Fog Level Research Articles

A three-tier approach to Internet of Things software architecture design

The Internet of Things (IoT) rapidly evolves, presenting challenges and opportunities. This study emphasises the critical role of software in advancing IoT technologies, focusing on machine learning (ML), fog computing, and process optimisation for security and resiliency. ML is pivotal in IoT for predicting equipment failures, evaluating process efficiency, and enabling informed decision- making through real-time data analysis. By integrating ML models directly into IoT devices (edge computing), latency is reduced, and data transmission needs are minimised. Fog computing and cloud computing address latency issues by moving computational resources closer to IoT devices, enhancing scalability and optimising network resource usage. Security remains a paramount concern due to the increasing number of connected devices and their vulnerabilities. IoT software must balance efficiency, security, and performance, employing deep learning for anomaly detection, blockchain for data transparency, and optimised encryption protocols. The trend towards distributed architectures like Edge Computing enhances system resilience by reducing latency and increasing fault tolerance. The proposed IoT system architecture is a three-tier structure consisting of Edge, Fog, and Cloud levels. At the Edge level, initial data processing occurs directly on IoT devices, reducing latency and network load. The Fog level processes data within the local network, utilising more powerful computational resources for complex tasks and ensuring security through advanced machine learning and encryption. The Cloud level serves as a central repository for long-term storage and global data analysis, leveraging containerisation and orchestration technologies for scalability and reliability. This multi-layered architecture ensures efficient data processing, high security, and adaptability, making it suitable for real-time applications. The study highlights the importance of software in optimising data processing across these levels, ensuring the IoT system’s resilience, scalability, and long-term sustainability.

Experimental investigations on an FSO-fiber converged communication system under fog and turbulence

This paper presents experimental results for a free-space optical-fiber converged (FSO-FC) communication system under varying turbulence and foggy conditions. Based on the experimental measurements, we statistically characterize the FSO channel under different levels of turbulence and fog in terms of their respective probability density functions (PDFs). Our experimental PDFs fit well with the theoretical PDFs proposed in the literature. We experimentally evaluate the average bit error rate (ABER) of the considered FSO-FC communication system under different levels of turbulence and fog. Further, to compensate for the effects of turbulence and fog, we use multimode fiber as the receiver and compare the results with single-mode fiber as the receiver. Finally, we show the improvement in ABER under varying turbulence and fog by adding cyclic redundancy check bits to data bits.

Implementation of forward error correction for improved performance of free space optical communication channel in adverse atmospheric conditions

The tremendous speed and security that Free Space Optical Communication (FSOC) technology provides have led to its rapid expansion. This opens up a plethora of possibilities for terrestrial communication with small ranges of up to a few kilometers, such as multi-campus and building-to-building communication. However, the ever-varying nature of atmospheric channels poses a major challenge degrading the optical signal strength. Several constituents of atmospheric channels like fog, dust, smoke, rain, and wind turbulence will influence the performance of an FSOC channel. In this paper, we present a system model focusing on adverse atmospheric channels, primarily by replicating varying fog conditions. Additionally, we showcase the successful real-time implementation of diverse forward error correction (FEC) codes in adverse atmospheric conditions, specifically varying levels of fog, using a dedicated test bed. Our experiments demonstrate the capability to recover erroneous data up to 50% Bit Error Rates (BER). Furthermore, we delve into the selection of suitable FEC codes tailored to different fog conditions, aiming to optimize time efficiency with the encoded bitrate.

Enhancing modular application placement in a hierarchical fog computing: A latency and communication cost-sensitive approach

Over the years, cloud computing has been a key enabler for handling complex applications and services for Internet of Things (IoT) devices situated at the edge of the network. Services and applications that are driven by the IoT environment commonly have stringent latency-sensitive requirements and may experience long network delays due to the long physical distance of cloud-based computational resources from IoT devices. Fog computing gained adoption as a solution in this case because it shortens this distance by spreading the computing power around the edge of the network in tiers. This contributes to network latency reduction and response times improvements of applications that have sensitive temporal requirements, besides improving the overall data traffic management in the network. Nevertheless, when certain requirements of an application are prioritized over others during the resource allocation process, a fog tier closer to IoT devices may experience resource depletion, forcing other latency-sensitive applications to use resources from a distant fog level and causing them to become non-responsive. To address this issue, this work proposes an approach for allocating modular applications in a hierarchical tier-based fog computing architecture. The proposed approach, named Least Impact - X (LI-X), aims to minimize the response time of latency-sensitive applications and reduce data traffic on the network by mitigating the idle time of resources at the lower levels of the hierarchical fog. This is achieved by distributing the application modules among the fog tiers in order to minimize the response time of delay-sensitive applications, while also reducing the overall network traffic. The performance of LI-X was compared to previous studies in a simulated iFogSim environment. Results have demonstrated that LI-X outperforms these studies in most of the proposed scenarios, effectively reducing response time and minimizing communication data costs on the network.

Neuropathic post-COVID pain symptomatology is not associated with serological biomarkers at hospital admission and hospitalization treatment in COVID-19 survivors

ObjectiveEvidence suggests that individuals who had survived to coronavirus disease, 2019 (COVID-19) could develop neuropathic post-COVID pain. This study investigated the association of serological biomarkers and treatments received during hospitalization with development of neuropathic-associated symptoms.MethodsOne hundred and eighty-three (n = 183) previously hospitalized COVID-19 survivors during the first wave of the pandemic were assessed in a face-to-face interview 9.4 months after hospitalization. Nineteen serological biomarkers, hospitalization data, and treatment during hospitalization were obtained from medical records. Neuropathic pain symptoms (Self-Report Leeds Assessment of Neuropathic Scale), sleep quality (Pittsburgh Sleep Quality Index), pain catastrophizing (Pain Catastrophizing Scale) and anxiety/depressive levels (Hospital Anxiety and Depression Scale) were assessed.ResultsThe prevalence of post-COVID pain was 40.9% (n = 75). Fifteen (20%) patients reported neuropathic symptoms. Overall, no differences in hospitalization data and serological biomarkers were identified according to the presence or not of neuropathic-associated symptoms. Patients with post-COVID pain had the highest neutrophil count, and post hoc analysis revealed that patients with neuropathic post-COVID associated symptoms had lower neutrophil count (p = 0.04) compared with those without neuropathic pain, but differences were small and possible not clinically relevant. No differences in fatigue, dyspnea, brain fog, anxiety or depressive levels, poor sleep, or pain catastrophism between patients with and without neuropathic symptoms were found.ConclusionIt seems that neuropathic-like post-COVID pain symptoms are not associated with neither of assessed serological biomarkers at hospital admission nor hospitalization treatments received in this cohort of hospitalized COVID-19 survivors.

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

A futuristic green service computing approach for smart city: A fog layered intelligent service management model for smart transport system

With the widespread adoption of technologies like the Internet of Things (IoT), context awareness, and decentralized computing at the edge of the network, service delivery in smart city parlance has been rapidly evolved. Management of Information and Communication Technologies (ICT) infrastructure for such dynamic environment at scale brings about new challenges. Existing fog layer resource management involves context-sharing and migration for real-time vertical and cross-vertical services. However, improper context migration might affect performance negatively. In this paper, the authors have envisioned improving the Quality of Service (QoS) of smart transportation while employing context-aware computing and Artificial Intelligence, which helps alleviate massive data transfers among Fog nodes and intelligent vehicles in real-time. The proposed Context-Aware Intelligent Transportation System (CAITS) manages the services of intelligent vehicles and manages the road traffic of traditional vehicles in an effective manner, with a three-layered learning model that accounts for on-vehicle, on-co-vehicle, and on-fog-and-vehicle learning by means of a platoon control algorithm as well as federated learning at the Fog level. Simulations are carried out on CloudSim simulator under different scenarios and the results demonstrate that the proposed scheme improves prediction efficacy of contexts at the Fog layer by approximately 8%–24% than existing models which in turn reflects in reduced service time and energy consumption of EVs and reduces the CO2 of environment.

IoT Enabled Sensory Monitoring System for Fog Optimal Resource Provisioning Method in Health Monitoring System

Fog is data management and analytics service. In this paper gains and most effective novel approach to provide IoT enabled services in healthcare application using Fog Computing. In this research the data is collected from Google Scholar, Science Director and MEDLINE database. IoT based Fog Computing techniques are proposed for delivering quality of services to the user. Optimal Resource Provisioning method is proposed to find edges, service level agreements and administration services for IoT client. The DeepQ residue information processing technique is applied for connecting data centre of the cloud and computing paradigms technique is finding the depth reference of Fog levels. The proposed Optimal resource provisioning algorithm is examining the dataset and TensorFlow tool is used for simulating environment. Fog computing layer consist of IoT sensor data inputs, data centres for the cloud and connected layers for simulations. The Deep belief network is generated based on above inputs using 256 X 256 X 3 layer system and 5000 trained data, 1000 test data are taken for simulations. Each dataset simulation is recording using supervised and unsupervised learning methods. Based on above results IoT enable Fog Computing data management and analytics systems provided 95% accuracy and the compared with existing computing techniques our proposed systems shows better efficiency with respect to safety and convenience.

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

The fog density level, as one of the indicators of weather conditions, will affect the management decisions of transportation management agencies. This paper proposes an image-based method to estimate fog density levels to improve the accuracy and efficiency of analyzing fine meteorological conditions and validating fog density predictions. The method involves two types of image entropy: a two-dimensional directional entropy derived from four-direction Sobel operators, and a combined entropy that integrates the image directional entropy and grayscale entropy. For evaluating the performance of the proposed method, an image test set and an image training set are constructed; and each image is labeled as heavy fog, moderate fog, light fog, or fog-free according to the fog density level of the image based on a user study. Using our method, the average accuracy rates of image fog level estimation were 77.27% and 79.39% on the training set using the five-fold cross-validation and the test set, respectively. Our experimental results demonstrate the effectiveness of the proposed combined entropy for image-based fog density level estimation.

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group the data from each stream into homogeneous clusters at the clustering fog level. Each cluster in the first data stream is stored in the clusters processing fog level and indexed directly in the indexing fog level in a Binary tree with Hyperplane (BH tree). The indexing of clusters in the subsequent data stream is determined by the coefficient of variation (CV) value of the union of the new cluster with the existing clusters in the cluster processing fog layer. An analysis and comparison of our experimental results with other results in the literature demonstrated the effectiveness of the CV method in reducing energy consumption during BH tree construction, as well as reducing the search time and energy consumption during a k Nearest Neighbor (kNN) parallel query search.

An energy efficient fog level resource management scheme for software defined cities

The proliferation of Internet of Things (IoT) and smart city services have paved the path for complex network architectures including edge and fog layer resources in order to meet such services’ Quality of Service (QoS). Such services should also be energy efficient for sustainable cities. Though energy efficiency has been widely studied over the past decade, yet most such solutions have focused on device or sensor level solutions or alternatively on cloud/datacenter level solutions. With the prominence of Software Defined Networks (SDNs) in recent times owing to its flexible and scalable network management features, SDN based fog level resource management has become an attractive alternative. This paper addresses fog level energy-aware resource management with the goal of minimizing energy consumption while QoS fulfillment by means of Virtual Machine (VM) consolidation through live migrations. Since any decent sized smart city would have millions of devices and large numbers of edge/fog instances, it would lead any solution mechanism to a NP hard problem. This paper proposes a novel set based Differential Evolutionary (S-DE) algorithm. Simulations have been carried out to test the efficacy of the proposed S-DE solution and the results demonstrate the efficiency of the proposed method.

A Scale Development Study: Brain Fog Scale.

This research was conducted to objectively evaluate the level of brain fog that may develop due to many reasons. This was a methodological study. This study was conducted in Turkey. Content validity ratio, EFA, CFA, Kaiser-Meyer-Olkin analysis and Bartlett's test of sphericity, Item analysis, Cronbach's alpha coefficient, Spearman-Brown, Guttman Analysis and test-retest correlations validity-reliability analysis were performed. The statistical meaningfulness level in all tests was determined as p<0.05. As a result of context validity, factor analysis and item analysis, a 30 item scale with 3 subscale was obtained. The variance amount explained by the 3 subscale was on a very good level (77.43%). The fact that all of the Cronbach alpha, Spearman-Brown and Guttman internal consistency coefficients of the scale and all of its subscale are above 0.70. When the test retest reliability coefficients of the scale was examined, the scale was found to present consistent results in different applications and the scale was found to be reliable with regard to the constancy coefficient. The Brain Fog Scale consists of 30 items and 3 subscales. It is a valid and reliable instrument.

Efficient employment of optical sources integrated with both light detectors and free space communication modulated channel under ambient conditions effects

Abstract The paper simulates the efficient employment of light sources integrated with both light detectors and free space communication channel under ambient condition effects. The modified duobinary non return to zero line coding system based optical wireless communication channel under light or dense fog level effects. Max. Q factor against OWC reach is simulated at light and dense fog level for the suggested with the previous model. The study emphasis that the OWC length has been reached to 65 km at light fog level and the OWC length has been reached to 5 km at dense Fog level with acceptable signal power. The total light power and total electrical power are illustrated at light and dense fog levels. Max. Q factor and min. BER after receiver based OWC are clarified at light and dense fog levels.

Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System

This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships.

Industry 4.0 for Aerospace Manufacturing: Condition Based Maintenance Methodology, Implementation and Challenges

&#x0D; Industry 4.0 is the fourth industrial revolution where machine operations are linked through data communications and together, they form a production ecosystem. In Industry 4.0 settings, these operations are monitored, recorded and analyzed. This can be performed at Edge, Fog or Cloud levels.&#x0D; &#x0D; &#x0D; In the industrial big data era, with ever maturing sensor technologies, data capture, communication and storage technologies, utilizing machine data for operational insights provides companies with competitive advantages. Benefits can include reduced operational and maintenance costs, a decrease in unscheduled downtime and greater assurance of on-time delivery of products. In this work, we will cover the milestones of implementing an industry 4.0 condition-based maintenance (CBM) strategy for machine tools and their surrounding systems. In addition, we will discuss the methodology for sensor selection, data collection, transmission and storage, return on investment for CBM and building CBM models for detection. Finally, will delve into challenges of implementing this methodology in industrial settings from both technology and logistics aspects.&#x0D;

Data science and machine learning in the IIoT concepts of power plants

Smart power plants are no longer just futuristic ideas but are rapidly becoming a reality, and for this to be achievable, an application of artificial intelligence in Industrial Internet of Things (IIoT) concepts is necessary. This paper presents the place and role of Data Science (DS) and Machine Learning (ML) on edge, fog, and cloud levels of vertical IIoT concepts of power plants. A comprehensive functional analysis of edge, fog, and cloud levels has been done. Data analyzing, preprocessing, and processing are described on all levels separately. Limitations in signal conversion and data preprocessing at the edge level (edge computing), data processing and analysis at the fog level (fog computing), and information processing at the application and cloud levels (high-end and cloud computing) are described in detail. ML algorithms have been selected based on the particular management and control level. The proposed concepts represent management and maintenance improvements with minimal investment and avoidance of production downtime.

RAMi: A New Real-Time Internet of Medical Things Architecture for Elderly Patient Monitoring

The aging of the world’s population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT), i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of the domains of IoT where real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture includes a Things layer where data are retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to address the specific problems of IoMT. Data are processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly persons and patients during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion.

Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance.

Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.

A new method for indexing continuous IoT data flows in metric space

The continuous production of heterogeneous internet of things (IoT) data created a new challenge, which is their storage and retrieve efficiently. In this work, a new method of indexing, called threshold distance (TD), is proposed in the fog‐cloud architecture. In this method, the fog layer is divided into two levels: a clustering level and an indexing level. In the clustering fog level, the first data flow is grouped into clusters of homogenous objects using the density‐based spatial clustering applications with noise (DBSCAN) algorithm. In the indexing fog level, objects in each cluster are indexed in separated generalized hyperplane‐trees (GHT). After the clustering of the next data flow, objects are inserted in existing GHT or new GHT are created according the comparison of the distances between centers of the next clusters and those of the first clusters (took as representatives of their corresponding indexes) with a threshold distance value. To test the efficiency of the TD method, the experimental results were compared with those of our second proposed method called creation of a new tree (CNT) in which, data of each cluster is indexed in a new GHT. The experimental results show that the efficiency of the TD method in GHT construction depend on the size of the data flow. For the parallel kNN search, the TD method proved its efficiency whatever the size of the data flow.