Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing

2019 ◽  
Vol 2 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Rajni Gupta
Keyword(s):  
Oil And Gas ◽  
Health Care Systems ◽  
Fog Computing ◽  
Fast Response ◽  
Resource Provisioning ◽  
Time Data ◽  
Computing Paradigm ◽  
Care Systems ◽  
Cloud Environments ◽  
Suitable Technique

Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.

scholarly journals Living Within and Without: Life-Space Mapping to Visualize Need and Resource Access of Rural Dementia Dyads

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 434-435
Author(s):  
Julia Loup ◽  
A Lynn Snow ◽  
Michelle Hilgeman
Keyword(s):  
Visual Cues ◽  
Daily Life ◽  
Health Care Systems ◽  
Qualitative Interviews ◽  
Time Data ◽  
Life Space ◽  
Resource Access ◽  
Space Modeling ◽  
Care Systems ◽  
Moca Score

Abstract Rural-dwelling veterans with dementia (PWD) and their family caregivers (CG) have unique needs and resource access limitations. Life-Space assessment models suggest older adults’ needs are reflected in their daily-life mobility and routines (Peel et al., 2005). Yet, medical treatment models seldom incorporate non-health related activities (e.g., transportation, groceries, distance to formal and informal support networks). This mixed-methods study proposes an exploratory life-space modeling visualization that integrates qualitative and quantitative daily-life data from rural dwelling dyads in Alabama. Two case studies are selected from a sample of 30 qualitative interviews to demonstrate this innovative analytic approach. One case depicts a married dyad (PWD and spousal CG) (CGage = 74; PWDage = 80, PWD MoCA score = 21) and the second visualization is of a PWD living alone (PWDage = 82, PWD MoCA Score = 20). Daily-life experiences and routines mentioned during interviews were categorized using a rapid analysis template approach and informed by unmet needs theories (Algase et al., 1996). Next, extracted data were placed into mapping visualization software. The maps include visual cues (colors, transportation routes, and icons) to designate met, unmet, and vulnerable needs and resources, allowing visual interaction with the two cases’ dementia caregiving context and qualitative responses. Life-space maps may be useful tools to visualize resource access and assist integrated health care systems in better understanding daily interactions and intervention gaps for difficult to reach populations. Future developments include ecological momentary assessment and Global Positioning System (GPS) data to develop life-space maps using real-time data collection.

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

An Overloading State Computation and Load Sharing Mechanism in Fog Computing

2021 ◽  
Vol 14 (4) ◽  
pp. 94-106
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Data Storage ◽  
Fog Computing ◽  
Load Sharing ◽  
Resource Provisioning ◽  
Time Data ◽  
High Demand ◽  
Node Failure ◽  
Proposed Model ◽  
Real Time Data ◽  
Efficient Resource

Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.

scholarly journals On the predictability of COVID-19 in USA: A Google Trends analysis

2020 ◽  
Author(s):  
Amaryllis Mavragani ◽  
Konstantinos Gillas
Keyword(s):  
Health Care Systems ◽  
Health Resources ◽  
State Level ◽  
Google Trends ◽  
Time Data ◽  
Policy Makers ◽  
The Us ◽  
Care Systems ◽  
Monitoring And Forecasting ◽  
Difficult Times

Abstract During the difficult times that the world is facing due to the COVID-19 pandemic that has already had severe consequences in all aspects of our lives, it is imperative to explore novel approaches of monitoring and forecasting the regional outbreaks as they happen or even before they do. In this paper, the first approach of exploring the role of Google query data in the predictability of COVID-19 in the US at both national and state level is presented. The results indicate that Google Trends correlate with COVID-19 data, while the estimated models exhibit strong predictability of COVID-19. In line with previous work that has argued on the value of online real-time data in the monitoring and forecasting epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers, in order to address the most crucial issue; that of flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of the respective health care systems.

scholarly journals Cómputo en la niebla aplicado a la manufactura inteligente bajo el contexto de la industria 4.0: Desafíos y oportunidades

2019 ◽  
pp. 16-27
Author(s):  
Mariela Juana Alonso-Calpeño ◽  
Julieta Santander-Castillo ◽  
Yuridia Ramírez-Chocolatl ◽  
Raúl Alanis-Teutle
Keyword(s):  
Data Processing ◽  
Vertical Integration ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Time Data ◽  
Industrial Systems ◽  
Computing Paradigm ◽  
Industrial Internet ◽  
Level Data ◽  
Real Time Data Processing

Cloud computing offers high server-level data processing capacity, while fog computing works using nodes at the edge of the network, enabling real-time data processing with low latency and improved ubiquity, so it can contribute on Industrial Internet of Things (IIoT) applications. This article discusses the technical challenges that have arisen in implementing the IIoT, and how the fog computing paradigm is helping to solve some of them. For this, a review of scientific articles in the Google Scholar and Web of Science databases has been carried out using keywords. The results show that there are various challenges related to interoperability, mixed criticality, latency, fault tolerance, scalability, horizontal and vertical integration, functional safety, legacy industrial systems, and energy efficiency. The main trends to face these challenges are reported. This article proposes a series of opportunity areas for research and development of possible solutions.

160: Cost-Effectiveness of Medical Management Strategies of Nephrolithiasis in Different Health Care Systems

2004 ◽  
Vol 171 (4S) ◽  
pp. 42-43 ◽  
Author(s):  
Yair Latan ◽  
David M. Wilhelm ◽  
David A. Duchene ◽  
Margaret S. Pearle
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Medical Management ◽  
Health Care Systems ◽  
Management Strategies ◽  
Care Systems
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