Siga este enlace para ver otros tipos de publicaciones sobre el tema: Fog systems.
Artículos de revistas sobre el tema "Fog systems"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Fog systems".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
1
Phan, Linh-An, Duc-Thang Nguyen, Meonghun Lee, Dae-Heon Park y Taehong Kim. "Dynamic fog-to-fog offloading in SDN-based fog computing systems". Future Generation Computer Systems 117 (abril de 2021): 486–97. http://dx.doi.org/10.1016/j.future.2020.12.021.
Texto completo
2
Sandvik, Jens-Petter, Katrin Franke, Habtamu Abie y André Årnes. "Evidence in the fog – Triage in fog computing systems". Forensic Science International: Digital Investigation 44 (marzo de 2023): 301506. http://dx.doi.org/10.1016/j.fsidi.2023.301506.
Texto completo
3
Benila S, Benila S. y Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems". 網際網路技術學刊 23, n.º 2 (marzo de 2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.
Texto completoResumen
<p>In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.</p> <p> </p>
4
Zhurylo, Oleh y Oleksii Liashenko. "Architecture and iot security systems based on fog computing". INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, n.º 1 (27) (2 de julio de 2024): 54–66. http://dx.doi.org/10.30837/itssi.2024.27.054.
Texto completoResumen
The subject of the study is is the security architecture of the Internet of Things (IoT) based on fog computing, which allows providing efficient and secure services for many IoT users. The goal is to investigate the security architecture for IoT systems based on fog computing. To achieve the goal, the following tasks were solved: the concept of fog computing is proposed, its architecture is considered and a comparative analysis of fog and cloud computing architectures is made; the principles of designing and implementing the architecture of a fog computing system are outlined; multi-level security measures based on fog computing are investigated; and the areas of use of fog computing-based Internet of Things networks are described. When performing the tasks, such research methods were used as: theoretical analysis of literature sources; analysis of the principles of designing and implementing the security architecture of the Internet of Things; analysis of security measures at different levels of the architecture. The following results were obtained: the architecture of fog computing is considered and compared with the cloud architecture; the principles of designing and implementing the architecture of fog computing systems are formulated; multi-level IoT security measures based on fog computing are proposed. Conclusions: research on IoT security systems based on fog computing has important theoretical implications. The fog computing architecture, in contrast to the cloud architecture, better meets the demand for high traffic and low latency of mobile applications, providing more advantages for systems that require real-time information processing. When designing and implementing the architecture of fog computing systems, the factors of memory capacity, latency, and utility should be taken into account to effectively integrate fog technologies with IoT. To ensure a high level of system security, multi-level security measures should be implemented using both software and hardware solutions.
5
Pinche, Cristobal y Loren Ruiz. "Fog on the brine — Fog-catching systems for arid lands". Waterlines 14, n.º 4 (abril de 1996): 4–7. http://dx.doi.org/10.3362/0262-8104.1996.013.
Texto completo
6
Alenizi, Faten y Omer Rana. "Dynamically Controlling Offloading Thresholds in Fog Systems". Sensors 21, n.º 7 (3 de abril de 2021): 2512. http://dx.doi.org/10.3390/s21072512.
Texto completoResumen
Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.
7
Almulifi, Asma y Heba Kurdi. "The Role of Fog Device Density in IoT-Fog-Cloud Systems". Procedia Computer Science 241 (2024): 242–47. http://dx.doi.org/10.1016/j.procs.2024.08.033.
Texto completo
8
Marir, Souad, Faiza Belala y Nabil Hameurlain. "A Strategy-Based Formal Approach for Fog Systems Analysis". Future Internet 14, n.º 2 (9 de febrero de 2022): 52. http://dx.doi.org/10.3390/fi14020052.
Texto completoResumen
Fog systems are a new emergent technology having a wide range of architectures and pronounced needs making their design complex. Consequently, the design of fog systems is crucial, including service portability and interoperability between the various elements of a system being the most essential aspects of fog computing. This article presents a fog system cross-layer architecture as a first step of such a design to provide a graphical and conceptual description. Then, a BiAgents* (Bigraphical Agents) formal model is defined to provide a rigorous description of physical, virtual, and behavioural aspects of Fog systems. Besides, this formalisation is implemented and executed under a Maude strategy system. The proposed approach is illustrated through a case study: an airport terminal Luggage Inspection System (LIS) while checking the correctness of its relevant properties: the portability of data and their interoperability. The integration of the Maude strategies in the rewriting of Fog system states made it possible to guide the execution of the model and its analysis.
9
Li, Xuewei, Yuchen Jia, Yufei Chen, Guanyang Xing, Xiaohua Zhao y Jian Rong. "Safety Evaluation of Fog Warning Systems in a Connected Vehicle Environment Based on Sample Entropy". Journal of Advanced Transportation 2021 (6 de octubre de 2021): 1–15. http://dx.doi.org/10.1155/2021/3047756.
Texto completoResumen
Changes in driving behavior caused by reduced visibility in fog can lead to crashes. To improve driving safety in fog weather, a fog warning system based on connected vehicle (CV) technology is proposed. From the perspective of human factors, this study evaluates the driving safety based on drivers’ speed change under different fog levels (i.e., no fog, light fog, and heavy fog) and different technical levels (i.e., normal, with a dynamic message sign (DMS), and with a human-machine interface (HMI)). The driving behavior data were collected by a driving simulation experiment. The experimental road was divided into three zones: clear zone, transition zone, and fog zone. To quantify the change of vehicle speed comprehensively, the speed and acceleration were selected. Meanwhile, the vehicle speed safety entropy and acceleration safety entropy were proposed based on sample entropy theory. Furthermore, the changes of each index in different zones were analyzed. The results show that the use of fog warning system can improve speed stability and driving safety in fog zones and can make the driver decelerate in advance with a smaller deceleration before entering the fog zones. The higher the technical level is, the earlier the driver decelerates. Under the condition of light fog, the fog warning system with HMI has a better effect in terms of improving speed stability, while under the condition of heavy fog, there is little difference between the two technical levels. In general, this study proposed a novel safety evaluation index and a general evaluation method of the fog warning system.
10
Hegarty, R. y M. Taylor. "Digital evidence in fog computing systems". Computer Law & Security Review 41 (julio de 2021): 105576. http://dx.doi.org/10.1016/j.clsr.2021.105576.
Texto completo
11
Kolesnyk, Zakhar, Oleksandr Mezhenskyi, Oleksandr Davykoza y Heorhii Kuchuk. "FOG COMPUTING TECHNOLOGY IN DISTRIBUTED SYSTEMS". Системи управління, навігації та зв’язку. Збірник наукових праць 1, n.º 75 (9 de febrero de 2024): 94–97. http://dx.doi.org/10.26906/sunz.2024.1.094.
Texto completoResumen
Topicality. The concept of fog computing is an evolutionary stage in the development of the cloud concept. It occupies a leading position among the general trends in the development of information technology. The emergence of this concept is closely related to the origin and development of the concept of the Internet of Things. The results. The subject area was analyzed. It includes an analysis of current trends in the field of organizing distributed computing, an analysis of the use of population algorithms and ontology models for solving optimization problems in distributed systems, an analysis of models, methods and algorithms for solving the problem of transferring the computational load in distributed systems implemented on the basis of fog computing. Conclusion. It has been revealed that the concept of fog computing makes it possible to solve most of the problems associated with the load on the communication infrastructure and the latency of information exchange. But they do not resolve issues related to the high dynamism of the foggy environment and the concomitant decrease in the efficiency of the distributed system.
12
Pagel, Peter y Stefan Schulte. "Fog Computing". Informatik Spektrum 42, n.º 4 (agosto de 2019): 233–35. http://dx.doi.org/10.1007/s00287-019-01211-z.
Texto completo
13
Martikainen, A. L. "Fog removal with a fog mesh—mist eliminators and multiple mesh systems". International Journal of Mining, Reclamation and Environment 21, n.º 3 (septiembre de 2007): 185–97. http://dx.doi.org/10.1080/17480930701325061.
Texto completo
14
shoker, Asmaa, Mohammed Amoon, ,. Ayman M. Bahaa-Eldin y Nirmeen A. El-Bahnasawy. "Resource Allocation Strategy in Fog Computing: Task Scheduling in Fog Computing Systems". Journal of Communication Sciences and Information Technology 1, n.º 1 (1 de julio de 2023): 1–11. http://dx.doi.org/10.21608/jcsit.2023.306757.
Texto completo
15
Hassan, Syed Rizwan, Ateeq Ur Rehman, Naif Alsharabi, Salman Arain, Asim Quddus y Habib Hamam. "Design of load-aware resource allocation for heterogeneous fog computing systems". PeerJ Computer Science 10 (18 de abril de 2024): e1986. http://dx.doi.org/10.7717/peerj-cs.1986.
Texto completoResumen
The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.
16
Markus, Andras, Mate Biro, Gabor Kecskemeti y Attila Kertesz. "Actuator behaviour modelling in IoT-Fog-Cloud simulation". PeerJ Computer Science 7 (30 de julio de 2021): e651. http://dx.doi.org/10.7717/peerj-cs.651.
Texto completoResumen
The inevitable evolution of information technology has led to the creation of IoT-Fog-Cloud systems, which combine the Internet of Things (IoT), Cloud Computing and Fog Computing. IoT systems are composed of possibly up to billions of smart devices, sensors and actuators connected through the Internet, and these components continuously generate large amounts of data. Cloud and fog services assist the data processing and storage needs of IoT devices. The behaviour of these devices can change dynamically (e.g. properties of data generation or device states). We refer to systems allowing behavioural changes in physical position (i.e. geolocation), as the Internet of Mobile Things (IoMT). The investigation and detailed analysis of such complex systems can be fostered by simulation solutions. The currently available, related simulation tools are lacking a generic actuator model including mobility management. In this paper, we present an extension of the DISSECT-CF-Fog simulator to support the analysis of arbitrary actuator events and mobility capabilities of IoT devices in IoT-Fog-Cloud systems. The main contributions of our work are: (i) a generic actuator model and its implementation in DISSECT-CF-Fog, and (ii) the evaluation of its use through logistics and healthcare scenarios. Our results show that we can successfully model IoMT systems and behavioural changes of actuators in IoT-Fog-Cloud systems in general, and analyse their management issues in terms of usage cost and execution time.
17
Sadatacharapandi, Thiruchadai Pandeeswari y Padmavathi S. "Survey on Service Placement, Provisioning, and Composition for Fog-Based IoT Systems". International Journal of Cloud Applications and Computing 12, n.º 1 (1 de enero de 2022): 1–14. http://dx.doi.org/10.4018/ijcac.305212.
Texto completoResumen
In the modern IoT era, time and resource critical applications are more prevalent. Though cloud computing enables fast processing of data and producing results, the time and network overhead are huge. Fog based solutions have proved to eliminate this overhead. However, establishing a scalable fog based architecture for IoT applications involves a number of challenges such as guaranteeing QoS, reducing operational costs etc. Hence a viable fog based architecture must involve optimal service placement facilitated by suitable schemes for resource estimation and reservation, QoS monitoring, data interchange and integration. This paper surveys various service placement and optimization approaches proposed in the literature and compares them. This paper also explores various components that are required for composing a viable fog based architecture and presents a representative fog provisioning framework. Relevance of modern technologies and tools such as RESTful architecture, SDN, Kafka, RabbitMQ, Zookeeper in establishing an efficient framework for fog based IoT applications is discussed
18
Li, Zhiyi, Songtao Zhang, Zihan Fu, Fanlei Meng y Lijuan Zhang. "Confidence-Feature Fusion: A Novel Method for Fog Density Estimation in Object Detection Systems". Electronics 14, n.º 2 (7 de enero de 2025): 219. https://doi.org/10.3390/electronics14020219.
Texto completoResumen
Foggy weather poses significant challenges to outdoor computer vision tasks, such as object detection, by degrading image quality and reducing algorithm reliability. In this paper, we present a novel model for estimating fog density in outdoor scenes, aiming to enhance object detection performance under varying foggy conditions. Using a support vector machine (SVM) classification framework, the proposed model categorizes unknown images into distinct fog density levels based on both global and local fog-relevant features. Key features such as entropy, contrast, and dark channel information are extracted to quantify the effects of fog on image clarity and object visibility. Moreover, we introduce an innovative region selection method tailored to images without detectable objects, ensuring robust feature extraction. Evaluation on synthetic datasets with varying fog densities demonstrates a classification accuracy of 85.8%, surpassing existing methods in terms of correlation coefficients and robustness. Beyond accurate fog density estimation, this approach provides valuable insights into the impact of fog on object detection, contributing to safer navigation in foggy environments.
19
Alkayal, Entisar S., Nesreen M. Alharbi, Reem Alwashmi y Waleed Ali. "Improving fog resource utilization with a dynamic round-robin load balancing approach". International Journal of ADVANCED AND APPLIED SCIENCES 11, n.º 10 (octubre de 2024): 196–205. http://dx.doi.org/10.21833/ijaas.2024.10.022.
Texto completoResumen
In fog computing, load balancing is an important research problem. It focuses on efficiently assigning tasks to fog nodes and minimizing delay in real-time applications. The traditional round-robin algorithm assigns tasks in a rotating manner among fog nodes, but it can send tasks to the cloud too early, leading to increased delays. To solve this problem, this paper introduces an improved round-robin algorithm that takes a dynamic approach to balancing the use of fog resources. The new model aims to improve load balancing in fog computing by distributing tasks more evenly among fog nodes, reducing dependence on cloud computing, and making better use of fog resources. The improved algorithm helps fog computing systems run more efficiently, reduces delays in real-time applications, and lowers the costs associated with cloud use. The results show that the proposed load balancing algorithm is key to optimizing fog resource use, improving system efficiency, and reducing task completion times in distributed computing systems.
20
Canali, Claudia y Riccardo Lancellotti. "GASP: Genetic Algorithms for Service Placement in Fog Computing Systems". Algorithms 12, n.º 10 (21 de septiembre de 2019): 201. http://dx.doi.org/10.3390/a12100201.
Texto completoResumen
Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.
21
Mohd Pakhrudin, Nor Syazwani, Murizah Kassim y Azlina Idris. "A review on orchestration distributed systems for IoT smart services in fog computing". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 2 (1 de abril de 2021): 1812. http://dx.doi.org/10.11591/ijece.v11i2.pp1812-1822.
Texto completoResumen
This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda
22
Rodríguez-Azar, Paula Ivone, Jose Manuel Mejía-Muñoz, Oliverio Cruz-Mejía, Rafael Torres-Escobar y Lucero Verónica Ruelas López. "Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI". Sensors 24, n.º 1 (27 de diciembre de 2023): 149. http://dx.doi.org/10.3390/s24010149.
Texto completoResumen
Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.
23
Yazdani, Azita, Seyedeh Fatemeh Dashti y Yeganeh Safdari. "A fog-assisted information model based on priority queue and clinical decision support systems". Health Informatics Journal 29, n.º 1 (enero de 2023): 146045822311527. http://dx.doi.org/10.1177/14604582231152792.
Texto completoResumen
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.
24
Guan, Wei, Haolin Chen, Xuewei Li, Haijian Li y Xin You. "Study on the Influence of Connected Vehicle Fog Warning Systems on Driving Behavior and Safety". Journal of Advanced Transportation 2022 (30 de abril de 2022): 1–11. http://dx.doi.org/10.1155/2022/8436388.
Texto completoResumen
Driving speeds are higher on freeways, and the visibility reduction occurring in freeway fog zones often increases traffic accidents. This study aims at assessing the impact of different levels of connected vehicle fog warning systems on driving behavior and safety. A connected vehicle fog warning system is developed based on driving simulators, and virtual scenes are developed based on fog zones. The connected vehicle technology includes three levels: a normal level, a level including a human-machine interface, and a level with both a human-machine interface and dynamic message signs. Speed and lateral deviation are chosen as assessment indicators and combined with sample entropy to evaluate the impact of the connected vehicle fog warning system on safety. The deceleration ratio of the warning point is used to evaluate the efficiency of the connected vehicle fog warning system. Results show that the connected vehicle fog warning system can significantly reduce driving speed, and that there are differences in the speed-reduction effectiveness for different technical levels. The connected vehicle fog warning system can reduce the lateral deviation and improve the lateral driving safety. From the perspective of change stability, speed safety entropy and lateral deviation safety entropy are increased, which indicates that the connected vehicle fog warning system will negatively impact safety because of the additional workload. Drivers’ responses are more pronounced in the human-machine interface group compared to the group with dynamic message signs, where the drivers maintained a lower speed. This study provides a reference for the studies on connected vehicle technology based on driving simulators and supports the optimization, design, and evaluation of connected vehicle fog warning systems.
25
Zeghib, Nour El Imane, Ali A. Alwan, Abedallah Zaid Abualkishik y Yonis Gulzar. "Multi-Route Plan for Reliable Services in Fog-Based Healthcare Monitoring Systems". International Journal of Grid and High Performance Computing 14, n.º 1 (1 de enero de 2022): 1–20. http://dx.doi.org/10.4018/ijghpc.304908.
Texto completoResumen
The main concern of fog computing is reducing data transmission on the cloud. Moreover, due to the short distance between end-user and fog nodes, fog computing considered more reliable to handle time-sensitive situations like the critical data provided by the Internet of Things (IoT). This may include sensory healthcare data which needs rapid processing to make decisions. However, in healthcare monitoring systems it is necessary to ensure the services’ availability when fog node failure occurred. The issue of monitoring service interruption during fog node failure has not received much attention. This paper proposes a multi-route plan that aims to identify an alternative route to ensure the availability of time-critical medical services. Various scenarios have been designed to evaluate the performance of the proposed strategy. The experimental results illustrate the superiority of our approach in terms of latency, energy consumption, and network usage in comparison with most recent related work.
26
Yousefelahiyeh, Roya, Christopher Cyril Sandeep Dominic y Joel Ducoste. "Modeling fats, oil and grease deposit formation and accumulation in sewer collection systems". Journal of Hydroinformatics 19, n.º 3 (7 de marzo de 2017): 443–55. http://dx.doi.org/10.2166/hydro.2017.016.
Texto completoResumen
Fats, oil and grease (FOG) deposits in sewer systems are responsible for approximately 25% of all annual line blockages in the United States and lead to the release of 3–10 million gallons of untreated wastewater to the surrounding environment. Considerable effort has been made to maintain the conveyance of wastewater by cleaning pipes that have a significant FOG deposit accumulation. Changes in the urban landscape from the addition or deletion of food service establishments (FSEs), however, may pose challenges in determining the location of these high accumulation zones. This research focuses on the development of a sewer collection system model that predicts high FOG deposit accumulation zones. Two collection systems were simulated to test the prediction capabilities of the tool and to assess how changes in the urban landscape affect the location of these accumulation zones. The numerical tool incorporates a mechanistic-based FOG deposit formation kinetic model. Results showed that the model predicts about 65% of the reported high FOG deposit accumulating zones. Simulations also showed significant changes in the location of these high FOG deposit accumulating zones from changes in the number and locations of FSEs and from FSE discharge and background wastewater flow variations.
27
Guevara, Judy C. y Nelson L. S. da Fonseca. "Task scheduling in cloud-fog computing systems". Peer-to-Peer Networking and Applications 14, n.º 2 (18 de enero de 2021): 962–77. http://dx.doi.org/10.1007/s12083-020-01051-9.
Texto completo
28
Fu, Chao, Qing Lv y Reza G. Badrnejad. "Fog computing in health management processing systems". Kybernetes 49, n.º 12 (4 de enero de 2020): 2893–917. http://dx.doi.org/10.1108/k-09-2019-0621.
Texto completoResumen
Purpose Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works. Design/methodology/approach The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms. Findings The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing. Research limitations/implications This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English. Practical implications The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future. Originality/value Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.
29
Shiriaev, Egor, Tatiana Ermakova, Ekaterina Bezuglova, Maria A. Lapina y Mikhail Babenko. "Reliablity and Security for Fog Computing Systems". Information 15, n.º 6 (29 de mayo de 2024): 317. http://dx.doi.org/10.3390/info15060317.
Texto completoResumen
Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution for addressing the new computing requirements. However, there are still many challenges to overcome to utilize this new computing paradigm, in particular, reliability and security. Following this need, a systematic literature review was conducted to create a list of requirements. As a result, the following four key requirements were formulated: (1) low latency and response times; (2) scalability and resource management; (3) fault tolerance and redundancy; and (4) privacy and security. Low delay and response can be achieved through edge caching, edge real-time analyses and decision making, and mobile edge computing. Scalability and resource management can be enabled by edge federation, virtualization and containerization, and edge resource discovery and orchestration. Fault tolerance and redundancy can be enabled by backup and recovery mechanisms, data replication strategies, and disaster recovery plans, with a residual number system (RNS) being a promising solution. Data security and data privacy are manifested in strong authentication and authorization mechanisms, access control and authorization management, with fully homomorphic encryption (FHE) and the secret sharing system (SSS) being of particular interest.
30
Hornik, Jacob, Chezy Ofir, Matti Rachamim y Sergei Graguer. "Fog Computing-Based Smart Consumer Recommender Systems". Journal of Theoretical and Applied Electronic Commerce Research 19, n.º 1 (11 de marzo de 2024): 597–614. http://dx.doi.org/10.3390/jtaer19010032.
Texto completoResumen
The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.
31
Alakbarov, Rashid y Mammad Hashimov. "Fog computing technology application in cyber-physical systems and analysis of cybersecurity problems". Problems of Information Society 13, n.º 2 (5 de julio de 2022): 23–29. http://dx.doi.org/10.25045/jpis.v13.i2.03.
Texto completoResumen
New requirements for modern technologies have become a driving force in the development of information technology. New distributed computing systems are required to handle a large data flow generated by the application of the Internet of Things and to ensure their efficient processing. Although cloud computing is an effective technology for processing and storing data generated in a networked environment, it has complications with the real time transmission of large amounts of data due to the low bandwidth of network. To speed up the data processing, fog computing systems have been widely used in recent years. Fog counting systems are one of the proposed solutions for working with IoT devices. Because it can meet the computing needs of multiple devices connected to the network. In these systems, the data is processed at computing nodes located near the data generating devices, which reduces the bandwidth complications of the network channel. In this regard, this article considers the application of fog computing technology in cyber-physical systems. It analyzes the fog technology architecture and its advantages over cloud computing. Cyber security problems arising when using fog technology in cyber-physical systems are analyzed and available protection methods partially solving them are highlighted.
32
Babaryka, A., O. Basarab, А. Ploshchyk y S. Tabenskyi. "Choice of architecture of video surveillance systems of State Border Guard of Ukraine based on cloud and fog technologies". Communication, informatization and cybersecurity systems and technologies, n.º 5 (1 de junio de 2024): 5–14. http://dx.doi.org/10.58254/viti.5.2024.01.05.
Texto completoResumen
Modern video surveillance systems have evolved from closed-circuit television systems to the level of complex systems that operate as part of complex integrated systems and solve the tasks of not only recording events in the sectors of view of video surveillance cameras but also analysing the received video information. The increase in the amount of information circulating in modern systems requires the creation of new concepts. In recent years, the attention of scientists has been drawn to distributed information processing technologies. The concept of cloud computing is developing rapidly, and the basic ideas that were laid down in its construction can no longer solve the problems faced by the technology. This has led to the emergence of so-called post-cloud architectures that extend and complement the capabilities of cloud computing. These architectures include Mist, Edge, Fog, etc. The purpose of the article is to study video surveillance systems based on the concept of cloud and fog computing and criteria for assessing their effectiveness. The research was conducted using the methods of analysis and generalisation, modelling, and experimentation. This approach made it possible to conduct a comparative study of video surveillance systems built on two architectures. The results obtained indicate that fog computing technology has the advantage of reducing latency, minimising the need for repeated requests to the cloud by performing calculations at the cloud edge. In a model based on the cloud computing concept, services that use cloud resources lead to an increase in network load. At the same time, fog computing technology allows you to relieve the network load by performing part of the computation by fog nodes. The results of the experimental study show the advantages of fog computing for networks that are sensitive to delays. However, if we consider a video surveillance system with the main task of recording events in the sectors of view of CCTV cameras and the ability to view video information in real time, it is obvious that fog nodes will not provide long-term storage of video information, and delays will not be critical. In the case of a video surveillance system with video analytics functions, fog nodes will be able to perform part of the video analytics algorithms, thus unloading the cloud. Therefore, an urgent task is to study the effectiveness of building video surveillance systems with video analytics functions based on fog architecture.
33
Du, Pei, Zhe Zeng, Jingwei Zhang, Lu Liu, Jianchang Yang, Chuanping Qu, Li Jiang y Shanwei Liu. "Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China". Remote Sensing 13, n.º 17 (5 de septiembre de 2021): 3530. http://dx.doi.org/10.3390/rs13173530.
Texto completoResumen
Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons.
34
Lucier, Kayla y Manzoor Qadir. "Gender and Community Mainstreaming in Fog Water Collection Systems". Water 10, n.º 10 (19 de octubre de 2018): 1472. http://dx.doi.org/10.3390/w10101472.
Texto completoResumen
Fog water collection is an emerging opportunity to combat local water shortages in water-scarce areas where sustainable access to water is unreliable, but fog events are frequent. Since fog water systems are implemented within or near communities, they eliminate or decrease the need to travel far distances for the collection of water during times of scarcity. As a result, these systems decrease the physical and social burden of water collection on women and girls, who are the primary water gatherers in most traditional communities. This is an important outcome because women and girls are disproportionately affected by water scarcity and are not seen as equals in water management, access, or control. This paper illustrates how several fog water collection projects have shown, empirically, that the positive outcomes for women and girls may include the freeing of time for domestic and educational pursuits, improved health outcomes, and improved perceptions of self and others’ perceptions of women. These findings are important at a time when the world at large is addressing the Sustainable Development Agenda, where Sustainable Development Goal (SDG) 6 necessitates safe water and sanitation for all and SDG 5 ensures gender equality to empower all women and girls.
35
Diachok, Roman y Halyna Klym. "MODIFIED FOG-BASED TRUST METHOD OF DATA MONITORING FOR MULTI-SENSOR CONFIGURATION SYSTEMS". Measuring Equipment and Metrology 83, n.º 4 (2022): 47–55. http://dx.doi.org/10.23939/istcmtm2022.04.047.
Texto completoResumen
.A modified Fog-based trust method to prevent third-party interference in establishing trust relationships between sensors and cloud service providers in multi-sensor systems is considered. Trust in behavior between nodes is established at the level of wireless sensor networks; in the nodes and objects data at the Fog layer. With more detailed data analysis of the latter, it becomes possible to monitor the trust status of the entire network, detect data attacks and recover from misjudged nodes. Fog layer can be built as a reliable third party. Experiment results show that the proposed trust mechanism is inherent in advantage due to reducing energy consumption and ensuring the trust state of Edge nodes and whole the network as well as detecting hidden attacks on data and recovering nodes.
36
Adil Ali, Ali, Saadi Mohammed Saadi, Tameem Mohammed Mahmood y Salama A. Mostafa. "A smart water grid network for water supply management systems". Bulletin of Electrical Engineering and Informatics 11, n.º 3 (1 de junio de 2022): 1706–14. http://dx.doi.org/10.11591/eei.v11i3.3227.
Texto completoResumen
This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.
37
Moysiadis, Vasileios, Panagiotis Sarigiannidis y Ioannis Moscholios. "Towards Distributed Data Management in Fog Computing". Wireless Communications and Mobile Computing 2018 (2 de septiembre de 2018): 1–14. http://dx.doi.org/10.1155/2018/7597686.
Texto completoResumen
In the emerging area of the Internet of Things (IoT), the exponential growth of the number of smart devices leads to a growing need for efficient data storage mechanisms. Cloud Computing was an efficient solution so far to store and manipulate such huge amount of data. However, in the next years it is expected that Cloud Computing will be unable to handle the huge amount of the IoT devices efficiently due to bandwidth limitations. An arising technology which promises to overwhelm many drawbacks in large-scale networks in IoT is Fog Computing. Fog Computing provides high-quality Cloud services in the physical proximity of mobile users. Computational power and storage capacity could be offered from the Fog, with low latency and high bandwidth. This survey discusses the main features of Fog Computing, introduces representative simulators and tools, highlights the benefits of Fog Computing in line with the applications of large-scale IoT networks, and identifies various aspects of issues we may encounter when designing and implementing social IoT systems in the context of the Fog Computing paradigm. The rationale behind this work lies in the data storage discussion which is performed by taking into account the importance of storage capabilities in modern Fog Computing systems. In addition, we provide a comprehensive comparison among previously developed distributed data storage systems which consist of a promising solution for data storage allocation in Fog Computing.
38
Chao, Kuo-Ming y Jen-Yao Chung. "Edge and fog services". Service Oriented Computing and Applications 13, n.º 1 (marzo de 2019): 1–2. http://dx.doi.org/10.1007/s11761-019-00256-y.
Texto completo
39
Nieuwenhuis, Eva, Jeroen Langeveld y François Clemens. "The relationship between fat, oil and grease (FOG) deposits in building drainage systems and FOG disposal patterns". Water Science and Technology 77, n.º 10 (13 de abril de 2018): 2388–96. http://dx.doi.org/10.2166/wst.2018.173.
Texto completoResumen
Abstract Fat, oil and grease (FOG) deposits are an important contributor to blockages in building drainage systems. Such blockages bring undesirable financial, health and environmental costs, and thereby burden society. It is unclear to what extent the behavior of inhabitants, and more precisely domestic FOG disposal, affects the occurrence of FOG blockages. For this study, samples of FOG blockages were collected from building drainage systems (kitchen drains and lateral house connections) and analyzed. The results showed that the deposits were calcium salts of fatty acids. Dissimilarities between the network locations demonstrate that, even at short distance, in-sewer transformation processes occur. Surveys were conducted to reveal information about FOG disposal patterns. Three households showed a clear link between the type of cooking oils used and the type of deposits collected.
40
Price, Jeremy y Kristian Stokkereit. "The Use of Thermal Infra-Red Imagery to Elucidate the Dynamics and Processes Occurring in Fog". Atmosphere 11, n.º 3 (29 de febrero de 2020): 240. http://dx.doi.org/10.3390/atmos11030240.
Texto completoResumen
Improving our ability to predict fog accurately is currently a high priority for Numerical Weather Prediction models. Such an endeavour requires numerous types of observations of real fog as a means to both better understand it and also provide an assessment of model performance. We consider the use of thermal infra-red imagery, used in conjunction with other meteorological observations, for the purposes of studying fog. Two cameras were used—a FLIR Systems Inc. A655sc and a FLIR Systems Inc. A65sc—which were set up to capture one image per minute. Images were then combined to provide video footage of nocturnal fog events. Results show that the imagery from such cameras can provide great insight into fog processes and dynamics, identifying interesting features not previously seen. Furthermore, comparison of imagery with conventional meteorological observations showed that the observations were often not capable of being used to delineate all of the processes affecting fog, due to their incomplete and local nature.
41
Mühleisen, Wolfgang, Cristina Consani, Povilas Smaliukas, Markus Bainschab, Georg Brunnhofer y Andreas Tortschanoff. "Creation of a Laboratory Fog Chamber for Testing Optical Sensors". European Journal of Engineering Science and Technology 7, n.º 2 (6 de enero de 2025): 31–43. https://doi.org/10.33422/ejest.v7i2.1427.
Texto completoResumen
LIDAR sensors and systems are gaining popularity, but their effectiveness can be compromised by light reflections in foggy conditions. To address the need for small, laboratory-scale testing systems, a fog chamber was developed and tested to have a laboratory environment. Two different methods to produce fog were evaluated: hot steam and ultrasonic nebulizers. The cold fog from ultrasonic nebulizers was more prone to stratification, while the hot fog from hot steam produced rather disturbing condensation for the measurement. All this was solved by implementing improving aids such as a ventilation and windscreen wiper system. First to mitigate the fog stratification with help of circulation and second to prevent condensation with help of wiping the windows in the measurement path. Ultimately, the ultrasonic nebulizers showed their strengths in the experiment due to the lower influence of the enhancement aids on the signal quality, which is why they are recommended as a fog source.
42
Kalyashina, Anna, Yuri Smirnov, Valeriy Esov, Maxim Kuznetsov y Oksana Dmitrieva. "Enhancing IoT systems through Cloud-Fog-Edge architectures challenges and opportunities". E3S Web of Conferences 583 (2024): 06012. http://dx.doi.org/10.1051/e3sconf/202458306012.
Texto completoResumen
This article examines the deployment and implications of Cloud- Fog-Edge architectures in Internet of Things (IoT) systems, highlighting their significance in enhancing data management and system security across diverse sectors. As IoT ecosystems expand, the necessity for architectures that efficiently handle large volumes of data and ensure real-time processing capabilities becomes paramount. The Cloud-Fog-Edge architecture addresses these needs by distributing computing resources across three layers—cloud, fog, and edge—each optimized for specific tasks within the IoT workflow. We discuss the challenges and solutions associated with interoperability in such multi-layered systems, emphasizing the need for standardized communication protocols and data formats to facilitate seamless interactions between heterogeneous devices and platforms. Furthermore, the article delves into the critical aspects of security within these architectures, outlining strategies for robust data encryption, access management, regular security updates, and comprehensive network activity monitoring to safeguard against unauthorized access and cyber threats. The integration of Cloud-Fog-Edge architectures not only promises enhanced operational efficiency and scalability but also significantly boosts the adaptability of IoT systems to meet evolving technological and operational demands. By providing a detailed analysis of the functionalities, integration challenges, and security practices associated with each architectural layer, this article contributes to a deeper understanding of how Cloud-Fog-Edge frameworks can be optimized to bolster the reliability, efficiency, and security of modern IoT environments.
43
Junejo, Aisha Kanwal y Nikos Komninos. "A Lightweight Attribute-Based Security Scheme for Fog-Enabled Cyber Physical Systems". Wireless Communications and Mobile Computing 2020 (21 de septiembre de 2020): 1–18. http://dx.doi.org/10.1155/2020/2145829.
Texto completoResumen
In this paper, a lightweight attribute-based security scheme based on elliptic curve cryptography (ECC) is proposed for fog-enabled cyber physical systems (Fog-CPS). A novel aspect of the proposed scheme is that the communication between Fog-CPS entities is secure even when the certification authority (CA) is compromised. This is achieved by dividing the attributes into two sets, namely, secret and shared, and subsequently generating two key pairs, referred to as the partial and final key pairs, for each entity of the Fog-CPS system. Unlike existing attribute-based encryption (ABE) and identity-based encryption schemes, in the proposed scheme, each entity calculates the final public key of the communicating CPS devices without the need of generating and transmitting digital certificates. Moreover, the proposed security scheme considers an efficient and secure key pair update approach in which the calculation overhead is limited to one group element. To show the effectiveness of the proposed scheme, we have calculated and compared the memory and processing complexity with other bilinear and elliptic curve schemes. We have also implemented our scheme in a Raspberry Pi (3B+ model) for CPS simulations. The proposed scheme guarantees the confidentiality, integrity, privacy, and authenticity in Fog-CPS systems.
44
Rana, Minahil, Khalid Mahmood, Muhammad Asad Saleem, Fadi Al-Turjman, Manjur Sayyadbadasha Kolhar y Chadi Altrjman. "Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems". Applied Sciences 13, n.º 3 (20 de enero de 2023): 1424. http://dx.doi.org/10.3390/app13031424.
Texto completoResumen
The emergence of fog-based Internet of Things (IoT) systems have played a significant role in enhancing the applicability of the IoT paradigm. In such systems, fog-nodes are proficient enough to retain, process and transmit the data coming from IoT devices. Nevertheless, as an extension of cloud computing, inheriting the security and privacy concerns of cloud computing is also inevitable in fog-based IoT systems. To deal with such challenges, a diverse range of security solutions are reported in the literature. However, most of them have several limitations (i.e., vulnerability to known security attacks and high computation overhead) that curtail their practical implementation applicability. Keeping these limitations in mind, this paper propose a privacy-preserving hash-based authenticated key agreement protocol using XOR and concatenation operations for fog-driven IoT systems. Using healthcare as a case study, the security of the novel protocol is evaluated by using informal and formal security analysis. In order to obtain the experimental results, the key cryptographic operations used at the user, fog node and cloud server-side are implemented on a mobile device, Arduino and cloud server, respectively. Findings from the performance evaluation results show that the proposed protocol has the least computation cost compared to several related competing protocols.
45
Powell, Colton, Christopher Desiniotis y Behnam Dezfouli. "The Fog Development Kit: A Platform for the Development and Management of Fog Systems". IEEE Internet of Things Journal 7, n.º 4 (abril de 2020): 3198–213. http://dx.doi.org/10.1109/jiot.2020.2966405.
Texto completo
46
Mouradian, Carla, Somayeh Kianpisheh, Mohammad Abu-Lebdeh, Fereshteh Ebrahimnezhad, Narjes Tahghigh Jahromi y Roch H. Glitho. "Application Component Placement in NFV-Based Hybrid Cloud/Fog Systems With Mobile Fog Nodes". IEEE Journal on Selected Areas in Communications 37, n.º 5 (mayo de 2019): 1130–43. http://dx.doi.org/10.1109/jsac.2019.2906790.
Texto completo
47
Wang, Junliang, Peng Zheng, Youlong Lv, Jingsong Bao y Jie Zhang. "Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems". Engineering 5, n.º 4 (agosto de 2019): 662–70. http://dx.doi.org/10.1016/j.eng.2018.12.013.
Texto completo
48
Gudla, Surya Pavan Kumar, Sourav Kumar Bhoi, Soumya Ranjan Nayak, Krishna Kant Singh, Amit Verma y Ivan Izonin. "A Deep Intelligent Attack Detection Framework for Fog-Based IoT Systems". Computational Intelligence and Neuroscience 2022 (22 de diciembre de 2022): 1–25. http://dx.doi.org/10.1155/2022/6967938.
Texto completoResumen
Fog computing provides a multitude of end-based IoT system services. End IoT devices exchange information with fog nodes and the cloud to handle client undertakings. During the process of data collection between the layer of fog and the cloud, there are more chances of crucial attacks or assaults like DDoS and many more security attacks being compromised by IoT end devices. These network (NW) threats must be spotted early. Deep learning (DL) assumes an unmistakable part in foreseeing the end client behavior by extricating highlights and grouping the foe in the network. Yet, because of IoT devices’ compelled nature in calculation and storage spaces, DL cannot be managed on those. Here, a framework for fog-based attack detection is proffered, and different attacks are prognosticated utilizing long short-term memory (LSTM). The end IoT gadget behaviour can be prognosticated by installing a trained LSTMDL model at the fog node computation module. The simulations are performed using Python by comparing LSTMDL model with deep neural multilayer perceptron (DNMLP), bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), hybrid ensemble model (HEM), and hybrid deep learning model (CNN + LSTM) comprising convolutional neural network (CNN) and LSTM on DDoS-SDN (Mendeley Dataset), NSLKDD, UNSW-NB15, and IoTID20 datasets. To evaluate the performance of the binary classifier, metrics like accuracy, precision, recall, f1-score, and ROC-AUC curves are considered on these datasets. The LSTMDL model shows outperforming nature in binary classification with 99.70%, 99.12%, 94.11%, and 99.88% performance accuracies on experimentation with respective datasets. The network simulation further shows how different DL models present fog layer communication behaviour detection time (CBDT). DNMLP detects communication behaviour (CB) faster than other models, but LSTMDL predicts assaults better.
49
Naik, K. Jairam y D. Hanumanth Naik. "Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing". Scalable Computing: Practice and Experience 21, n.º 1 (19 de marzo de 2020): 73–84. http://dx.doi.org/10.12694/scpe.v21i1.1616.
Texto completoResumen
Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than somecommon algorithms such as active monitoring, weighted round robin and throttled load balancer.
50
Calsavara, Felipe, Felipe Issa Kabbach y Ana Paula C. Larocca. "Effects of Fog in a Brazilian Road Segment Analyzed by a Driving Simulator for Sustainable Transport: Drivers’ Speed Profile under In-Vehicle Warning Systems". Sustainability 13, n.º 19 (22 de septiembre de 2021): 10501. http://dx.doi.org/10.3390/su131910501.
Texto completoResumen
Intelligent transport systems enable vehicles to communicate with each other and with the environment, ensuring road safety. Their implementation can help reduce the number of accidents, especially in stretches of s-curves, where speed control is essential to ensure the safety of drivers, and under hazardous weather conditions. Such systems promptly notify drivers about potentially dangerous road conditions, such as fog, so that they can better adapt their driving behavior. This study evaluates the driver’s speed profile in different scenarios (clear weather, fog weather, and fog with an in-vehicle fog warning system) considering the road geometry elements (s-curves). A driving simulator recreated the real scenarios of a principal Brazilian road segment, showing the geometric and weather conditions of a road known for its several s-curves and frequent incidence of fog. A preliminary study identified the most critical curves through a weighted severity index methodology to define the critical segment. The results showed drivers considerably reduced their speed in the scenario with a warning system, thus contributing to the safety of s-curved segments. The implementation of in-vehicle warning systems can avoid or reduce the need for major infrastructure interventions such as geometric design, through investments in new intelligent transport systems.