Fog Model Research Articles

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Impact of Assimilating Uncrewed Aircraft System Observations on River Valley Fog Prediction

Abstract The impact of assimilating targeted uncrewed aircraft system (UAS) observations on the prediction of radiation and river valley fog is assessed using observing system experiments (OSEs). Two multirotor UASs were deployed during Frequent in situ Observations above Ground for Modeling and Advanced Prediction of fog (FOGMAP) which took place during the summer of 2022 in northern Kentucky. Targeted UAS missions were flown to sample the spatiotemporal variability of temperature and moisture in the vicinity of the Cincinnati/Northern Kentucky International Airport. During each mission, the UAS performed near-continuous profiling at two locations between the surface to 120 m AGL throughout the night. Data denial experiments were performed using the ensemble adjustment Kalman filter available in NSF NCAR’s Data Assimilation Research Testbed (DART) to determine the impact of assimilating UAS observations on the skill of analyses and forecasts issued during potential fog events. Simulations that only assimilated conventional observations tended to have a dry bias in the analyses and forecasts. The dry bias in the analyses was reduced in experiments that assimilated UAS observations leading to improved probabilistic predictions of fog. Sensitivity tests revealed that the ensemble mean analyses were improved when assimilating UAS observations of specific humidity rather than relative humidity (RH) due to the existence of a cold bias near the surface and the negative covariance between RH and temperature. It was also found that either the assumed observation error variance of (1 g kg−1)2 or the ensemble spread of the background specific humidity was too large since their sum tended to overestimate the root-mean-square error (RMSE) of the predicted ensemble mean values.

Energy- and Resource-Efficient VM Consolidation Based on Sailfish Optimization Model for Fog and Cloud Infrastructure

Energy- and Resource-Efficient VM Consolidation Based on Sailfish Optimization Model for Fog and Cloud Infrastructure

Is a more physical representation of aerosol chemistry needed for fog forecasting?

AbstractWith the changing climate, the study of fog formation is essential due to the impact of the complexity of natural and anthropogenic aerosols. The evolution of the droplet size distribution in the presence of different aerosol species remains poorly understood. To make progress towards reducing the uncertainty of fog forecasts, the Eulerian–Lagrangian particle‐based small‐scale model for the diffusional growth of droplets is used to better understand the droplet activation and growth. The small‐scale model simulations are performed using observed data from the Winter Fog Experiment study over Indira Gandhi International Airport, New Delhi. The microphysical properties, such as droplet number concentrations (Nd) and liquid water content (LWC), important for fog simulation, are evaluated to gain more insights. The small‐scale simulations have shown the droplet microphysical properties at different evolutionary stages. The Nd and effective radius change with variations in LWC for different aerosol chemistries (i.e., organics, mix, and inorganic). The calculated visibility at small scale is also shown with the variation of Nd and LWC. This study compared visibility from an existing parametrization with parcel–direct numerical simulation calculation. The hygroscopicity , which is highly related to the activation of aerosols to condensation nuclei, is taken into account to demonstrate the contribution of aerosol chemistry to fog droplet formation. The results highlight that hygroscopicity is essential in the numerical model for fog and visibility prediction as the microphysical properties of fog are regulated by aerosol species.

Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method.

An advanced security model for fog computing to tackle various attacks on the data link layer using the QQL-FHES scheme

An advanced security model for fog computing to tackle various attacks on the data link layer using the QQL-FHES scheme

An empirically derived adjustable model for particle size distributions in advection fog

AbstractRealistically modelled atmospheric phenomena are a long‐standing research topic in rendering. While significant progress has been made in modelling clear skies and clouds, fog has often been simplified as a medium that is homogeneous throughout, or as a simple density gradient. However, these approximations neglect the characteristic variations real advection fog shows throughout its vertical span, and do not provide the particle distribution data needed for accurate rendering. Based on data from meteorological literature, we developed an analytical model that yields the distribution of particle size as a function of altitude within an advection fog layer. The thickness of the fog layer is an additional input parameter, so that fog layers of varying thickness can be realistically represented. We also demonstrate that based on Mie scattering, one can easily integrate this model into a Monte Carlo renderer. Our model is the first ever non‐trivial volumetric model for advection fog that is based on real measurement data, and that contains all the components needed for inclusion in a modern renderer. The model is provided as open source component, and can serve as reference for rendering problems that involve fog layers.

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

A Multifactor Combined Data Sharing Scheme for Vehicular Fog Computing Using Blockchain

Vehicular Fog Computing (VFC), as an extended model of fog computing, combines fog computing with traditional in-vehicle networks to provide real-time response services for users. However, in such a dynamic system architecture, achieving secure and efficient data sharing is an enormous challenge. Ciphertext-policy attribute-based encryption (CP-ABE) is widely regarded as an excellent way of achieving one-to-many data sharing. Nevertheless, several practical challenges hinder its widespread application in VFC, such as inefficient attribute revocation, single-factor access control, and centralized data storage. For this purpose, we design a multi-factor combined data sharing scheme for VFC with CP-ABE and blockchain (MC-DS-VFC, in short). We first propose an efficient attribute revocation mechanism that does not require complex key update operations. We then embed time, user attributes, and access interests into data sharing for more fine-grain access control, which enables users with sufficient attributes to efficiently access real-time shared data according to their access interests. Finally, we combine the interplanetary file system (IPFS) and the blockchain maintained by roadside units (RSUs) to achieve distributed collaborative storage. Furthermore, our mechanism also supports user traceability, attribute joining, online/offline encryption, and verifiable outsourced decryption. Our proposal is shown to satisfy the indistinguishability under chosen plaintext attack (IND-CPA) in the standard model. Theoretical analysis and simulation experiments indicate that the MC-DS-VFC scheme is efficient and practical for VFC.

Security Issues in Service Model of Fog Computing Environment

Fog computing is an innovative way to expand the cloud platform by providing computing resources. The platform is a cloud that has the same data, management, storage and application features, but their origins are different because they are deployed to different locations. The platform system can retrieve a large amount, work in the field, be fully loaded, and mount on a variety of hardware devices. With this utility, Fog Framework is perfect for applications and critical moments. Fog computing is similar to cloud computing, but because of its variability, creates new security and privacy challenges that go beyond what is common for fog nodes. This paper aims to understand the impact of security problems and how to overcome them, and to provide future safety guidance for those responsible for building, upgrading and maintaining fog systems.

An Effective Multi-Criteria Decision-Making Approach for Allocation of Resources in the Fog Computing Environment

Recent advances in Internet technology have shifted the focus of end-users from the usage of traditional mobile applications to the Internet of Things (IoT)-based service-oriented smart applications (SAs). These SAs use edge devices to obtain different types of Fog services and provide their real-time response to the end-users. The Fog computing environment extends its services to the edge network layer and hosts SAs that require low latency. Further, a growing number of latency-aware SAs imposes the issue of effective allocation of resources in the Fog environment. In this paper, we have proposed an effective multi-criteria decision-making (MCDM) based solution for resource ranking and resource allocation in the Fog environment. The Proposed algorithms implement the modified edition of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchical Process (AHP) and consider Quality of Experience parameters (QoE), i.e., network bandwidth, average latency, and cores for ranking and mapping of resources. The obtained results reveal that the proposed approach utilizes 70% resources, and reduces the response time by an average of 7.5[Formula: see text]s as compared to the Cloud model and the Fog model, respectively. Similarly, the completion time of the proposed framework is minimum in comparison with the cloud and Fog models with a difference of 9[Formula: see text]s and 16[Formula: see text]s.

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.

The ongoing fast-paced technology trend has brought forth ceaseless transformation. In this regard, cloud computing has long proven to be the paramount deliverer of services such as computing power, software, networking, storage, and databases on a pay-per-use basis. The cloud is a big proponent of the internet of things (IoT), furnishing the computation and storage requisite to address internet-of-things applications. With the proliferating IoT devices triggering a continual data upsurge, the cloud-IoT interaction encounters latency, bandwidth, and connectivity restraints. The inclusion of the decentralized and distributed fog computing layer amidst the cloud and IoT layer extends the cloud's processing, storage, and networking services close to end users. This hierarchical edge-fog-cloud model distributes computation and intelligence, yielding optimal solutions while tackling constraints like massive data volume, latency, delay, and security vulnerability. The healthcare domain, warranting time-critical functionalities, can reap benefits from the cloud-fog-IoT interplay. This research paper propounded a fog-assisted smart healthcare system to diagnose heart or cardiovascular disease. It combined a fuzzy inference system (FIS) with the recurrent neural network model's variant of the gated recurrent unit (GRU) for pre-processing and predictive analytics tasks. The proposed system showcases substantially improved performance results, with classification accuracy at 99.125%. With major processing of healthcare data analytics happening at the fog layer, it is observed that the proposed work reveals optimized results concerning delays in terms of latency, response time, and jitter, compared to the cloud. Deep learning models are adept at handling sophisticated tasks, particularly predictive analytics. Time-critical healthcare applications reap benefits from deep learning's exclusive potential to furnish near-perfect results, coupled with the merits of the decentralized fog model, as revealed by the experimental results.

AI based temperature reduction effect model of fog cooling for human thermal comfort: Climate adaptation technology

This study developed a gradient-boosted regression tree-based artificial intelligence (AI) model––temperature reduction effect AI model (TREAM)––to determine the temperature reduction effect of fog cooling that varies with weather conditions. According to the trend of global warming, our society is suffering from serious damage from urban heat islands, especially negatively affecting human health and thermal comfort. Therefore, it is very important to develop and evaluate adaptive technology for providing pleasant thermal comfort to humans. This study select fog cooling as adaptive technology for human thermal comfort, and indoor and outdoor simulation were performed using STAR CCM+, a computational fluid dynamics(CFD) program. Moreover, transient analysis to validate the outdoor model of CFD and parametric study to identify the correlation between the environmental factors and fog cooling were performed. When initial temperature set as 45 °C at a relative humidity of 90% and wind speed of 1 m/s, a temperature reduction of 8.92% can be obtained. Regardless of the temperature and humidity conditions, the temperature reduction effect of fog cooling was ∼1% if the wind speed increased above 5 m/s. This study contributed to the quantitative analysis of the temperature reduction effect according to the change of environmental factors.

Analysis of hybrid RF/FSO wireless communications links under foggy weather conditions

Abstract Terrestrial free-space optical (FSO) links are based on the fundamental idea of a light beam transporting information, enabling extremely high data rates. Fog still presents a significant obstacle to enhancing the availability and dependability of terrestrial FSO lines because the fog particles scatter the light as it travels and only significantly attenuate the signal. Using a hybrid network with an FSO link and a backup link will solve the problem. GHz offers outstanding performance in this frequency band. In this research, the attenuation caused by fog is predicted using an empirical model based on measurements of visibility and their impacts on FSO/RF links. Multiple functioning FSO and RF links operating at 1 GHz, 10 GHz, and 25 GHz, as well as visibility, were simultaneously monitored for changes in signal intensity. The bit error rate (BER) for the optical modulation formats NRZ-OOK, L-PPM, and RZ-OOK is investigated under various fog scenarios. According to the results of the simulation, RF link performance at 25 GHz is better suited for hybrid system fog weather circumstances. On the other hand, we discuss the appropriateness of fog models within these various modulation formats.

Joint channel model for fog and atmospheric turbulence and performance analysis of unmanned aerial vehicles’ free-space optical communication

To study the joint effects of turbulence and fog on free-space optical (FSO) communication, a joint fog-turbulence-pointing error probability model for FSO is derived based on the random fog channel, Malaga turbulence model, and pointing error model. The closed-form expressions for the average signal to noise ratio (SNR), outage probability, average bit error rate (BER), ergodic channel capacity, and moment generating function in FSO communication under intensity modulation/direct detection are derived based on the joint model. Based on the unmanned aerial vehicle (UAV) working scenario, applying these expressions, the performance analysis of the inter-UAVs optical wave communication is implemented under the conditions for different beam widths, turbulence intensity and fog density and the pointing error caused by position, orientation, and jitter deviations. The calculated results are in good agreement with the Monte Carlo simulations. The analytical results for the average BER and outage probability show that the increase in beam width can significantly degrade the BER and outage probability, for the UAVs optical communication link. The analysis for the ergodic channel capacity shows that it increases with the increase of average transmission power; however, it increases slowly with the increase of beam width. And the increase in turbulence intensity, fog density, and drone jitter will degrade the communication performance of the system. Our study lays an important theoretical foundation for the development of FSO communication and UAV communication performance improvement in joint fog and turbulent environment.

An Intelligent Health Care System in Fog Platform with Optimized Performance

Cloud computing delivers services through the Internet and enables the deployment of a diversity of apps to provide services to many businesses. At present, the low scalability of these cloud frameworks is their primary obstacle. As a result, they are unable to satisfy the demands of centralized computer systems, which are based on the Internet of Things (IoT). Applications such as disease surveillance and tracking and monitoring systems, which are highly latency sensitive, demand the computation of the Big Data communicated to centralized databases and from databases to cloud data centers, resulting in system performance loss. Recent concepts, such as fog and edge computing, offer novel approaches to data processing by relocating the processing power and other resources closer to the end user, thereby reducing latency and maximizing energy efficiency. Existing fog models, on the other hand, have a number of limitations and tend to prioritize either the precision of their findings or a faster response time, but not both. For the purpose of applying a healthcare solution in the real world, we developed and implemented a one-of-a-kind architecture that integrates quartet deep learning with edge computing devices. The paradigm that has been developed delivers health management as a fog service through the Internet of Things (IoT) devices and efficiently organizes the data from patients based on the requirements of the user. FogBus, a fog-enabled cloud framework, is used to measure the effectiveness of the proposed structure in regards to resource usage, network throughput, congestion, precision, and runtime. To maximize the QoS or forecast the accuracy in different fog computing settings and for different user requirements, the suggested technique can be set up to run in a number of different modes.

An adaptive distributed access control model for IoT and fog computing environments

An adaptive distributed access control model for IoT and fog computing environments

A Method for Improving the Precision of FOG Scaling Coefficient Calibration Based on the High Precision Turntable

Aiming at the problem that the calibration accuracy of FOG depends on the precision of turntable, the relationship between the rotation excitation mode calibration and system-level calibration of FOG and the precision of turntable is analyzed. Firstly, the error model of fog is given, and the optimal rotation path is designed based on the principle of rotation excitation mode calibration. The rotation excitation mode calibration and system-level calibration simulation are carried out respectively. The comparison results show that when the precision of the turntable is 1”, the scale coefficient error of the rotation excitation mode calibration is about 60% lower than that of the system calibration. Through further simulation, it is found that the scale coefficient error of the gyroscope calibrated with 10 turns of the turntable is about 50% lower than that of the gyroscope calibrated with 5 turns, and the scale coefficient error can be effectively reduced by increasing the number of turns of the turntable. Finally, the actual experiment is carried out, and it is proved that the calibration accuracy of the rotating excitation mode is higher than that of the system level when the precision of the turntable is higher, which has a certain practical significance for improving the precision of the inertial navigation beacon.

SLA-based Multi-dimensional Trust Model for Fog Computing Environments

SLA-based Multi-dimensional Trust Model for Fog Computing Environments