To see the other types of publications on this topic, follow the link: Fog layer.
Journal articles on the topic 'Fog layer'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Fog layer.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Singh, Sonia, Ankita Bansal, Rajinder Sandhu, and Jagpreet Sidhu. "Fog computing and IoT based healthcare support service for dengue fever." International Journal of Pervasive Computing and Communications 14, no. 2 (June 4, 2018): 197–207. http://dx.doi.org/10.1108/ijpcc-d-18-00012.
Full textAbstract:
Purpose This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files. Design/methodology/approach To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies. Findings Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system. Originality/value The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.
2
Han, Yu, De Liu, Huan Wang, and Fan Hua Min. "Cause Analysis of Rainy Fog and Radiation Fog in Chongqing." Advanced Materials Research 599 (November 2012): 261–67. http://dx.doi.org/10.4028/www.scientific.net/amr.599.261.
Full textAbstract:
With the NCEP reanalysis data and L-band radar data to analyze the circulation situation and the vertical structure of temperature, humidity and wind speed of radiation fog and rainy fog in Chongqing by compositely analysis method. The result shows that: When radiation fog takes place, the 500hPa areas of Central Asia and Qinghai-Tibet plateau become high pressure ridge, and the cold front have reached the South China. When rainy fog takes place, the 500hPa area of Qinghai-Tibet plateau is controlled by the low pressure trough and the center of ground cold anticyclone will be in the northern area of China. Temperature inversion in the surface layer is obviously stronger when it’s radiation fog than rainy fog; And the moisture vertical structure are reflected as being dry in the upper layer, being moist in the lower layer when radiation fog occurs, and the moisture layer being deep and thick when rainy fog takes place. However in regard to wind speed, it’s slow in the surface layer during the formation of each fog.
3
Wang, Zhao Yu, Bin Gui Wu, and He Huang. "Tower Atmosphere Characteristic of Advection Fog." Applied Mechanics and Materials 137 (October 2011): 369–73. http://dx.doi.org/10.4028/www.scientific.net/amm.137.369.
Full textAbstract:
Using the profile data observed by the Tianjin 250m Meteorological Tower, vertical distribution characteristic of meteorological elements in the advection fog on Feb. 13, 2006 has been analyzed. The results show that the meteorological elements distribution of the fog is distinct from known fog structure. The lower atmosphere state gradually became moist unstable which the temperature decreased at wet-adiabatic lapse rate and humidity increased obviously in 250m companied with the process of the low level jet stream. During the fog continuance, there is a temperature inversion layer in the surface layer and double-deck strong inversion structure at upper layer of the tower. The inversion layers disappeared in the surface layer and weaken at upper layer of the tower and humidity decreases rapidly after the fog. In addition, the wind shear is not obvious in horizontal and is remarkable in vertical during the advection fog developing. The fog dissipates from the top down.
4
Hu, Jianqiang, Keshou Wu, and Wei Liang. "An IPv6-based framework for fog-assisted healthcare monitoring." Advances in Mechanical Engineering 11, no. 1 (January 2019): 168781401881951. http://dx.doi.org/10.1177/1687814018819515.
Full textAbstract:
The new generation healthcare monitoring system combines technologies of wireless body sensor network, cloud computing, and Bigdata, and there are still limitations in protocol security, response delay, and prediction of potential severity disease. In response to the above situation, an Internet Protocol Version 6 (IPv6)-based framework for fog-assisted healthcare monitoring is proposed. This framework is composite of body-sensing layer, fog layer, and cloud layer. The body-sensing layer generates physiological data, and fog computing node in fog layer collects and analyses time-sensitive data. Fog layer sends physiological data to cloud computing node in cloud layer for further processing. Mobile intelligent device connects fog computing node and helps individuals to predict the potential disease with its level of severity. The proposed framework uses advanced techniques such as IPv6-based network architecture, cloud–fog resource scheduling algorithm based on time threshold, and classification model of chronic diseases based on cascaded deep learning and so on. In order to determine the validity of the framework, health data were systematically generated from 45 patients for 30 days. Results depict that the proposed classification model of chronic diseases has high accuracy in determining the level of severity of potential disease. Moreover, response delay is much lower than Internet Protocol Version 4 (IPv4)-based cloud-assisted environment.
5
Izett, Jonathan G., and Bas J. H. van de Wiel. "Why Does Fog Deepen? An Analytical Perspective." Atmosphere 11, no. 8 (August 14, 2020): 865. http://dx.doi.org/10.3390/atmos11080865.
Full textAbstract:
The overall depth of a fog layer is one of the important factors in determining the hazard that a fog event presents. With discrete observations and often coarse numerical grids, however, fog depth cannot always be accurately determined. To address this, we derive a simple analytical relation that describes the change in depth of a fog interface with time, which depends on the tendencies and vertical gradients of moisture. We also present a lengthscale estimate for the maximum depth over which mixing can occur in order for the fog layer to be sustained, assuming a uniform mixing of the vertical profiles of temperature and moisture. Even over several hours, and when coarse observational resolution is used, the analytical description is shown to accurately diagnose the depth of a fog layer when compared against observational data and the results of large-eddy simulations. Such an analytical description not only enables the estimation of sub-grid or inter-observation fog depth, but also provides a simple framework for interpreting the evolution of a fog layer in time.
6
Aleisa, Mohammed A., Abdullah Abuhussein, Faisal S. Alsubaei, and Frederick T. Sheldon. "Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment." Sensors 21, no. 21 (October 20, 2021): 6950. http://dx.doi.org/10.3390/s21216950.
Full textAbstract:
The fog layer provides substantial benefits in cloud-based IoT applications because it can serve as an aggregation layer and it moves the computation resources nearer to the IoT devices; however, it is important to ensure adequate performance is achieved in such applications, as the devices usually communicate frequently and authenticate with the cloud. This can cause performance and availability issues, which can be dangerous in critical applications such as in the healthcare sector. In this paper, we analyze the efficacy of the fog layer in different architectures in a real-world environment by examining performance metrics for the cloud and fog layers using different numbers of IoT devices. We also implement the fog layer using two methods to determine whether different fog implementation frameworks can affect the performance. The results show that including a fog layer with semi-heavyweight computation capability results in higher capital costs, although the in the long run resources, time, and money are saved. This study can serve as a reference for fundamental fog computing concepts. It can also be used to walk practitioners through different implementation frameworks of fog-aided IoT and to show tradeoffs in order to inform when to use each implementation framework based on one’s objectives.
7
Mangla, Cherry, Shalli Rani, and Henry Kwame Atiglah. "Secure Data Transmission Using Quantum Cryptography in Fog Computing." Wireless Communications and Mobile Computing 2022 (January 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/3426811.
Full textAbstract:
Fog computing’s idea is to bring virtual existence into objects used on a daily basis. The “objects” layer of fog architecture is also known as the smart object layer (SOL). SOL has provided the fog network with a strong platform to outperform. Although the fog architecture decentralizes data, uses more data centers, and collects and transmits it to adjacent servers for faster processing in fog networks, it faces several security challenges. The security problems of fog computing need to be alleviated for the exploitation of all benefits of fog computing in classical networks. This article has addressed the security challenges in fog computing, potential solutions via quantum cryptography, a use case portraying the importance of quantum cryptography in fog computing along future scope, and research directions.
8
Gultepe, I. "Fog and Boundary Layer Clouds: Introduction." Pure and Applied Geophysics 164, no. 6-7 (June 2007): 1115–16. http://dx.doi.org/10.1007/s00024-007-0209-4.
Full text
9
G., Shruthi, Monica R. Mundada, S. Supreeth, and Bryan Gardiner. "Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing." International Journal of Computer Network and Information Security 15, no. 4 (August 8, 2023): 84–95. http://dx.doi.org/10.5815/ijcnis.2023.04.08.
Full textAbstract:
Load balancing plays a major part in improving the performance of fog computing, which has become a requirement in fog layer for distributing all workload in equal manner amongst the current Virtual machines (VMs) in a segment. The distribution of load is a complicated process as it consists of numerous users in fog computing environment. Hence, an effectual technique called Mutated Leader Algorithm (MLA) is proposed for balancing load in fogging environment. Firstly, fog computing is initialized with fog layer, cloud layer and end user layer. Then, task is submitted from end user under fog layer with cluster of nodes. Afterwards, load balancing process is done in each cluster and the resources for each VM are predicted using Deep Residual Network (DRN). The load balancing is accomplished by allocating and reallocating the task from the users to the VMs in the cloud based on the resource constraints optimally using MLA. Here, the load balancing is needed for optimizing resources and objectives. Lastly, if VMs are overloaded and then the jobs are pulled from associated VM and allocated to under loaded VM. Thus the proposed MLA achieved minimum execution time is 1.472ns, cost is $69.448 and load is 0.0003% respectively.
10
Bhatt, Chintan, and C. K. Bhensdadia. "Fog Computing." International Journal of Grid and High Performance Computing 9, no. 4 (October 2017): 105–13. http://dx.doi.org/10.4018/ijghpc.2017100107.
Full textAbstract:
The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.
11
Bergot, Thierry, Enric Terradellas, Joan Cuxart, Antoni Mira, Olivier Liechti, Mathias Mueller, and Niels Woetmann Nielsen. "Intercomparison of Single-Column Numerical Models for the Prediction of Radiation Fog." Journal of Applied Meteorology and Climatology 46, no. 4 (April 1, 2007): 504–21. http://dx.doi.org/10.1175/jam2475.1.
Full textAbstract:
Abstract The short-term forecasting of fog is a difficult issue that can have a large societal impact. Radiation fog appears in the surface boundary layer, and its evolution is driven by the interactions between the surface and lower layers of the atmosphere. Current NWP models poorly forecast the life cycle of fog, and improved NWP models are needed before improving the prediction of fog. Six numerical model simulations are compared for two cases from the Paris-Charles de Gaulle (Paris-CdG) fog field experiment. This intercomparison includes both operational and research models, which have significantly different vertical resolutions and physical parameterizations. The main goal of this intercomparison is to identify the capabilities of the various models to forecast fog accurately. An attempt is made to identify the main reasons behind the differences among the various models. This intercomparison reveals that considerable differences among models exist in the surface boundary layer before the fog onset, particularly in cases with light winds. The lower-resolution models crudely forecast the nocturnal inversion, the strong inversion at the top of the fog layer, and the interactions between soil and atmosphere. This intercomparison further illustrates the importance of accurate parameterizations of dew deposition and gravitational settling on the prediction of fog.
12
Li, Pengyuan, Gang Fu, Chungu Lu, Dan Fu, and Shuai Wang. "The Formation Mechanism of a Spring Sea Fog Event over the Yellow Sea Associated with a Low-Level Jet." Weather and Forecasting 27, no. 6 (December 1, 2012): 1538–53. http://dx.doi.org/10.1175/waf-d-11-00152.1.
Full textAbstract:
Abstract In this paper, a dense sea fog event that occurred over the Yellow Sea (YS) on 9 March 2005 is investigated using the Weather Research and Forecasting Model version 3.1.1 (WRF v3.1.1). It is shown that the WRF can reasonably reproduce the main features of this fog case with a newly implemented planetary boundary layer (PBL) scheme developed by Mellor–Yamada–Nakanishi–Niino (MYNN). The low-level jet (LLJ) associated with this fog episode played an important role in triggering the turbulence. During the fog formation, sea fog extended vertically with the aid of turbulence. The mechanical production term resulting from wind shear contributed to the generation of the turbulence. WRF simulation results showed that the fog layer was thicker in the northeastern part of the YS than that in the southwestern part due to the intensity of the inversion layer and the LLJ. The topography test in which the mountain region in Fujian Province was removed showed that the roles of topography were to prevent the moisture from extending to land, to intensify the inversion layer, and to enhance the intensity of LLJ, as well as to elevate its altitude.
13
Chegini, Hossein, Ranesh Kumar Naha, Aniket Mahanti, and Parimala Thulasiraman. "Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy." IoT 2, no. 1 (February 7, 2021): 92–118. http://dx.doi.org/10.3390/iot2010006.
Full textAbstract:
The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.
14
Huang, Huijun, Hongnian Liu, Jian Huang, Weikang Mao, and Xueyan Bi. "Atmospheric Boundary Layer Structure and Turbulence during Sea Fog on the Southern China Coast." Monthly Weather Review 143, no. 5 (May 1, 2015): 1907–23. http://dx.doi.org/10.1175/mwr-d-14-00207.1.
Full textAbstract:
Abstract Small-scale turbulence has an essential role in sea-fog formation and evolution, but is not completely understood. This study analyzes measurements of the small-scale turbulence, together with the boundary layer structure and the synoptic and mesoscale conditions over the life cycle of a cold advection fog event and a warm advection fog event, both off the coast of southern China. The measurement data come from two sites: one on the coast and one at sea. These findings include the following: 1) For cold advection fog, the top can extend above the inversion base, but formation of an overlaying cloud causes the fog to dissipate. 2) For warm advection fog, two layers of low cloud can merge to form deep fog, with the depth exceeding 1000 m, when strong advection of warm moist air produces active thermal-turbulence mixing above the thermal-turbulence interface. 3) Turbulence near the sea surface is mainly thermally driven for cold advection fog, but mechanically driven for warm advection fog. 4) The momentum fluxes of both fog cases are below 0.04 kg m−1 s−2. However, the sensible and latent heat flux differ between the cases: in the cold advection fog case, the sensible and latent heat fluxes are roughly upward, averaging 2.58 and 26.75 W m−2, respectively; however, in the warm advection fog case, the sensible and latent heat flux are mostly downward, averaging −6.98 and −6.22 W m−2, respectively. 5) Low-level vertical advection is important for both fogs, but has a larger influence on fog development in the warm advection fog case.
15
Elmansy, Hossam, Khaled Metwally, and Khaled Badran. "Learning agent-based security schema mitigating man-in-the-middle attacks in fog computing." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (October 1, 2023): 5908. http://dx.doi.org/10.11591/ijece.v13i5.pp5908-5921.
Full textAbstract:
<div style="text-align: start;" align="center"><span lang="EN-US">The fast emerging of internet of things (IoTs) has introduced fog computing as an intermediate layer between end-users and the cloud datacenters. Fog computing layer characterized by its closeness to end users for service provisioning than the cloud. However, security challenges are still a big concern in fog and cloud computing paradigms as well. In fog computing, one of the most destructive attacks is man-in-the-middle (MitM). Moreover, MitM attacks are hard to be detected since they performed passively on the network level. This paper proposes a MitM mitigation scheme in fog computing architecture. The proposal mapped the fog layer on software-defined network (SDN) architecture. The proposal integrated multi-path transmission control protocol (MPTCP), moving target defense (MTD) technique, and reinforcement learning agent (RL) in one framework that contributed significantly to improving the fog layer resources utilization and security. The proposed schema hardens the network reconnaissance and discovery, thus improved the network security against MitM attack. The evaluation framework was tested using a simulation environment on mininet, with the utilization of MPTCP kernel and Ryu SDN controller. The experimental results shows that the proposed schema maintained the network resiliency, improves resource utilization without adding significant overheads compared to the traditional transmission control protocol (TCP).</span></div>
16
Bergot, Thierry, and Renaud Lestringant. "On the Predictability of Radiation Fog Formation in a Mesoscale Model: A Case Study in Heterogeneous Terrain." Atmosphere 10, no. 4 (March 28, 2019): 165. http://dx.doi.org/10.3390/atmos10040165.
Full textAbstract:
This study evaluates the predictability of the formation phase of a radiation fog event observed during the night of 31 October 2015 to 01 November 2015 in the north-east of France at three sites managed by OPE (Observatoire Pérenne de l’Environnement). The fog layer shows significantly different behaviors at the three areas, which are located only a few kilometers apart. Three fog life cycles were observed: the formation of a dense adiabatic fog, the formation of a thin patchy fog, or no fog formation despite favorable conditions. This event was studied with the Meso-NH numerical mesoscale model at two horizontal resolutions, 500 m and 50 m. Simulations at 50 m allow estimation of the spread of the predicted parameters over the heterogeneous terrain studied. These numerical simulations strongly suggest that this event involved numerous interactions and complex circulations. The wind above the nocturnal boundary layer greatly affects the transition of shallow patchy fog into thick adiabatic fog. These numerical simulations also show that the occurrence and type of fog could be very different over a small but heterogeneous area. It is also interesting to note that the spread of the simulated parameters was very high during the transition from shallow fog to a deep fog layer. The spread was concentrated during the regime transition between the fog formation and its maturity. This appeared to be the result of the complex interplay of processes at numerous ranges of scale. A new concept called “pseudo-process diagram” is presented. These pseudo-process diagrams are very good tools to analyze fog, and allow a good illustration of the spread of fog during this chaotic phase. This kind of concept seems a promising tool to analyze fog predictability in depth.
17
Yazdani, Azita, Seyedeh Fatemeh Dashti, and Yeganeh Safdari. "A fog-assisted information model based on priority queue and clinical decision support systems." Health Informatics Journal 29, no. 1 (January 2023): 146045822311527. http://dx.doi.org/10.1177/14604582231152792.
Full textAbstract:
Objectives Telehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called “priority queue” in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency. Material and Methods We analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model. Result The fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud. Conclusion By combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing.
18
Alraddady, Sara, Ben Soh, Mohammed A. AlZain, and Alice S. Li. "Fog Computing: Strategies for Optimal Performance and Cost Effectiveness." Electronics 11, no. 21 (November 3, 2022): 3597. http://dx.doi.org/10.3390/electronics11213597.
Full textAbstract:
The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time.
19
Ngabo, Desire, Dong Wang, Celestine Iwendi, Joseph Henry Anajemba, Lukman Adewale Ajao, and Cresantus Biamba. "Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things." Electronics 10, no. 17 (August 30, 2021): 2110. http://dx.doi.org/10.3390/electronics10172110.
Full textAbstract:
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.
20
Prieto González, Lisardo, Corvin Jaedicke, Johannes Schubert, and Vladimir Stantchev. "Fog computing architectures for healthcare." Journal of Information, Communication and Ethics in Society 14, no. 4 (November 14, 2016): 334–49. http://dx.doi.org/10.1108/jices-05-2016-0014.
Full textAbstract:
Purpose The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.
21
Tahmasebi-Pouya, Niloofar, Mehdi-Agha Sarram, and Seyedakbar Mostafavi. "A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes." Wireless Communications and Mobile Computing 2022 (August 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/1533949.
Full textAbstract:
In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user’s requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods.
22
Gudla, Surya Pavan Kumar, Sourav Kumar Bhoi, Soumya Ranjan Nayak, and Amit Verma. "DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications." Mathematical Problems in Engineering 2022 (August 8, 2022): 1–17. http://dx.doi.org/10.1155/2022/3747302.
Full textAbstract:
Nowadays, fog computing plays a very vital role in providing many services to end-based IoT (Internet of Things) systems. The end IoT devices communicate with the middle layer fog nodes and to the above cloud layer to process the user tasks. However, this large data communication experiences many security challenges as IoT devices are being compromised and thus the fog nodes at the fog layer are more prone to a very critical attack known as Distributed Denial of Service (DDoS) attack. The attackers or the compromised IoT devices need to be detected well in the network. Deep Learning (DL) plays a prominent role in predicting the end-user behavior by extracting features and classifying the adversary in the network. But, due to IoT device’s constrained nature in computation and storage facilities, DL cannot be administered on those. In this paper, a deep intelligent DDoS attack detection scheme (DI-ADS) is proposed for fog-based IoT applications. The framework mainly uses a deep learning model (DLM) to detect DDoS attacks in the network. The DLM is installed on the computation module of the fog node that predicts the end IoT device behavior. For the selection of the best DLM model at the fog layer, the performance comparison is made on Deep Neural Multilayer Perceptron (DNMLP) and Long Short-Term Memory (LSTM) models along with the conventional machine learning (ML) models such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Random Forest (RF). The simulation is performed using the Python Anaconda platform by considering a new DDoS-SDN (Mendeley Dataset) dataset that consists of three DDoS attacks such as TCP Syn, UDP Flood, and ICMP attacks. From the results, DNMLP showed the best accuracy of 99.44% as compared to other DL and ML models. By outperforming nature in the detection of DDoS attacks, DNMLP is considered in the proposed framework for being implemented at the fog layer.
23
Ijaz, Muhammad, Gang Li, Huiquan Wang, Ahmed M. El-Sherbeeny, Yussif Moro Awelisah, Ling Lin, Anis Koubaa, and Alam Noor. "Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection." Electronics 9, no. 12 (November 28, 2020): 2015. http://dx.doi.org/10.3390/electronics9122015.
Full textAbstract:
Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).
24
Wu, Bin Gui, Zhao Yu Wang, and Yi Yang Xie. "Analysis of Air Flow and Turbulent Fluxes Features of Night Fog." Applied Mechanics and Materials 137 (October 2011): 297–301. http://dx.doi.org/10.4028/www.scientific.net/amm.137.297.
Full textAbstract:
The air flow and turbulent fluxes features during the radiation fog formed on the dawn of 17 October 2007 is discussed in order to study the mechanism of an unexpected night fog, based on the meteorological and turbulent data obtained from the 250 m height tower in Tianjin, as well as the NCEP reanalysis data and other observational data. The results show that the lower layer easterly flow coming from the south region of the Northeast cold high pressure led to remarkable temperature fall and humidity value increase in the daytime prior to the fog formation, which quickly turned the dry boundary layer to be moist. The vapor transfer indicated that the vapor of the radiation fog was provided by the easterly advection from Bohai Sea, not from local area. Turbulent vapor fluxes increased ten times as that before the fog. The horizontal vapor fluxes transported against the wind direction, which led to the escape of water vapor from Tianjin city and the dissipation of fog.
25
Singh, Jagdeep, Parminder Singh, El Mehdi Amhoud, and Mustapha Hedabou. "Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing." Sustainability 14, no. 19 (October 10, 2022): 12951. http://dx.doi.org/10.3390/su141912951.
Full textAbstract:
The number of client applications on the fog computing layer is increasing due to advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant role in reducing latency and enhancing resource usage for IoT users’ tasks. Along with its various benefits, fog computing also faces several challenges, including challenges related to resource overloading, security, node placement, scheduling, and energy consumption. In fog computing, load balancing is a difficult challenge due to the increased number of IoT devices and requests, which requires an equal load distribution throughout all available resources. In this study, we proposed a secure and energy-aware fog computing architecture, and we implemented a load-balancing technique to improve the complete utilization of resources with an SDN-enabled fog environment. A deep belief network (DBN)-based intrusion detection method was also implemented as part of the proposed techniques to reduce workload communication delays in the fog layer. The simulation findings showed that the proposed technique provided an efficient method of load balancing in a fog environment, minimizing the average response time, average energy consumption, and communication delay by 15%, 23%, and 10%, respectively, as compared with other existing techniques.
26
Aleisa, Mohammed A., Abdullah Abuhussein, Faisal S. Alsubaei, and Frederick T. Sheldon. "Novel Security Models for IoT–Fog–Cloud Architectures in a Real-World Environment." Applied Sciences 12, no. 10 (May 10, 2022): 4837. http://dx.doi.org/10.3390/app12104837.
Full textAbstract:
With the rise of the Internet of Things (IoT), there is a demand for computation at network edges because of the limited processing capacity of IoT devices. Fog computing is a middle layer that has appeared to address the latency issues between the Internet of things (IoT) and the cloud. Fog computing is becoming more important as companies face increasing challenges in collecting and sending data from IoT devices to the cloud. However, this has led to new security and privacy issues as a result of the large number of sensors in IoT environments as well as the massive amount of data that must be analyzed in real time. To overcome the security challenges between the IoT layer and fog layer and, thus, meet the security requirements, this paper proposes a fine-grained data access control model based on the attribute-based encryption of the IoT–Fog–Cloud architecture to limit the access to sensor data and meet the authorization requirements. In addition, this paper proposes a blockchain-based certificate model for the IoT–Fog–Cloud architecture to authenticate IoT devices to fog devices and meet the authentication requirements. We evaluated the performance of the two proposed security models to determine their efficiency in real-life experiments of the IoT–Fog–Cloud architecture. The results demonstrate that the performance of the IoT–Fog–Cloud architecture with and without the blockchain-based certificate model was the same when using one, two, or three IoT devices. However, the performance of the IoT–Fog–Cloud architecture without the access control model was slightly better than that of the architecture with the model when using one, two, or three IoT devices.
27
Ma, Kun, Antoine Bagula, Clement Nyirenda, and Olasupo Ajayi. "An IoT-Based Fog Computing Model." Sensors 19, no. 12 (June 21, 2019): 2783. http://dx.doi.org/10.3390/s19122783.
Full textAbstract:
The internet of things (IoT) and cloud computing are two technologies which have recently changed both the academia and industry and impacted our daily lives in different ways. However, despite their impact, both technologies have their shortcomings. Though being cheap and convenient, cloud services consume a huge amount of network bandwidth. Furthermore, the physical distance between data source(s) and the data centre makes delays a frequent problem in cloud computing infrastructures. Fog computing has been proposed as a distributed service computing model that provides a solution to these limitations. It is based on a para-virtualized architecture that fully utilizes the computing functions of terminal devices and the advantages of local proximity processing. This paper proposes a multi-layer IoT-based fog computing model called IoT-FCM, which uses a genetic algorithm for resource allocation between the terminal layer and fog layer and a multi-sink version of the least interference beaconing protocol (LIBP) called least interference multi-sink protocol (LIMP) to enhance the fault-tolerance/robustness and reduce energy consumption of a terminal layer. Simulation results show that compared to the popular max–min and fog-oriented max–min, IoT-FCM performs better by reducing the distance between terminals and fog nodes by at least 38% and reducing energy consumed by an average of 150 KWh while being at par with the other algorithms in terms of delay for high number of tasks.
28
Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.
Full textAbstract:
The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.
29
Andersen, Hendrik, Jan Cermak, Julia Fuchs, Peter Knippertz, Marco Gaetani, Julian Quinting, Sebastian Sippel, and Roland Vogt. "Synoptic-scale controls of fog and low-cloud variability in the Namib Desert." Atmospheric Chemistry and Physics 20, no. 6 (March 24, 2020): 3415–38. http://dx.doi.org/10.5194/acp-20-3415-2020.
Full textAbstract:
Abstract. Fog is a defining characteristic of the climate of the Namib Desert, and its water and nutrient input are important for local ecosystems. In part due to sparse observation data, the local mechanisms that lead to fog occurrence in the Namib are not yet fully understood, and to date, potential synoptic-scale controls have not been investigated. In this study, a recently established 14-year data set of satellite observations of fog and low clouds in the central Namib is analyzed in conjunction with reanalysis data in order to identify synoptic-scale patterns associated with fog and low-cloud variability in the central Namib during two seasons with different spatial fog occurrence patterns. It is found that during both seasons, mean sea level pressure and geopotential height at 500 hPa differ markedly between fog/low-cloud and clear days, with patterns indicating the presence of synoptic-scale disturbances on fog and low-cloud days. These regularly occurring disturbances increase the probability of fog and low-cloud occurrence in the central Namib in two main ways: (1) an anomalously dry free troposphere in the coastal region of the Namib leads to stronger longwave cooling of the marine boundary layer, increasing low-cloud cover, especially over the ocean where the anomaly is strongest; (2) local wind systems are modulated, leading to an onshore anomaly of marine boundary-layer air masses. This is consistent with air mass back trajectories and a principal component analysis of spatial wind patterns that point to advected marine boundary-layer air masses on fog and low-cloud days, whereas subsiding continental air masses dominate on clear days. Large-scale free-tropospheric moisture transport into southern Africa seems to be a key factor modulating the onshore advection of marine boundary-layer air masses during April, May, and June, as the associated increase in greenhouse gas warming and thus surface heating are observed to contribute to a continental heat low anomaly. A statistical model is trained to discriminate between fog/low-cloud and clear days based on information on large-scale dynamics. The model accurately predicts fog and low-cloud days, illustrating the importance of large-scale pressure modulation and advective processes. It can be concluded that regional fog in the Namib is predominantly of an advective nature and that fog and low-cloud cover is effectively maintained by increased cloud-top radiative cooling. Seasonally different manifestations of synoptic-scale disturbances act to modify its day-to-day variability and the balance of mechanisms leading to its formation and maintenance. The results are the basis for a new conceptual model of the synoptic-scale mechanisms that control fog and low-cloud variability in the Namib Desert and will guide future studies of coastal fog regimes.
30
Westcott, Nancy E. "Some Aspects of Dense Fog in the Midwestern United States." Weather and Forecasting 22, no. 3 (June 1, 2007): 457–65. http://dx.doi.org/10.1175/waf990.1.
Full textAbstract:
Abstract To better understand dense fog events in the midwestern United States, a fog climatology was developed that examines the surface weather conditions at dense fog onset and during dense fog events, in relationship to fog duration. Surface airways hourly observations for the period 1948–96 were examined, focusing primarily on Peoria, Illinois, during the cold season (October–March). Temperature, winds, and visibility at dense fog onset did not prove to be useful in differentiating between short- (1–2 h) and long- (>5 h) duration dense fog events. However, it was found that once dense fog forms, it is more likely to persist if the horizontal visibility is 200 m (1/8 mi) or less and the ceiling height lowers to 30 m (100 ft) or less. Further, dense fog events at Peoria tend to last longer if they are widespread, that is, when many other midwestern surface airways hourly stations also report dense fog. When dense fog develops early in the night to the hours just after midnight, it is more likely to persist than when it develops later in the night or during the day. This was found to be the case for many other midwestern stations as well. Fog events forming earlier in the night may last longer because of the absence of solar insolation upon the fog layer during the night. As longer-duration fogs often become more opaque and more widespread than short-duration events, more time may be required to dissipate fog once the sun has risen. Dense fog onset time and the physical dimensions of the fog events appear to be the best predictors of fog duration considering all types of fog in the Midwest.
31
van der Velde, I. R., G. J. Steeneveld, B. G. J. Wichers Schreur, and A. A. M. Holtslag. "Modeling and Forecasting the Onset and Duration of Severe Radiation Fog under Frost Conditions." Monthly Weather Review 138, no. 11 (November 1, 2010): 4237–53. http://dx.doi.org/10.1175/2010mwr3427.1.
Full textAbstract:
Abstract A case of a severe radiation fog during frost conditions is analyzed as a benchmark for the development of a very high-resolution NWP model. Results by the Weather Research and Forecasting model (WRF) and the High-Resolution Limited-Area Model (HIRLAM) are evaluated against detailed observations to determine the state-of-the-art in fog forecasting and to derive requirements for further research and development. For this particular difficult case, WRF is unable to correctly simulate the fog for any of the parameterizations and model configurations utilized. Contrary, HIRLAM does model the onset of fog, but is unable to represent it beyond the lowest model layer, which leads to an early dispersal of fog in the morning transition. The sensitivity of fog forecasts to model formulation is further analyzed with a high-resolution single-column version of HIRLAM, and with the Duynkerke single-column model as a reference. The single-column results are found to be sensitive to the proper specification of the external forcings. It is reconfirmed that high vertical resolution is essential for modeling the fog formation, the growth of the fog layer, and when the fog lifts for the maintenance of a stratus deck. The properly configured column models are able to accurately model the onset of fog and its maturation, but fail in the simulation of fog persistence and subsequent dispersal. Details of the turbulence parameterization appear to be important in this process. It is concluded that, despite all of the advances in numerical weather prediction, fog forecasting is still a major challenge.
32
Sugimoto, Shiori, Tomonori Sato, and Kazuki Nakamura. "Effects of Synoptic-Scale Control on Long-Term Declining Trends of Summer Fog Frequency over the Pacific Side of Hokkaido Island." Journal of Applied Meteorology and Climatology 52, no. 10 (October 2013): 2226–42. http://dx.doi.org/10.1175/jamc-d-12-0192.1.
Full textAbstract:
AbstractIn this study, long-term visibility data for the Pacific Ocean side of Hokkaido Island, northeast Japan, are investigated to clarify the relationship between interannual variation in summer fog frequency (FF) and large-scale circulation patterns. At Kushiro, a significant FF decrease is found during 1931–2010 even without the influence of the observatory's relocation after 2000. In particular, since the late 1970s, a linear declining trend has accelerated, as evidenced by an increased number of years with very low FF in July and August. To clarify the climatological factor causing the summer FF declining trend at Kushiro, atmospheric vertical conditions in the planetary boundary layer and large-scale circulation are examined during 1989–2009 and 1958–2002, respectively, using available datasets. Composite analyses that are based on radiosonde observations reveal that the shallow fog layer is covered with a strong inversion layer during fog days whereas the inversion layer is absent during nonfog days. Composite circulation anomalies for the low-FF years at Kushiro show an intensified Okhotsk high (OH) pressure feature and southward shrinking of the North Pacific high (NPH) in July, in addition to the eastward displacement or shrinking of the NPH in August. These anomalous synoptic circulation patterns cause weakening in the southerly–southeasterly wind, which reduces sea-fog advection toward Kushiro and prevents the formation of stable stratification over the sea-fog layer. The authors suggest that the interannual variation in summer FF with the recent accelerated decline at Kushiro is primarily controlled by changes in the synoptic circulation associated with the OH and NPH development.
33
Liu, Shuo, Li Tian, Zhongyan Lu, Han Sai, Chenghan Liu, and Ping Li. "The Boundary Layer Characteristics and Development Mechanism of a Warm Advective Fog Event over the Yellow Sea." Journal of Physics: Conference Series 2486, no. 1 (May 1, 2023): 012004. http://dx.doi.org/10.1088/1742-6596/2486/1/012004.
Full textAbstract:
Abstract A large-scale persistent fog event occurred over the Yellow Sea of China from April 27 to May 4, 2015. In this study, we used satellite remote sensing data, ground meteorological observed data, global sounding data, and reanalysis data from the National Centers for Environmental Prediction (NCEP) and sea surface temperature (SST) data from the National Oceanic and Atmospheric Administration (NOAA) to analyze the evolutionary characteristics, the boundary-layer marine meteorological characteristics, and the development mechanism of the sea fog event. The results show that the sea fog event was a warm advective fog process. The Yellow Sea was at the rear of the warm high and the front of the continental low (circulation situation with high in the east and low in the west). The southerly and southeasterly winds transported warm and moist air from the Northwest Pacific northward to the Yellow Sea which served as the water vapor source. A thermal turbulence interface was formed during the sea fog development. The weak vertical wind shear below the interface was conducive to the maintenance and development of sea fog in the area of the temperature inversion in the boundary layer and the formation of a certain thickness of sea fog. The sea fog occurred in the area with water vapor convergence, where the 2-m dew point was slightly higher than the SST. The area with a relative humidity greater than 90% and an air–sea temperature difference from 0 to 2 °C overlapped with the sea fog area, and these two values indicate the extent of the sea fog.
34
Bodaballa, Jeevan Kumar, and Gabriella Schmeller. "Assessment of WRF planetary boundary layer schemes in the simulation of fog events over Hungary." Időjárás 127, no. 1 (2023): 1–22. http://dx.doi.org/10.28974/idojaras.2023.1.1.
Full textAbstract:
Accurate depiction of meteorological conditions, especially within the planetary boundary layer (PBL), is essential for fog forecasting. This study examines the sensitivity of the performance of the Weather Research and Forecast (WRF) model to the use of four different PBL schemes [Yonsei University (YSU), asymmetric convective model version 2 (ACM2), quasinormal scale elimination (QNSE), and Mellor-Yamada-Nakanishi-Niino version 3.0 (MYNN3)]. For this case study we have taken the fog event occurred in November 23-24, 2020. Surface observed temperature and relative humidity, furthermore, sounding data are compared with the output of the 36 hours, high-resolution weather forecast. The horizontal extension of the simulated fog is compared with satellite observations. The visibility is calculated from the prognostic variables of drop number concentration and mixing ratio. The simulated visibility and fog duration are validated by the visibility and fog duration evaluated by ceilometer observations. Validation of thermodynamical values such as 2-m temperature and relative humidity reveals, that during most of the simulation time, the bias is significant between the simulated and observed data. Results show that the PBL parameterization scheme significantly impacts fog microphysics also. The QNSE scheme results in unrealistic early formation of the fog, and too large liquid water content. YSU and ACM2 simulated the duration of fog to be rather short comparing with the other two PBL schemes. The best fitting with observed data is found in the case of MYNN3 PBL schemes.
35
Na, Uikyun, and Eun-Kyu Lee. "Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System." Sustainability 12, no. 7 (April 2, 2020): 2831. http://dx.doi.org/10.3390/su12072831.
Full textAbstract:
It has been found that a cloud building energy management system (BEMS) alone cannot support increasing numbers of end devices (e.g., energy equipment and IoT devices) and emerging energy services efficiently. To resolve these limitations, this paper proposes Fog BEMS, which applies an emerging fog computing concept to a BEMS. Fog computing places small computing resources (fog nodes) just next to end devices, and these nodes process data in real time and manage local contexts. In this way, the BEMS becomes distributed and scalable. However, existing fog computing models have barely considered scenarios where many end devices and fog nodes are deployed and interconnected. That is, they do not scale up and cannot be applied to scalable applications like BEMS. To solve the problem, this paper (i) designs a fog network where a list of functionally heterogeneous nodes is deployed in a hierarchy for collaboration and (ii) designs an agent-based, modular programming model that eases the development and management of computing services at a fog node. We develop a prototype of a fog node and build a real-world testbed on a campus to demonstrate the feasibility of the proposed system. We also conduct experiments, and results show that Fog BEMS is scalable enough for a node to connect up to 900 devices and that network traffic is reduced by 27.22–97.63%, with varying numbers of end devices.
36
Haj Qasem, Mais, Alaa Abu-Srhan, Hutaf Natoureah, and Esra Alzaghoul. "Fog Computing Framework for Smart City Design." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 01 (January 20, 2020): 109. http://dx.doi.org/10.3991/ijim.v14i01.9762.
Full textAbstract:
Fog-computing is a new network architecture and computing paradigm that uses user or near-users devices (network edge) to carry out some processing tasks. Accordingly, it extends the cloud computing with more flexibility the one found in the ubiquitous networks. A smart city based on the concept of fog-computing with flexible hierarchy is proposed in this paper. The aim of the proposed design is to overcome the limitations of the previous approaches, which depends on using various network architectures, such as cloud-computing, autonomic network architecture and ubiquitous network architecture. Accordingly, the proposed approach achieves a reduction of the latency of data processing and transmission with enabled real-time applications, distribute the processing tasks over edge devices in order to reduce the cost of data processing and allow collaborative data exchange among the applications of the smart city. The design is made up of five major layers, which can be increased or merged according to the amount of data processing and transmission in each application. The involved layers are connection layer, real-time processing layer, neighborhood linking layer, main-processing layer, data server layer. A case study of a novel smart public car parking, traveling and direction advisor is implemented using IFogSim and the results showed that reduce the delay of real-time application significantly, reduce the cost and network usage compared to the cloud-computing paradigm. Moreover, the proposed approach, although, it increases the scalability and reliability of the users’ access, it does not sacrifice much time, nor cost and network usage compared to fixed fog-computing design.
37
Hu, Jianqiang, Wei Liang, Zhiyong Zeng, Yong Xie, and Jianxun Yang. "A framework for Fog-assisted healthcare monitoring." Computer Science and Information Systems 16, no. 3 (2019): 753–72. http://dx.doi.org/10.2298/csis180930025h.
Full textAbstract:
In order to tackle some challenges in ubiquitous healthcare monitoring such as mobility, scalability, and network latency, a framework for Fog-assisted healthcare monitoring is proposed in this paper. This framework is composite of body-sensing layer, Fog layer (Fog-assisted gateway), Cloud layer (health Cloud). And then, this paper makes an intensive study in some key technologies of the proposed framework such as an IPv6-based network architecture, intelligent warning model based on subband energy feature, security framework of HL7 RIM-based data exchange, health risk assessment based on fusion of grey model and Markov model. Finally, results of experiment depict that the proposed intelligent warning model can make immediate distinction abnormal signals. Moreover, the proposed health assessment model confirms its effectiveness with respect to 245 patients in Xiamen District of Jimei.
38
Boers, R., H. Klein Baltink, H. J. Hemink, F. C. Bosveld, and M. Moerman. "Ground-Based Observations and Modeling of the Visibility and Radar Reflectivity in a Radiation Fog Layer." Journal of Atmospheric and Oceanic Technology 30, no. 2 (February 1, 2013): 288–300. http://dx.doi.org/10.1175/jtech-d-12-00081.1.
Full textAbstract:
Abstract The development of a radiation fog layer at the Cabauw Experimental Site for Atmospheric Research (51.97°N, 4.93°E) on 23 March 2011 was observed with ground-based in situ and remote sensing observations to investigate the relationship between visibility and radar reflectivity. The fog layer thickness was less than 200 m. Radar reflectivity values did not exceed −25 dBZ even with visibilities less than 100 m. The onset and evaporation of fog produce different radar reflectivity–visibility relationships. The evolution of the fog layer was modeled with a droplet activation model that used the aerosol size distribution observed at the 60-m altitude tower level as input. Radar reflectivity and visibility were calculated from model drop size spectra using Mie scattering theory. Since radiative cooling rates are small in comparison with cooling rates due to adiabatic lift of aerosol-laden air, the modeled supersaturation remains low so that few aerosol particles are activated to cloud droplets. The modeling results suggest that the different radar reflectivity–visibility relationships are the result of differences in the interplay between water vapor and cloud droplets during formation and evaporation of the fog. During droplet activation, only a few large cloud droplets remain after successfully competing for water vapor with the smaller activated droplets. These small droplets eventually evaporate (deactivate) again. In the fog dissolution/evaporation stage, only these large droplet need to be evaporated. Therefore, to convert radar reflectivity to visibility for traffic safety products, knowledge of the state of local fog evolution is necessary.
39
Priyadarshini, Rojalina, Rabindra Kumar Barik, Harish Chandra Dubey, and Brojo Kishore Mishra. "A Survey of Fog Computing-Based Healthcare Big Data Analytics and Its Security." International Journal of Ambient Computing and Intelligence 12, no. 2 (April 2021): 53–72. http://dx.doi.org/10.4018/ijaci.2021040104.
Full textAbstract:
Growing use of wearables within internet of things (IoT) creates ever-increasing multi-modal data from various smart health applications. The enormous volume of data generation creates new challenges in transmission, storage, and processing. There were challenges such as communication latency and data security associated with processing medical big data in cloud backend. Fog computing (FC) is an emerging distributed computing paradigm that solved these problems by leveraging local data processing, storage, filtering, and machine intelligence within an intermediate fog layer that resides between cloud and wearables devices. This paper focuses on doing survey on two major aspects of deploying fog computing for smart and connected health. Firstly, the role of machine learning-based edge intelligence in fog layer for data processing is investigated. A comprehensive analysis is provided during the survey, highlighting the strength and improvements in the existing literature. The paper ends with some open challenges and future research areas in the domain of fog-based healthcare.
40
Zhang, Lei. "Exploration of Intelligent Manufacturing Methods for Complex Products Driven by Multisource Data." Advances in Multimedia 2022 (January 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2452533.
Full textAbstract:
In order to improve the multisource data-driven fusion effect in the intelligent manufacturing process of complex products, based on the proposed adaptive fog computing architecture, this paper takes into account the efficient processing of complex product intelligent manufacturing services within the framework and the rational utilization of fog computing layer resources to establish a fog computing resource scheduling model. Moreover, this paper proposes a fog computing architecture for intelligent manufacturing services for complex products. The architecture adopts a three-layer fog computing framework, which can reasonably provide three types of services in the field of intelligent manufacturing. In addition, this study combines experimental research to verify the intelligent model of this article and counts the experimental results. From the analysis of experimental data, it can be seen that the complex product intelligent manufacturing system based on multisource data driven proposed in this paper meets the data fusion requirements of complex product intelligent manufacturing.
41
Luan, Tian, Xueliang Guo, Lijun Guo, and Tianhang Zhang. "Quantifying the relationship between PM<sub>2.5</sub> concentration, visibility and planetary boundary layer height for long-lasting haze and fog–haze mixed events in Beijing." Atmospheric Chemistry and Physics 18, no. 1 (January 8, 2018): 203–25. http://dx.doi.org/10.5194/acp-18-203-2018.
Full textAbstract:
Abstract. Air quality and visibility are strongly influenced by aerosol loading, which is driven by meteorological conditions. The quantification of their relationships is critical to understanding the physical and chemical processes and forecasting of the polluted events. We investigated and quantified the relationship between PM2.5 (particulate matter with aerodynamic diameter is 2.5 µm and less) mass concentration, visibility and planetary boundary layer (PBL) height in this study based on the data obtained from four long-lasting haze events and seven fog–haze mixed events from January 2014 to March 2015 in Beijing. The statistical results show that there was a negative exponential function between the visibility and the PM2.5 mass concentration for both haze and fog–haze mixed events (with the same R2 of 0.80). However, the fog–haze events caused a more obvious decrease of visibility than that for haze events due to the formation of fog droplets that could induce higher light extinction. The PM2.5 concentration had an inversely linear correlation with PBL height for haze events and a negative exponential correlation for fog–haze mixed events, indicating that the PM2.5 concentration is more sensitive to PBL height in fog–haze mixed events. The visibility had positively linear correlation with the PBL height with an R2 of 0.35 in haze events and positive exponential correlation with an R2 of 0.56 in fog–haze mixed events. We also investigated the physical mechanism responsible for these relationships between visibility, PM2.5 concentration and PBL height through typical haze and fog–haze mixed event and found that a double inversion layer formed in both typical events and played critical roles in maintaining and enhancing the long-lasting polluted events. The variations of the double inversion layers were closely associated with the processes of long-wave radiation cooling in the nighttime and short-wave solar radiation reduction in the daytime. The upper-level stable inversion layer was formed by the persistent warm and humid southwestern airflow, while the low-level inversion layer was initially produced by the surface long-wave radiation cooling in the nighttime and maintained by the reduction of surface solar radiation in the daytime. The obvious descending process of the upper-level inversion layer induced by the radiation process could be responsible for the enhancement of the low-level inversion layer and the lowering PBL height, as well as high aerosol loading for these polluted events. The reduction of surface solar radiation in the daytime could be around 35 % for the haze event and 94 % for the fog–haze mixed event. Therefore, the formation and subsequent descending processes of the upper-level inversion layer should be an important factor in maintaining and strengthening the long-lasting severe polluted events, which has not been revealed in previous publications. The interactions and feedbacks between PM2.5 concentration and PBL height linked by radiation process caused a more significant and long-lasting deterioration of air quality and visibility in fog–haze mixed events. The interactions and feedbacks of all processes were particularly strong when the PM2.5 mass concentration was larger than 150–200 µg m−3.
42
Zhou, Binbin, and Brad S. Ferrier. "Asymptotic Analysis of Equilibrium in Radiation Fog." Journal of Applied Meteorology and Climatology 47, no. 6 (June 1, 2008): 1704–22. http://dx.doi.org/10.1175/2007jamc1685.1.
Full textAbstract:
Abstract A vertical distribution formulation of liquid water content (LWC) for steady radiation fog was obtained and examined through the singular perturbation method. The asymptotic LWC distribution is a consequential balance among cooling, droplet gravitational settling, and turbulence in the liquid water budget of radiation fog. The cooling produces liquid water, which is depleted by turbulence near the surface. The influence of turbulence on the liquid water budget decreases with height and is more significant for shallow fogs than for deep fogs. The depth of the region of surface-induced turbulence can be characterized with a fog boundary layer (FBL). The behavior of the FBL bears some resemblance to the surface mixing layer in radiation fog. The characteristic depth of the FBL is thinner for weaker turbulence and stronger cooling, whereas if turbulence intensity increases or cooling rate decreases then the FBL will develop from the ground. The asymptotic formulation also reveals a critical turbulent exchange coefficient for radiation fog that defines the upper bound of turbulence intensity that a steady fog can withstand. The deeper a fog is, the stronger a turbulence intensity it can endure. The persistence condition for a steady fog can be parameterized by either the critical turbulent exchange coefficient or the characteristic depth of the FBL. If the turbulence intensity inside a fog is smaller than the turbulence threshold, the fog persists, whereas if the turbulence intensity exceeds the turbulence threshold or the characteristic depth of the FBL dominates the entire fog bank then the balance will be destroyed, leading to dissipation of the existing fog. The asymptotic formulation has a first-order approximation with respect to turbulence intensity. Verifications with numerical solutions and an observed fog event showed that it is more accurate for weak turbulence than for strong turbulence and that the computed LWC generally agrees with the observed LWC in magnitude.
43
Price, Jeremy D. "Ismail Gultepe (ed): Fog and Boundary Layer Clouds." Surveys in Geophysics 29, no. 1 (January 2008): 65–66. http://dx.doi.org/10.1007/s10712-008-9035-1.
Full text
44
Kim, Wonheung, Seong Soo Yum, Jinkyu Hong, and Jae In Song. "Improvement of Fog Simulation by the Nudging of Meteorological Tower Data in the WRF and PAFOG Coupled Model." Atmosphere 11, no. 3 (March 23, 2020): 311. http://dx.doi.org/10.3390/atmos11030311.
Full textAbstract:
Improvement of fog simulation accuracy was investigated for the fogs that occurred on the south coast of the Korean Peninsula using the WRF (3D) and PAFOG (1D) coupled model. In total, 22 fog cases were simulated and accuracy of the fog simulation was examined based on Critical Success Index, Hit Rate and False Alarm Rate. The performance of the coupled WRF-PAFOG model was better than that of the single WRF model as expected. However, much more significant improvement appeared only when the data from a 300 m meteorological tower was not only used for the initial conditions but also nudged during the simulation. Moreover, a proper prescription of soil moisture was found to be important for accurate fog simulation especially for the fog cases with prior precipitation since efficient moisture supply from the precipitation-soaked soil might have been critical for fog formation. It was also demonstrated that with such optimal coupled model setting, a coastal radiation fog event with prior precipitation could be very realistically simulated: the fog onset and dissipation times matched so well with observation. In detail, radiative cooling at the surface was critical to form a surface inversion layer as the night fell. Then the vapor flux from the precipitation-soaked surface was confined within the inversion layer to form fog. It is suggested that a proper prescription of soil moisture in the model based on observations, if readily available, could be a cost-effective method for improving operational fog forecasting, considering the fact that tall meteorological towers are a rarity in the world.
45
Golkar, Ali, Razieh Malekhosseini, Keyvan RahimiZadeh, Azita Yazdani, and Amin Beheshti. "A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments." Health Informatics Journal 28, no. 4 (October 2022): 146045822211374. http://dx.doi.org/10.1177/14604582221137453.
Full textAbstract:
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.
46
Anber, Usama, Pierre Gentine, Shuguang Wang, and Adam H. Sobel. "Fog and rain in the Amazon." Proceedings of the National Academy of Sciences 112, no. 37 (August 31, 2015): 11473–77. http://dx.doi.org/10.1073/pnas.1505077112.
Full textAbstract:
The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models, exhibiting peak evapotranspiration in the wrong season and rain too early in the day. We show that those biases are not present in cloud-resolving simulations with parameterized large-scale circulation. The difference is attributed to the representation of the morning fog layer, and to more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present in the wet season but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget. These results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents.
47
Pinzón Castellanos, Javier, and Miguel Antonio Cadena Carter. "Fog Computing in the context of Smart Home, voice assistant and the future of IoT." Revista Colombiana de Computación 21, no. 1 (June 1, 2020): 6–12. http://dx.doi.org/10.29375/25392115.3894.
Full textAbstract:
Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.
48
Reuge, N., P. Fede, J. F. Berthoumieu, F. Foucoin, and O. Simonin. "Modeling of the Denebulization of Warm Fogs by Hygroscopic Seeding: Effect of Various Operating Conditions and of the Turbulence Intensity." Journal of Applied Meteorology and Climatology 56, no. 2 (February 2017): 249–61. http://dx.doi.org/10.1175/jamc-d-16-0151.1.
Full textAbstract:
AbstractThis study addresses the modeling of the denebulization (i.e., the removal of droplets) of warm fogs (T ≥ 0°C) by hygroscopic salt microparticles from the initial seeding at the top of the fog layer to the fall of the rain droplets on the ground. Two main phenomena can occur: condensation of water vapor on salted droplets and the concomitant evaporation of fog droplets, and coalescence between the salted droplets and the fog droplets. Three salts have been investigated: NaCl, CaCl2, and KCl. Based on the conservation equations, the modeling approach (1D) considers the hygroscopicity of the salts through the water activity in the aqueous solution and the coalescence induced by gravity and turbulence. From this study, NaCl is the most efficient salt in the tested operating conditions. Actually, this result can be explained by the strong hygroscopicity of this salt in very dilute solutions. From the calculations, 15 kg of NaCl particles of 6.7-μm diameter can dissipate a typical fog layer of 40 m in height within less than 17 min over 0.25 km2. According to the calculations, a fog layer of 100 m in height can be denebulized within 45 min. The contribution of the coalescence induced by gravity and by turbulence seems to have a negligible effect on the final horizontal visibility, the condensation/evaporation phenomena being preponderant for these operating conditions.
49
Klimenko, Anna B., and Ol’ga V. Klimenko. "CONSERVATION OF FOG LAYER BROKER COMPUTE NODE RESOURCE IN DATA PROCESSING APPLICATIONS." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 2 (2023): 30–45. http://dx.doi.org/10.28995/2686-679x-2023-2-30-45.
Full textAbstract:
In accordance with the concept of fog computing, when processing large amounts of data, the computing workload shifts from the “cloud” to the edge of the network, to the nodes of the fog layer. At the same time, however, a very small number of published works deal with the issues of reducing with that the residual resource of peripheral devices, which, as a rule, are not of a high computational power comparing with devices in data centers. The reliability indicator, the probability of failure-free operation are also associated with such a characteristic as the average residual resource – the value of the reciprocal of the failure rate, and in general, the decreasing trend of the value characterizes the safety of the computing resource of the device and the time during which its operation will be expedient due to the allowable failure rate. Modern implementations of the concept of fog computing suggest the presence of a so-called cloud-fog broker, which is entrusted with the functions of computing scheduling that can be performed by nearby fog layer nodes. Obviously, in this case, the cloud-fog broker operates with an increased load, and it is quite reasonable to raise the question of saving the resource of the node. The article is concerned with the study of comparing the “selfish” strategy of distributing the computational load over the nodes of the foggy layer in the network and the approach based on the choice of a node for computing based on the simulation of the average residual resource. The proposed algorithm makes it possible, depending on the preferences in the safety of the resource of the node, to implement the choice of device. The simulation performed demonstrates the expediency of including the fog layer broker in the subset of considered candidates for load distribution even in the case when, in accordance with the “selfish” approach, it is more profitable for the broker to transfer calculations to the next node. The latter is also relevant for the case when it is a priority to reduce resource consumption for a group of devices, including a broker
50
De Paula, Nórton Franciscatto, Franciano Scremin Puhales, Gabriele Golart Silva, Jéssica Melo Mintegui, Vagner Anabor, Everson Dal Piva, and Felipe Denardin Costa. "AVALIAÇÃO DE DIFERENTES ESQUEMAS DE CAMADA LIMITE PARA SIMULAÇÃO E PREVISÃO DE EVENTOS DE NEVOEIRO NO RIO GRANDE DO SUL." Ciência e Natura 39, no. 2 (May 23, 2017): 451. http://dx.doi.org/10.5902/2179460x25940.
Full textAbstract:
In this work we used two boundary layer schemes to reproduce numerically a persistent fog event in Santa Maria, Rio Grande do Sul. The model employed was WRF using, as initial and boundary conditions, GFS data. The boundary layer employed schemes were the Mellor-Yamada-Janic (MYJ) and Yonsei University (YSU). Generally, boundary layer overestimate of wind speed values schemes 10m and represent relatively well temperature. However, the relative humidity does not represent the fog condition, not reaching saturation in some cases. When saturation is achieved the same if not properly maintained during the occurrence of the event.