these patients must be monitored continuously, so to keep track of their condition. It might be impossible to manually handle the monitoring of numerous coma patients. In the era of technology, the importance of the Internet of Things (IoT) is expanding on the technical, social, and economic fronts towards humankind. A real-time database system has been proposed, by combining IoT and monitoring system that can monitor and store data on different parameters of a patient's various characteristics including heart rate, ECG, temperature, urine level, movement, SpO2 and eye movement or blinking. It can detect as well as store all the data of respective patient. Two methods have been implemented to display the respective parameters of coma patient, one is the developed mobile application which displays all the parameters (heart rate, ECG, temperature, urine level, movement, SpO2 and eye movement or blinking) and the other is LCD which displays only 4 of the respective vitals (heart rate, temperature, urine level and SpO2). In the event of any anomaly, both displays have the ability to generate alerts. The LCD ceases to display the parameters in favor of the alert note while the mobile application generates an alert notification. It is concluded that a human health monitoring system based on the Internet of Things can give individuals daily health management, which is essential for raising the standard and quality of healthcare services.

Enhancing Energy Efficiency in Telehealth IoT through MultiObjective Optimization on a Hybrid Fog/Cloud Computing Platform

In the rapidly evolving field of Telehealth Internet of Things (IoT), the pursuit of energy-efficient solutions that coexist with optimal system performance is a critical concern. This paper introduces a novel approach to address this challenge by integrating multi-objective optimization techniques within a hybrid fog/ cloud computing platform. Building upon established research on a fog-based telehealth model, this study extends its investigation to encompass a broader spectrum of performance metrics, including energy efficiency, response time, throughput, and resource utilization. The study employs well-established multiobjective optimization algorithms, specifically NSGA-II (Non-dominated Sorting Genetic Algorithm II) and SPEA2 (Strength Pareto Evolutionary Algorithm 2), to construct a comprehensive optimization framework. An intricate objective function is meticulously formulated to quantify the trade-offs between energy efficiency and other key performance metrics, facilitating the identification of Pareto-optimal solutions. The resulting Pareto front offers illuminating insights into the nuanced interplay between energy efficiency and performance attributes, providing decision-makers with tailored options that cater to their specific priorities. Rigorous evaluation of these solutions through simulated experiments reveals a harmonious landscape where energy-saving imperatives coalesce harmoniously with response time, throughput, and resource utilization goals. The implications of this multi-objective optimization approach are analyzed in depth, underscoring its potential to reshape optimization paradigms for Telehealth IoT deployments within a fog/cloud hybrid platform. This research represents a pioneering stride towards reconciling energy efficiency and performance in Telehealth IoT systems, offering a nuanced perspective for informed decision-making and a sustainable future for energy-saving initiatives in Telehealth IoT applications

Control Robotic Arm Workstation Using IoT System for Education

Robotics play an important role in the era of modern technology. Robots can be defined as mechanical devices programmed to perform manipulative tasks under automatic control. Robotic arm is usually used in conjunction with aids such as machines and fixtures. As advancement in robot technology have become an integral part of automation in the manufacturing process. This research gives an idea of how robotic arm can be controlled using internet systems. Therefore, it also a way to reduce the risk of humans being in dangerous areas. The emergence of technology has managed to witness the evolvement of a new era known as the Internet of Things (IoT). The purpose of the research is to control robotic arm workstation using IoT system for education. The research is use Arduino uno Wi-Fi microcontroller and blink application to control IoT system. By using Arduino microcontroller, the robotic arm can directly connect to the internet. Therefore, the implementation of control robotic arm using IoT based on internet connectivity of students.

Integrated analysis of reliability, power, and performance for IoT devices and servers

The Internet of Things (IoT) is currently widely used in various sectors and spaces. IoT devices are becoming small yet powerful servers and perform server-like functions. Reliability is a critical aspect in both IoT devices and servers, as they work together to create a robust and dependable IoT ecosystem. Power and performance are two other major considerations of an IoT system. Modeling, analysis, evaluation, and optimization of reliability, power, and performance for IoT devices and servers are major components in IoT systems development and deployment. In this paper, we conduct an integrated study of reliability, power, and performance for IoT devices and servers by mathematically rigorous modeling and analysis. The contributions of the paper can be summarized as follows. We establish a continuous-time Markov chain (CTMC) model that incorporates server failure rate, server repair rate, task arrival rate, and task processing rate. Using such an analytical model, we can calculate the server availability, the average task response time, and the average power consumption. We point out that there is an optimal server speed that minimizes the power-time product and a combined cost-performance metric of power, performance, and reliability. We show the impact of server reliability on response time, power consumption, server utilization, and the power-performance tradeoff. To the best of the author’s knowledge, this is the first paper that takes a combined approach to modeling and analysis of reliability, power, and performance for IoT devices and servers. It has been noticed that there has been little such theoretically solid investigation in the existing literature. Therefore, this paper has made tangible contributions and significant advances in the joint understanding of reliability, power, performance, and their interplay in IoT devices and servers quantitatively and mathematically.

On the Usability of a Modeling Language for IoT-Based Public Transportation Systems

Internet of Things (IoT)-based public transportation systems face distinct challenges within the broader realm of IoT. Developers of such systems encounter a notably intricate development environment compared to general IoT systems, which are inherently characterized by elevated levels of complexity and heterogeneity. As successfully applied in other domains, domain-specific modeling languages (DSMLs) can also be employed to facilitate the development of IoT-based public transportation systems and address the challenges mentioned. Hence, in this study, a novel model-driven engineering (MDE) methodology is presented, comprising the steps of using a DSML, called DSML4PT, for the development of a wide-range of IoT-based public transportation applications. Moreover, the usability evaluation of DSML4PT within this MDE methodology during the real applications of IoT-based public transportation systems is also provided, which is missing in similar studies. For this purpose, we investigated the usability of DSML4PT within a systematic evaluation approach in which the features of DSML4PT are assessed both quantitatively and qualitatively in eight different real public transportation applications with the participation of experienced developers. Comparative analysis revealed that approximately 80% of IoT-based public transportation systems could be automatically generated through modeling exclusively employing DSML4PT. In contrast to the conventional software development methodologies, the novel DSML4PT approach also decreased the time required for the development of public transportation applications by almost 50%. In addition, according to a questionnaire-based assessment, the general evaluation rating of the language was measured as 4.44 over 5-point Likert scale. Feedback from the developers corroborated the practicality of this language and its widespread adoption across diverse perspectives.

Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system.

Ensemble learning approach to enhancing binary classification in Intrusion Detection System for Internet of Things

The Internet of Things (IoT) has experienced significant growth and plays a crucial role in daily activities. However, along with its development, IoT is very vulnerable to attacks and raises concerns for users. The Intrusion Detection System (IDS) operates efficiently to detect and identify suspicious activities within the network. The primary source of attacks originates from external sources, specifi-cally from the internet attempting to transmit data to the host network. IDS can identify unknown attacks from network traffic and has become one of the most effective network security. Classification is used to distinguish between normal class and attacks in binary classification problem. As a result, there is a rise in the false positive rates and a decrease in the detection accuracy during the model's training. Based on the test results using the ensemble technique with the ensemble learning XGBoost and LightGBM algorithm, it can be concluded that both binary classification problems can be solved. The results using these ensemble learning algorithms on the ToN IoT Dataset, where binary classification has been performed by combining multiple devices into one, have demonstrated improved accuracy. Moreover, this ensemble approach ensures a more even distribution of accuracy across each device, surpassing the findings of previous research.

Аналіз технологій реконфігурації систем інтернету речей на рівні програмних модулів та завантажувачів

The subject of study and research in this article are the technology of programming and interaction of nodes, as well as individual small-sized components of embedded Internet of Things (IoT) systems. The goal is to increase the flexibility of IoT system components by simplifying the reconfiguration process of individual nodes and components. The task is: to analyze existing approaches to building configurable systems, analyze and describe existing solutions that implement joint approaches; to analyze the tools for implementing the selected approaches; to describe the method of building a living system based on the analysis approach; and to provide a technical example of the proposed programming method. According to the tasks, the following results were obtained. Possible ways of reconfiguration of IoT components are analyzed. Methods of reconfiguration at the network level and partial node reconfiguration are considered. Methods to ensure the flexibility of IoT components are analyzed and classified. A comparative analysis of the summary parameters of the loaders was carried out. The possibility of using software components as immutable blocks of code to modify the microcontroller program as a method of reconfiguration was analyzed. The idea of using variable software modules to represent the microcontroller program at a higher level of abstraction with a modified bootloader is thus proposed. Elements of a method for reprogramming variable software modules of microcontrollers as middleware are proposed. Conclusions. According to a review of the existing types of systems, they were classified according to the level of organization of their reconfigured parts. According to the obtained preliminary classifications, some architectural solutions were considered from the point of view of improving operational characteristics. The main advantages for each type of organization of the reconfigurable parts of the systems are highlighted. An analysis of practical solutions regarding the modification of the built-in components of microcontrollers for managing and updating their firmware was conducted. The practical significance of this study is the proposal of a method of programming with variable software modules for microcontrollers and the obtained results of a competitive analysis of methods of modifying components and loader characteristics.

Integration of 5G, 6G and IoT with Low Earth Orbit (LEO) networks: Opportunity, challenges and future trends

The rapid growth of the massive smart Internet of Things (IoT) with mobile connections, the enhanced Mobile Broadband (eMBB) and the high demand for building a connected and intelligent world increase the probability of mobile satellite systems to be a major network in providing internet communication services in the future. Currently, the mobile satellite systems are envisioned as a significant solution for providing mobile services in different settings and for various vital objectives. These satellite systems have special qualities in each of these situations, including extensive coverage area, robustness, and ability to broadcast/multicast. The Low Earth Orbit (LEO) systems are the best promising technology that will offer internet services among the different types of satellite systems. However, the LEO systems are still experiencing certain restrictions with respect to connectivity, stability, and mobility support; because of which communication becomes unreliable. Therefore, the aim of this paper is to broadly explain the LEO systems and services in a comprehensive manner using a variety of perspectives. The paper focus is on key aspects of mobile internet based on satellite systems. This paper illustrates the integration of LEO systems with fifth and sixth generations of mobile cellular networks as well as with the IoT networks. It discusses the problems being faced as a result of the integration between cellular with IoT and satellite systems by comprehending which future research plans are outlined.

Smart Control and Monitoring System for Closed Poultry House based on IoT

The application of technology in the livestock sector has been carried out to increase the productivity of the livestock business. Closed chicken coops are one of the applications of technology in the livestock sector by utilizing a microcontroller that can control the temperature and ammonia levels in the coop under optimal conditions for fattening chickens. When the temperature rises (hot), the system set on the microcontroller will make the fan turn on automatically. This study aims to improve the offline and semi-manual fan control system in closed chicken coops to become automatic. By utilizing Internet of Things (IoT)-based technology, monitoring the condition of the chicken coop can be more easily accessed remotely. The method uses the if-then rule to regulate fan performance based on variable temperature and ammonia levels. The fan work set-point value can be set through the Android application online. Based on the test results, the IoT system treatment can work well. A consumer satisfaction survey was conducted on breeders with satisfactory results to ensure their feasibility as a commercial product.

High performance piezoelectric nanogenerator by fiber microstructure engineering toward self-powered wireless sensing system

Piezoelectric nanogenerator (PENG) with these advantages of low cost, small volume and stable output in extreme environment is constantly required to develop self-powered sensing system in Internet of Things (IoT), which can relieve energy crisis and reduce labor maintenance costs. However, low electrical output of PENG severely restricts its application and has been a key challenge in the development of PENG. To attain high output performance, a new PENG based on core-shell heterostructure of barium titanate(BT)/polyvinylidene fluoride(PVDF) composite fibers coated with BT@Ag was designed for energy harvesting and wireless sensing application. The outputs of PENG with this special structure are enhanced near 3 times than that of PENG based on traditional fibers, benefiting from the enhanced induced-polarization and stress transfer mechanism in PENG, which is confirmed by experimental results and explained by multi-physics simulations. Moreover, the PENG can effectively harvest wind and acoustic energy, which can deliver the high outputs of 107.5 V and 16.18 µA under 12 m/s wind speed, 45.4 V and 6.5 µA under 110 dB sound pressure, respectively. To verify the practicability of the PENG, a whole self-powered wireless sensing system based on the PENG to harvest energy in environment was demonstrated, where the signal of humidity condition of soil can be sensed periodically and transmitted to mobile phone for further analysis. This work provides an effective strategy to boost performance of PENG and further paves a route about advanced self-powered wireless sensing technology in IoT.

SCALABILITY AND INTEGRATION CHALLENGES OF IOT SYSTEMS IN KAZAKHSTAN

This article presents an analysis of the current state of development and integration of Internet of Things (IoT) systems in Kazakhstan. The article goes on to describe a number of key projects and initiatives aimed at the digitalization of various sectors, including smart cities, agriculture, energy and healthcare. The principal obstacles confronting IoT systems in Kazakhstan are delineated, including infrastructural constraints, the high power consumption of devices, cybersecurity, equipment incompatibility, data processing challenges, organizational impediments, financial limitations, the dearth of proficient professionals, and regulatory hurdles. For each challenge, potential solutions are proposed, including the development of network infrastructure, the implementation of energy-efficient technologies, the assurance of cybersecurity, the standardization of devices and protocols, the training of skilled professionals and the creation of an enabling environment for investment. An integrated approach to addressing these challenges will ensure the successful development and implementation of IoT technologies in Kazakhstan, thereby contributing to economic growth, increasing the efficiency of various industries and improving the quality of life of the population.

Classification of malware for security improvement in IoT using heuristic aided adaptive multi-scale and dilated ResneXt with gated recurrent unit

The rise of malware in the Internet of Things (IoT) realm exploits sensitive IoT devices that lead to extensive malicious attacks that pose a significant danger to the integrity of the Internet ecosystem. Addressing this threat effectively requires a robust system for classifying and attributing IoT malware, serving as vital initial steps toward implementing countermeasures for attack prevention and mitigation. So, in this work, a new malware classification technique using deep learning is developed for solving different kinds of malware in IoT. The required images for the malware classification process are gathered from online databases and given to the developed Adaptive Multi-scale and Dilated ResneXt with Gated Recurrent Unit (AMDR-GRU)-based malware classification process. Here, the last layer of the ResneXt is modified with GRU, and several parameters are optimized in the suggested AMDR-GRU model with the support of the designed Intensified Random Parameter-based Chameleon Swarm Algorithm (IRPCSA) to enhance the classification performance. Finally, the developed AMDR-GRU model offered the classified outcome and the obtained classification results are compared with different existing malware classification models to prove the effectiveness of the developed model over others. The developed model offered 99.59 % of precision. The result proved that the developed model is used to classify different malware without reverse engineering, binary code analysis, and feature engineering. Moreover, by effectively classifying malware, this technique can contribute to enhancing the security of IoT devices, protecting them from potential threats and vulnerabilities. This has significant implications for ensuring the privacy, integrity, and overall safety of IoT systems.

Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs).

EdgeGuard: Machine Learning for Proactive Intrusion Detection on Edge Networks

Edge computing has emerged as a promising paradigm to address the challenges of latency-sensitive applications and the exponential growth of Internet of Things (IoT) devices. However, the distributed nature of edge networks introduces new security vulnerabilities, necessitating robust intrusion detection mechanisms. In this paper, we propose EdgeGuard, a machine learning based framework for proactive intrusion detection on edge networks. Leveraging convolutional neural networks (CNNs) arranged in a residual fashion, EdgeGuard effectively captures complex patterns in network traffic data, enhancing the system's ability to detect intrusions with high accuracy. We conduct experiments using the Edge-IIoTset Cyber Security Dataset, containing a diverse range of normal and attack traffic samples. The proposed method achieves promising results, as evidenced by the receiver operating characteristic area under the curve (ROCAUC) and confusion matrix analysis. EdgeGuard offers a robust solution to safeguard edge computing environments against cyber threats, contributing to the security and integrity of IoT systems in real-world deployment scenarios.