Distant Cloud Research Articles

The Distance to Dobashi 6193—A Dark Cloud Shadowing the eROSITA Bubbles

We focus on a prominent gas cloud, Dobashi 6193, toward the western base of the northern eROSITA bubble (eRObub). It can potentially put a tight distance constraint on the eRObubs and clarify the emissions of the Loop I superbubble from the eRObubs. We found its distance to be 700 pc based on Gaia DR3 stellar extinction and parallax measurements, making it one of the most distant clouds shadowing the eRObubs well above the Galactic disk.

Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks

Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions.

Optimize data association of point cloud to improve the quality of mapping and positioning

Purpose Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop. Design/methodology/approach In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching. Findings Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization. Originality/value The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

The Formation of Milky Way “Bones”: Ubiquitous HI Narrow Self-absorption Associated with CO Emission

Long and skinny molecular filaments running along Galactic spiral arms are known as “bones,” since they make up the skeleton of the Milky Way. However, their origin is still an open question. Here, we compare spectral images of HI taken by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) with archival CO and Herschel dust emission to investigate the conversion from HI to H2 in two typical Galactic bones, CFG028.68-0.28 and CFG047.06+0.26. Sensitive FAST HI images and an improved methodology enabled us to extract HI narrow self-absorption (HINSA) features associated with CO line emission on and off the filaments, revealing the ubiquity of HINSA toward distant clouds for the first time. The derived cold HI abundances, [HI]/[H2], of the two bones range from ∼(0.5 to 44.7) × 10−3, which reveal different degrees of HI–H2 conversion, and are similar to those of nearby, low-mass star-forming clouds, Planck Galactic cold clumps, and a nearby active high-mass star-forming region G176.51+00.20. The HI–H2 conversion has been ongoing for 2.2–13.2 Myr in the bones, a timescale comparable to that of massive star formation therein. Therefore, we are witnessing young giant molecular clouds (GMCs) with rapid massive star formation. Our study paves the way of using HINSA to study cloud formation in Galactic bones and, more generally, in distant GMCs in the FAST era.

FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

Abstract Simultaneous Localization and Mapping (SLAM) is one of the key technologies in robot navigation and autonomous driving, playing an important role in robot navigation. Due to the sparsity of LiDAR data and the singularity of point cloud features, accuracy loss of LiDAR SLAM can occur during point cloud matching and localization. In response to these issues, this paper proposes a LiDAR Measurement SLAM algorithm that integrates multi type geometric feature extraction and optimized point cloud registration algorithms. This article first adopts advanced ground segmentation methods and feature segmentation strategies, including ground features, edge features, planar features, and spherical features, to improve matching accuracy. In addition, this article improves the previous method for extracting edge and planar features, extracting clearer and more robust line and surface features to address the degradation of geometric features. Finally, by introducing a robust decoupling global registration method for loop closure detection in the backend of the system, the sparsity problem of distant point clouds and the degradation problem caused by the reduction of inner layers in point cloud registration were effectively solved. In the evaluation of the KITTI dataset, our algorithm reduced absolute trajectory error values by 60%, 29%, and 71% compared to LeGO-LOAM in multi loop and feature constrained scenarios (such as sequences 00, 01, and 02), respectively. The evaluation of the M2DGR and Botanic Garden datasets also indicates that the positioning accuracy of our algorithm is superior to other advanced LiDAR SLAM algorithms.

A note of double-layer cloud detection method based on cloud base height and brightness

Double-layer cloud is a special cloud condition, and clouds in two layers have different effects on solar radiation reaching ground. Getting the shape of double-layer clouds is helpful to study the change of solar irradiance. The proposed double-layer clouds detection method is based on the cloud base height (CBH) and brightness of double-layer clouds. The CBH is calculated based on binocular parallax, which requires two images filmed synchronously by two cameras, and the brightness is offered by the brightness channel in HSV color space. The accuracy of this method is 98.5% for single-layer clouds and 89.4% for double-layer clouds. The reasons for misidentifications are (1) the brightness contrast decreases when the high-layer clouds are thick, (2) the distant low clouds have different brightness in top and bottom.

Evaluating the long-term stability of a predefined palatal region for tooth movement analysis

ObjectivesWe conducted a three-dimensional (3D) analysis of orthodontic tooth movement (TM) using digital dental models (DMs), focusing on the palatal region of interest (PROI), aiming to evaluate the long-term stability of the PROI, validate the 3D TM analysis with PROI registration, and compare it with conventional lateral cephalometric analyses. MethodsTwenty adult patients treated with a multibracket appliance were evaluated at their first visit (T0) and at least 5 years later (T1) using DMs and lateral cephalograms (LCs). The long-term stability of PROI was assessed by calculating the point cloud distances between DM-T0 and DM-T1. TM analysis using DM with PROI registration for the maxillary central incisors was assessed through linear and angular measurements in the sagittal view and subsequently compared with the LCs. ResultsThe average point cloud distance of the PROI between DM-T0 and DM-T1 was 0.21 mm (standard deviation, 0.13 mm). TM analysis using DMs demonstrated excellent reproducibility for both linear and angular measurements (intra-rater correlation coefficient, > 0.99). The 95 % limits of agreement between the DM and LC measurements were < 5.14° for angular change, 3.53 mm for horizontal displacement, and 0.98 mm for vertical displacement. No significant differences were observed in the angular and linear measurements when the TM was compared using the DMs and LCs. ConclusionsThe PROI remained stable for over 5 years, supporting the reproducibility and accuracy of TM assessment using PROI registration in orthodontic clinical practice. Clinical significanceDM analysis lacks the risks associated with X-ray exposure and can be easily performed in daily clinical practice, indicating its potential for future clinical applications. These findings further support the use of DM with PROI registration for TM analysis in orthodontic clinical practice, emphasizing its long-term stability and reproducibility.

Investigation of the effects of indoor air stability on the airborne transmission of droplets from violent respiratory events

Indoor air stability is a condition under which the indoor temperature gradients can affect the trajectories of constant breathing flows. However, it remains unclear how indoor air stability affects the airborne contaminants from violent respiratory events such as coughing. Here, we report a study of the dispersion of airborne droplets from coughing under different indoor air stability conditions. The distributions of droplets from coughing processes under stable and unstable conditions were compared. The results revealed that the stable conditions confined the coughed droplets to the breathing zone of the droplet host, whereas the unstable conditions intensified the dispersion of droplets and reduced their local concentration. The dimensionless vertical distance of the droplet cloud under the stable condition was 4 times longer than that of the unstable condition at t = 15 s. In addition, we observed that high ventilation rate caused an intense mixing of the indoor air, thus weakening the effect of indoor air stability on the propagation of droplet cloud. Personal exposure of the stable condition was 6.5 times larger than that of the unstable condition at a ventilation rate of 2.1 ACH, while it decreased to lower than half of that of the unstable condition at 7.1 ACH and 12.3 ACH. Personal exposure to the indoor environment should be assessed by simultaneously considering the indoor air stability conditions and ventilation rates. This study has provided insights into the transmission of cough droplets in indoor environments and has practical significance for preventing the spread of contagious diseases.

Enhancing Cloud Data Deduplication with Dynamic Chunking and Public Blockchain

The majority of cloud service providers (CSPs) store and remove customer data according to certain principles. The majority of them have designed their cloud platform to have very high levels of consistency, speed, availability, and durability. Their systems are built with these performance characteristics in mind, and the requirement to ensure precise and rapid data deletion must be carefully balanced. In the public blockchain, this paper suggests employing the rapid content-defined Chunking algorithm for data duplication. Acute data is frequently outsourced by individuals and organizations to distant cloud servers since doing so greatly reduces the headache of maintaining infrastructure and software. However, because user data is transmitted to cloud storage providers and stored on a remote cloud, ownership and control rights are nonetheless separated. Users thus have significant challenges when attempting to confirm the integrity of private information. According to the experiment results, the suggested dynamic chunking has a fast processing time that is on par with fixed-length chunking and significantly improves deduplication processing capability.

Study on Liquid Hydrogen Leakage and Diffusion Behavior in a Hydrogen Production Station

Liquid hydrogen storage is an important way of hydrogen storage and transportation, which greatly improves the storage and transportation efficiency due to the high energy density but at the same time brings new safety hazards. In this study, the liquid hydrogen leakage in the storage area of a hydrogen production station is numerically simulated. The effects of ambient wind direction, wind speed, leakage mass flow rate, and the mass fraction of gas phase at the leakage port on the diffusion behavior of the liquid hydrogen leakage were investigated. The results show that the ambient wind direction directly determines the direction of liquid hydrogen leakage diffusion. The wind speed significantly affects the diffusion distance. When the wind speed is 6 m/s, the diffusion distance of the flammable hydrogen cloud reaches 40.08 m, which is 2.63 times that under windless conditions. The liquid hydrogen leakage mass flow rate and the mass fraction of the gas phase have a greater effect on the volume of the flammable hydrogen cloud. As the leakage mass flow rate increased from 5.15 kg/s to 10 kg/s, the flammable hydrogen cloud volume increased from 5734.31 m3 to 10,305.5 m3. The installation of a barrier wall in front of the leakage port can limit the horizontal diffusion of the flammable hydrogen cloud, elevate the diffusion height, and effectively reduce the volume of the flammable hydrogen cloud. This study can provide theoretical support for the construction and operation of hydrogen production stations.

Large group decision-making with a rough integrated asymmetric cloud model under multi-granularity linguistic environment

Large group decision-making often contains strong uncertainty and randomness due to the complexity of decision-making problems. Moreover, existing methods about large group decision-making usually assume that the relationships among decision-makers are mutually independent, which will neglect the relevance of decision information to some extent. To address these limitations, this paper proposes a novel rough integrated asymmetric cloud model to tackle large group decision-making under multi-granularity linguistic environment. This model can not only flexibly portray decision-makers’ preferences, but also objectively handle the uncertainty and randomness in large group decision-making through the relevance of preferences. Firstly, a trust propagation model based on rough integrated asymmetric cloud is raised to consider the impact of uncertainty and randomness in the trust propagation on large group decision-making. Secondly, a new asymmetric cloud distance and similarity are proposed to overcome some defects of existing methods. Thirdly, a decision-maker weight calculation method based on the Shapley function is advanced to enhance the decision-making reasonableness. Next, an investment problem is solved by the proposed method. Finally, the effectiveness and superiority of the proposed method are demonstrated by the sensitivity and comparison analyses.

CO in the Draco nebula: the atomic–molecular transition

ABSTRACT This paper presents maps of the J=2-1 transition of CO towards the Draco nebula intermediate-velocity cloud (IVC). The maps cover 8500 square arcmin with a velocity resolution of 0.33 km s$^{-1}$ and angular resolution of 38 ${\rm arcsec}$, or 0.11 pc at the cloud distance of 600 pc. The mapped area includes all the emission detected by the Herschel satellite with 250 $\mu$m intensity &amp;gt;5 MJy sr−1. Previously published observations of the far-infrared (far-IR) emission and the 21 cm line of HI are used to derive the column density distribution of H$_2$ and the abundance ratio CO/H$_2$, as well as the distribution of the molecular fraction of hydrogen, which approaches 90 per cent over much of the brighter parts of the nebula. The CO emission is highly clumpy and closely resembles the structures seen in far-IR images. The kinematics of the CO show supersonic motions between clumps but near-thermal to trans-sonic motions within clumps, consistent with model predictions that the scale length for dissipation of supersonic turbulence should be $\sim 0.1$ pc, mediated by kinematic viscosity and/or ambipolar diffusion. Different parts of the nebula show evidence for a spread of molecular formation time-scales of a few 10$^5$ yr, comparable with the dynamical time-scale of the infalling gas. The IVC will likely merge with the Galactic interstellar medium in $\sim 10^7$ yr, and the densest clumps may form an unbound cluster of low-mass stars.

Morphological Evidence for the eROSITA Bubbles Being Giant and Distant Structures

There are two contradictory views of the eROSITA bubbles: either a 104 pc scale pair of giant bubbles blown by the Galactic center (GC), or a 102 pc scale local structure coincidentally located in the direction of GC. A key element of this controversy is the distance to the bubbles. Based on the 3D dust distribution in the Galactic plane, we found three isolated, distant (500–800 pc) clouds at intermediate Galactic latitudes. Their projected morphologies perfectly match the X-ray shadows on the defining features of the north eROSITA bubble, i.e., the North Polar Spur (NPS) and the Lotus Petal Cloud (LPC), indicating that both the NPS and LPC are distant, with a distance lower limit of nearly 1 kpc. In the X-ray-dark region between the NPS and LPC, we found a few polarized radio arcs and attributed them to the bubble’s shock front. These arcs match up perfectly with the outer border of the NPS and LPC and provide a way to define the bubble’s border. The border defined in this way can be well described by the line-of-sight tangent of a 3D skewed cup model rooted in the GC. We conclude that, instead of being two independent, distant features, the NPS and LPC compose a single, giant bubble, which therefore is most plausibly a 10 kpc scale bubble rooted at the GC.

Chemical Diagnostics to Unveil Environments Enriched by First Stars

Unveiling the chemical fingerprints of the first (Population III, hereafter Pop III) stars is crucial for indirectly studying their properties and probing their massive nature. In particular, very massive Pop III stars explode as energetic pair-instability supernovae (PISNe), allowing their chemical products to escape in the diffuse medium around galaxies, opening the possibility to observe their fingerprints in distant gas clouds. Recently, three z > 6.3 absorbers with abundances consistent with an enrichment from PISNe have been observed with JWST. In this Letter, we present novel chemical diagnostics to uncover environments mainly imprinted by PISNe. Furthermore, we revise the JWST low-resolution measurements by analyzing the publicly available high-resolution X-Shooter spectra for two of these systems. Our results reconcile the chemical abundances of these absorbers with those from literature, which are found to be consistent with an enrichment dominated (>50% metals) by normal Pop II SNe. We show the power of our novel diagnostics in isolating environments uniquely enriched by PISNe from those mainly polluted by other Pop III and Pop II SNe. When the subsequent enrichment from Pop II SNe is included, however, we find that the abundances of PISN-dominated environments partially overlap with those predominantly enriched by other Pop III and Pop II SNe. We dub these areas confusion regions. Yet, the odd–even abundance ratios [Mg,Si/Al] are extremely effective in pinpointing PISN-dominated environments and allowed us to uncover, for the first time, an absorber consistent with a combined enrichment by a PISN and another Pop III SN for all the six measured elements.

A novel framework and validation for the dynamic characterization of civil structures via ground-based lidar

While there has been substantial research conducted on the use of Ground-Based Lidar (GBL; i.e., Terrestrial Laser Scanners) in monitoring the static deformation of civil structures, its application in monitoring dynamic vibrations of structures, which is critical for structural health monitoring (SHM), has only been studied to a limited extent. Traditional contact-based SHM frameworks are constrained by a limited number of sensors and the need for physical access for instrumentation placement. Although recent case studies demonstrate GBL's potential in quantifying dynamic displacements, there is a need for more comprehensive research to validate the accuracy of GBL-based dynamic measurements under various GBL- and structure-based parameters in an autonomous and scalable framework. Therefore, the main objective of this study is to develop and comprehensively validate a novel end-to-end framework to monitor the dynamic vibrations of structures using GBL through extensive experimentation in a controlled laboratory environment. In this study, a novel two-step spatio-temporal algorithm was developed to extract the dynamic vibrations of structures from the dynamic point clouds. The framework leverages the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and change detection algorithms. The impact of several GBL-based parameters on the accuracy of the operational modal analysis results was investigated across six single-degree-of-freedom structures with unique natural frequencies. The GBL-based parameters included the resolution, quality, and point-to-point distance of the dynamic point clouds. Accelerometers and infrared-based sensors were used for the validation of GBL measurements and operational modal analysis results. To validate GBL's full-field mode shapes across the tested specimen configurations, analytical and finite element models were constructed to provide high-fidelity mode shapes as ground-truth data. The results show that the GBL can detect sub-millimeter structural vibrations, and that the resulting natural frequencies and operational deflected shapes closely match those of traditional sensing modalities, and the analytical and finite element models. This study concludes that GBL can be used reliably for remotely monitoring the dynamic response of structures at a high spatial resolution. However, further research is warranted to evaluate the full extents of the proposed framework.

Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs’ QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle’s mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints.

PC-IDN: Fast 3D loop closure detection using projection context descriptor and incremental dynamic nodes

3D Loop Closure Detection (LCD) is a crucial technique for robotics, which can correct accumulated errors after long time exploration. Existing 3D LCD methods have been designed for mechanical LiDAR, limiting their performance for various solid-state LiDARs. In this paper, we propose a new LiDAR LCD method, PC-IDN, based on a novel descriptor called Projection Context (PC), and an efficient data structure named Incremental Dynamic Nodes (IDN). The PC considers side-view and describes scenes by leveraging distance and intensity values of the point clouds. Subsequently, the PC descriptors are collected in corresponding nodes for candidate scans selection. To ensure real-time performance, the IDN module dynamically adjusts the visited PCs in the database. The results demonstrate that our method is able to achieve better precision–recall and efficiency performance than existing 3D LCD methods.

New method for estimating molecular cloud distances based on Gaia, 2MASS, and the TRILEGAL galaxy model

We propose a new method for estimating the distances of molecular clouds traced by CO line emission. Stars from 2MASS and Gaia EDR3 are selected as on-cloud stars when they are projected on a cloud. The background on-cloud stars have redder colors on average than the foreground stars. Instead of searching for stars projected away from the cloud, we employed the TRILEGA galaxy model to mimic the stellar population without cloud extinction along the sightline toward the cloud. Our method does not require an exact boundary of a cloud. The boundaries are highly variable and depend on the sensitivity of the molecular line data. For each cloud, we compared the distributions of on-cloud stars to the TRILEGAL stellar populations in the diagram of J−Ks color versus distance. The intrinsic J−Ks colors of main-sequence and evolved stars from TRILEGAL were considered separately, and they were used as the baseline for subtracting the observed J−Ks colors. The baseline-corrected J−Ks color was deployed with the Bayesian analysis and Markov chain Monte Carlo sampling to determine the distance at which the J−Ks color jump is largest. This method was successfully applied to measure the distances of 27 molecular clouds, which were selected from previously published cloud samples. By replacing TRILEGAL with the GALAXIA galaxy model, we were able to measure the distances for 21 of the 27 clouds. The distances of the 21 clouds based on the GALAXIA model agree well with those based on the TRILEGAL model. The distances of the 27 clouds estimated by this method are consistent with previous estimates. We will apply this new method to a larger region of the gaseous galactic plane, in particular, for the inner galactic region, where a region free of CO emission is hard to separate from the crowded field of clouds.

Cometary outbursts in the Oort cloud

Comet nuclei in the outer Solar system are constantly irradiated by cosmic rays at low temperatures. Accumulated high concentrations of radicals can undergo fast recombination with significant heating of cometary surface layers. We present the model of comet activity at large heliocentric distances caused by the recombination of radicals. We found that the considered mechanism can cause activity of comets in distant regions of the Solar system, even at the Oort cloud distances. Outbursts in distant comet reservoirs can be a new source of dust and ice particles contributing to the recently discovered anomalous diffuse light in the cosmic extragalactic background optic light and the unexpectedly high flux of dust particles detected by the New Horizons dust counter at the edge of the Kuiper belt. The orbits of small-radii comets in the Oort cloud are highly influenced by cometary outbursts. This effect may account for the observed decrease in the number of small-radius long-period comets.

GEMLIDS-MIOT: A Green Effective Machine Learning Intrusion Detection System based on Federated Learning for Medical IoT network security hardening

The increasing use of Internet of Things (IoT) gadgets in a daily rate has heightened security apprehension, particularly within the healthcare sector. In order to prevent the unauthorized disclosure of sensitive data, it is imperative for Internet of Things (IoT) systems to promptly and effectively respond to harmful activities. Nevertheless, the act of transferring data to distant cloud servers for the purpose of analysis gives rise to both temporal delays and apprehensions regarding privacy. To ensure the security of medical Internet of Things (MIoT) networks, a power-efficient Intrusion Detection System (IDS) is employed for three primary objectives that it will result in three stages of execution: (i) The objective is to categorize different types of attacks, such as Man-in-the-Middle (MitM) and Distributed Denial of Service (DDoS), by utilizing well-established machine learning (ML) techniques. This classification stage will serve to enhance the Intrusion Detection System (IDS) and the reporting system. (ii) Anomaly detection (unknown attack identification), or detection of unknown attacks, will be employed to identify previously unknown attacks. This identification stage involves retraining the ML model to enable future recognition and classification of these unknown attacks when the anomaly attack detector identifies that an unknown attack is recognized. Then, a retraining of the first stage classification model is executed due to the anomaly detection. (iii) To ensure that a remote cloud server remains current with the latest classification model changes, Federated Learning (FL) will be utilized. FL allows for collaborative model training while preserving data privacy and security. The experimental findings indicate that the Enhanced Random Forest (also called ensemble random forest) algorithm achieves a remarkable accuracy rate of 99.98% in classifying attacks. Thus, it will be our first stage classifier. Continuing, the One-Class Support Vector Machine (SVM) algorithm demonstrates a high level of accuracy, reaching 99.7% in detecting anomalies so that it will be our second stage identifier. Finally, the third-stage approach, which has as a target the overall system model updater, will be our introduced Federated Learning approach that works with the Enhanced Random Forests and identifies the ERF differences from the old model in an optimal way. The efficacy of our technique is confirmed through the implementation of experiments involving an Internet of Things (IoT) system and a Raspberry Pi MIoT gateway and with simulations that simulate the FL updating process. These experiments successfully identify known and unknown attacks with a high reliability level while limiting resource utilization and energy consumption. Future studies of this work will focus on enhancing the scalability and efficiency of our Intrusion Detection System in MIoT networks.