Fog Model Research Articles

An Intelligent Blockchain-Assisted Cooperative Framework for Industry 4.0 Service Management

The shift towards Industry 4.0 has seen significant steps forward with the advancements in processing, communication, and storage capabilities of Internet of Things (IoT) devices. Cyber-physical systems (CPS) have become more intelligent and withhold advanced processing, storage, and communication capabilities. Rejuvenated network and service management architectures must incorporate the capabilities of intelligent CPS. With that said, this article introduces a cooperative blockchain (BC)-assisted resource and capability sharing approach to fulfill CPS tasks. The solution uses Federated Learning (FL)-enabled Intelligent IoT (IIoT) devices to support Next-Generation Networks (NGNs). A clustering multi-stage blockchain and FL algorithm is used to create local and global models for CPS tasks. Local models are created for each cluster during the first stage. At the second stage, Federated Averaging is used by fog devices to create fog models. A global deep model is then created on the cloud using Federated Aggregation. Blockchain is used to record and validate the added models and ensure that records are not altered under cyber-attacks. Simulation results have shown that the proposed solution outperforms conventional FL and blockchain approaches in terms of accuracy and delay tolerance.

An Attitude Improvement Method of FOG-Based Measurement-While-Drilling Utilizing Backtracking Navigation Algorithm

Directional drilling utilizes the measurement-while-drilling (MWD) to provide attitude for drilling bit. A traditional MWD system, consisting of magnetometers and accelerometers, relies on expensive and huge nonmagnetic drill collar (NMDC) to shield from magnetic interference to obtain accurate attitude angles. A fiber optic gyroscope-based MWD (FOG-based MWD) that is based on inertial navigation is an alternative survey scheme. But, random vibration environment of underground introduces high-amplitude random noise to the FOGs. Besides, the auxiliary measuring equipment [such as global position system (GPS)] will not be available in underground environments, which will bring massive attitude error under pure inertial navigation. Therefore, combined with the engineering application conditions, this article proposes a modified attitude precision improvement approach of the FOG-based MWD. First, an autoregressive moving average (ARMA) error model for FOG is established to restrain the random vibration environment interference of underground. Second, combined with the FOG error model, a continuous backtracking navigation error model is developed to improve the azimuth accuracy. Moreover, the continuous backtracking algorithm frame is designed to estimate the attitude. Finally, a semi-physical experiment and a drilling test verify that the error model has an obvious effect on reducing the amplitude of FOG random noise. The proposed backtracking algorithm make the azimuth angle achieve a high-level estimation in short time. The result of the drilling test shows that the azimuth has an improvement of 48.79%, as compared with the traditional zero velocity update (ZUPT).

A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments.

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.

Embedded fog models for remote aquatic environmental monitoring

Fog computing is used in Internet of Things (IoT) applications to allow end node processing and storage to facilitate device autonomy and other benefits in light of unstable/unreliable cloud network connections. This paper investigates how a cloud-based IoT remote aquatic environmental monitoring platform can be adapted using a fog architectural approach to provide embedded condition assessment and forecasting functionality. An equivalent IoT hydroponic garden water quality monitoring test platform is developed which is capable of accommodating embedded fog models using a node-level microcontroller (i.e., ESP8266). The test platform is deployed in a hydroponics setting to gather environmental data and evaluate the feasibility of fog intelligence functionality on an embedded system. The first model involves a node-level Fuzzy Logic water quality condition assessment using the current sensor readings. The second model is a Support Vector Machine that makes short-term predictions of water quality parameters based on a history of locally stored sensor readings. The third model contrasts Long Short-Term Memory for water quality parameter prediction. The fog test platform and embedded models are evaluated according to performance, functionality and ease-of-implementation on a microcontroller. Finally, we propose a framework detailing the future modifications for the remote aquatic environmental monitoring platform to achieve embedded fog capabilities based on the experience with the hydroponics test platform.

Smart Detection of Cardiovascular Disease Using Gradient Descent Optimization

The Internet of Medical Things (IoMT) is the networking of health things or equipment that communicate data over the internet without the need for human involvement in the healthcare field. A large quantity of data is collected from numerous sensors in the health field, and it is all transferred and stored on the cloud. This data is growing bigger here all time, and it's becoming increasingly challenging to secure it on the cloud with real-time storage and computing. Data security problem can be addressed with the aid of machine algorithms and fog computing. For data security in IoMT gadgets correspondence in an intelligent fashion, an intelligent encryption algorithm (IEA) is proposed using blockchain technology in cloud based system framework (CBSF). It is applied on patient’s database to provide immutable security, tampering prevention and transaction transparency at the fog layer in IoMT. The suggested expert system's results indicate that it is suitable for use in for the security. In the fog model, the blockchain technology approach also helps to address latency, centralization, and scalability difficulties.

Ensemble Deep Learning Intrusion Detection Model for Fog Computing Environments

Fog computing is decentralized architecture located between the cloud and devices that produce data. It acts as an intermediate layer between IoT devices and Cloud. Fog computing can perform substantial processing for the time sensitive IoT applications to reduce the latency. At the same time the Fog layer is exposed to various kinds of attacks. Deep learning-based intrusion detection system (IDS) can be suitable for fog computing paradigms for protecting the fog nodes from attacks. In this paper we have proposed a novel ensemble deep learning intrusion detection architecture for fog computing by combining two deep learning models such as traditional CNN and IDS-AlexNet model and showed this model gives high accuracy of attack detection. The respective model implementations were applied on the UNSW-NB15 datasets. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for intrusion detection. Our proposed model shows that it outperformed various other traditional and recent models.

Fog Computing

Managing the knowledge generated by the Internet of Things (IoT) sensors and actuators is one of the most important. Challenges in introducing IoT systems. Traditional cloud-based IoT systems We challenged the large scale, non- uniformity, and high latency observed in some cloud ecosystems. One solution is to decentralize applications, management, and data analytics within the network itself. Use of distributed and integrated computing models. This approach is called the nebula Calculate. This document introduces a conceptual model of fog and mist computing. They refer to the cloud-based computing model of the IoT. This document shows further features Key characteristics and aspects of fog computing, including service models and deployments It provides a baseline of what strategy and fog computing are, and how to use them.

Importance of 3D convolution and physics on a deep learning coastal fog model

The forecasting of hazardous atmospheric phenomena is often challenging. Artificial intelligence (AI) models have been applied to atmospheric science problems. Model complexity provides a motivation to quantify the importance of model architecture components. We studied the relative importance of the components of the FogNet model that was designed for big atmospheric data: 1) 3D versus 2D convolution, 2) physics-based grouping and ordering of meteorological input features, 3) different auxiliary CNN-based feature learning modules and 4) parallel versus sequential spatial-variable-wise feature learning. We investigate the relative importance of these CNN architectural features by predicting coastal fog,a complex spatiotemporal dynamical process. We use four explainable AI techniques to better understand input feature contributions. The results of the experiments demonstrate that 3D-CNN based models better capture the complexity of the fog prediction process than the 2D-CNNs. We also show that physics-based feature grouping, and the order in which they are fed into the CNNs, significantly impacts performance.

Clarity method of fog and dust image in fully mechanized mining face

At present, the abnormal state of equipment and surrounding rocks in the fully mechanized mining face is mainly detected by visual methods. However, the vision sensor works in a low-light environment and it is affected by factors such as water fog and dust, which lead to blurred images. The defogging algorithm of image based on boundary constraint and context regularization has a good effect on image restoration in the daily environment, but the recovery quality is poor in low illumination environment. Therefore, a method based on boundary constraint and nonlinear context regularization is proposed. The model of fog and dust image is established, and the transmittance function is roughly estimated by boundary constraint method. Then, the nonlinear context regularization method based on logarithmic transformation is used to estimate and optimize the scene transmission model to improve the brightness of the image, and the low illumination fog and dust image is restored by the optimized transmittance function. The logarithmic transformation multiple is selected according to the peak value of image brightness. In order to highlight the effectiveness of our method, the widely used and improved Dark Channel Prior or other methods are used for comparison. The experiment results indicate that our method can effectively remove fog and dust and improve the brightness of the image of the fully mechanized face. It is of great significance to ensure safe production and safety of workers and equipment in coal mine.

A queuing theory model for fog computing

AbstractUnder many scenarios where resources may be scarce or a good Quality of Service is a requirement, appropriately sizing components and devices is one of the main challenges. New scenarios, such as IoT, mobile cloud computing, mobile edge computing or fog computing, have emerged recently. The ability to design, model and simulate those infrastructures is critical to dimension them correctly. Queuing theory models provide a good approach to understanding how a given architecture would behave for a given set of parameters, thus helping to detect possible bottlenecks and performance issues in advance. This work presents a fog-computing modelling framework based on queuing theory. The proposed framework was used to simulate a given scenario allowing the possibility of adjusting the system by means of user-defined parameters. The results show that the proposed model is a good tool for designing optimal fog architectures regarding QoS requirements. It can also be used to fine-tune the designs to detect possible bottlenecks or improve the performance parameters of the overall environment.

EEG-based functional connectivity and executive control in patients with Parkinson’s disease and freezing of gait

ObjectiveTo explore changes over time in the network specificities underpinning a visual attentional task in patients with Parkinson’s disease and freezing of gait (the PD + FoG group), patients with Parkinson’s disease but no FoG (PD-FoG), and healthy controls (HCs). MethodsHigh-resolution electroencephalography (EEG) data were acquired for 15 PD + FoG patients, 14 PD-FoG patients, and 18 HCs performing the Attention Network Test. After source localization, functional connectivity was assessed and compared by applying the dynamic phase-locking value method. ResultsThe PD + FoG patients showed an impairment in executive control. Furthermore, the PD + FoG patients showed abnormally high theta band connectivity (relative to HCs, and 400 to 600 ms after target presentation) in a network connecting the orbitofrontal and occipitotemporal regions. ConclusionsIn PD + FoG, the greater functional connectivity between the visual network and the regions to which executive function has been attributed might indicate greater reliance on environmental features when seeking to overcome the impairment in executive control. SignificanceFoG in PD involves cognitive, attentional and executive dysfunctions. Our observation of abnormally high connectivity in PD + FoG patients argues in favor of the interference model of FoG.

Embedded Fog Models for Remote Aquatic Environmental Monitoring

Embedded Fog Models for Remote Aquatic Environmental Monitoring

Fog Model-Based Hyperspectral Image Defogging

Fog in hyperspectral images severely limits the visibility of imaging scene and reduces the image contrast, which has a negative effect on the following image interpretation. Defogging methods aim at restoring a high-quality image from the degraded image. Currently, most dehazing methods mainly depend on the atmospheric scattering model in computer vision and multispectral image communities. However, when these approaches are directly used to remove the fog from HSIs, they cannot produce satisfactory defogging performance. To alleviate this issue, we develop a novel fog model to achieve fog removal from hyperspectral images. First, a fog density map is calculated by differentiating the averaged bands falling into visible and infrared spectral ranges. Then, haze abundance in different spectral bands is estimated based on the pixel reflectance between two selected pixels with different haze levels. Finally, the high-quality hyperspectral image is restored by solving the defogging model. Experiments performed on a new benchmark created by ourselves demonstrate that the proposed method obtains favorable dehazing performance in contrast to other approaches in computer vision and remote sensing fields.

Teaching Learning Based Optimization (TLBO) for energy efficiency in Fog Computing

Fog Computing is eminent to ensure quality of service in handling huge volume and variety of data and to display output, or for closed loop process control. It comprises of fog devices to manage huge data transmission but results in high energy consumption, end-to end-delay, latency. In this paper, an energy model for fog computing environment has been proposed and implemented based on teacher student learning model called Teaching Learning Based Optimization (TLBO) to improve the responsiveness of the fog network in terms of energy optimization. The results show the effectiveness of TLBO in choosing the shortest path with least energy consumption.

Quality-aware energy efficient scheduling model for fog computing comprised IoT network

The present phase of the IoT (Internet of Things) networks has no limits to perform loaded data transmissions and computational requirements, which is made possible by Fog Computing technology. However, excessive energy resources and considerable loss of quality in loaded data transmission remains a significant challenge to the Fog Computing comprised IoT networks. This manuscript endeavors to portray a novel scheduling algorithm that optimizes edge node and fog node scheduling processes. The proposed approach is a supervised learning approach, titled "Quality-aware Energy Efficient Scheduling Model for Fog computing comprised IoT networks. The statistical measures moving averages and Heiken-Ashi patterns (Heiken-Ashi has been used in conjunction with candlestick charts to predict the present scheduling opportunities easily) have been used as a base for the proposed scheduling model. The experimental study signifies the performance of the proposed model and is scaled against the contemporary models.

A Systematic Review of Challenges in Fog Computing

The number of Internet of Things (IoT) applications is rapidly increasing. Current cloud-centric IoT designs, on the other hand, are unable to meet the mobility and dormancy necessities of duration precarious IoT practices. In certain industries, the practices have hampered the expansion of IoT. The computation in the Fog model is investigated in the present study as an option for IoT applications. Fog computing problems or obstacles for IoT applications must be thoroughly reviewed and synthesized. This study uses a well-known and commonly used method of systematic literature review (SLR) technique to address this critical research requirement. From an initial collection of 439 publications, 17 relevant studies were selected and examined by means of the SLR method and specific searching conditions generated from the study topic. In addition, four papers were hand-selected based on the significance to the topic. The collected information from the review was divided into a set of primary difficulty groups. The verdicts of the presented study may assist practitioners and scholars in better understanding fog computing problems, as well as providing a variety of valuable acumens for upcoming research. Range or scope of the study mainly is restricted to collection of the resources that have been evaluated from the database.

A Case Study: Evaluation of PAFOG One‐D Model With Advection in Simulations of Fog/Stratus From C‐FOG Experiment

AbstractFog simulation is a challenge due to the complex microphysical, radiative, and turbulent processes. C‐FOG (Coastal Fog) was a comprehensive experiment which aimed to improve our understanding and forecasting skill of fog in coastal areas of Atlantic Canada. An intermittent fog event, containing two fog periods occurring over the Grand Banks of Newfoundland beginning at 00:00 UTC on September 13, 2018, is investigated in this study. Our approach is to apply the Weather Research and Forecasting (WRF) model, in conjunction with a 1‐D model for the atmospheric boundary layer, PAFOG (Parameterized Fog model). Model results are evaluated against observations collected onboard the research vessel Hugh R. Sharp. We introduced an advection term to PAFOG to investigate the ability of the model to simulate the two‐level fog/stratus system and we developed a set of sensitivity experiments. Results suggest that PAFOG with the advection terms calculated from ERA5 shows a good ability to simulate intermittent fog caused by fog lifting as stratus, and by comparison, outperform results with advection terms derived from WRF simulations. Regarding the latter, in an experiment with high vertical resolution, WRF gives a delayed fog event, whereas with low vertical resolution, WRF gives descending stratus. Our results provide a possible methodology to study the detailed structure of fog/stratus using PAFOG, a sounding profile as initial conditions, and advection profiles derived from ERA5, with low computing cost.

A survey on reliability and availability modeling of edge, fog, and cloud computing

A survey on reliability and availability modeling of edge, fog, and cloud computing

A new conceptual model for adiabatic fog

Abstract. Visibility reduction caused by fog can be hazardous for human activities, especially for the transport sector. Previous studies show that this problem could be mitigated by improving nowcasting of fog dissipation. To address this issue, we propose a new paradigm which could potentially improve our understanding of the life cycle of adiabatic continental fogs and of the conditions that must take place for fog dissipation. For this purpose, adiabatic fog is defined as a layer filled with suspended liquid water droplets, extending from an upper boundary all the way down to the surface, with a saturated adiabatic temperature profile. In this layer, the liquid water path (LWP) must exceed a critical value: the critical liquid water path (CLWP). When the LWP is less than the CLWP, the amount of fog liquid water is not sufficient to extend all the way down to the surface, leading to a surface horizontal visibility greater than 1 km. Conversely, when the LWP exceeds the CLWP, the amount of cloud water is enough to reach the surface, inducing a horizontal visibility of less than 1 km. The excess water with respect to the critical value is defined as the reservoir liquid water path (RLWP). The new fog paradigm is formulated as a conceptual model that relates the liquid water path of adiabatic fog with its thickness and surface liquid water content and allows the critical and reservoir liquid water paths to be computed. Both variables can be tracked in real time using vertical profiling measurements, enabling a real-time diagnostic of fog status. The conceptual model is tested using data from 7 years of measurements performed at the SIRTA observatory, combining cloud radar, microwave radiometer, ceilometer, scatterometer, and weather station measurements. In this time period we found 80 fog events with reliable measurements, with 56 of these lasting more than 3 h. The paper presents the conceptual model and its capability to derive the LWP from the fog top height and surface horizontal visibility with an uncertainty of 10.5 g m−2. The impact of fog liquid water path and fog top height variations on fog life cycle (formation to dissipation) is presented based on four case studies and statistics derived from 56 fog events. Our results, based on measurements and an empirical parametrization for the adiabaticity, validate the applicability of the model. The calculated reservoir liquid water path is consistently positive during the mature phase of fog and starts to decrease quasi-monotonously about 1 h before dissipation, reaching a near-zero value at the time of dissipation. Hence, the reservoir liquid water path and its time derivative could be used as indicators of the life cycle stage, to support nowcasting of fog dissipation.

Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things

The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.