scholarly journals A Panorama of Cloud Platforms for IoT Applications Across Industries

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2701
Author(s):  
Sami Yangui
Keyword(s):  
Fog Computing ◽  
Relevant Literature ◽  
Critical Role ◽  
Critical Research ◽  
Research Directions ◽  
Platform As A Service ◽  
Iot Applications ◽  
Industrial Iot ◽  
Future Challenges ◽  
Cloud Environments

Internet of Things (IoT) applications can play a critical role in business and industry. Industrial IoT (IIoT) refers to the use of IoT technologies in manufacturing. Enabling IIoT applications in cloud environments requires the design of appropriate IIoT Platform as-a-Service (IIoT PaaS) to support and ease their provisioning (i.e., development, deployment and management). This paper critically reviews the IIoT PaaS architectures proposed so far in the relevant literature. It only surveys the architectures that are suitable for IIoT applications provisioning and it excludes regular IoT solutions from its scope. The evaluation is based on a set of well-defined architectural requirements. It also introduces and discusses the future challenges and the research directions. The critical review discusses the PaaS solutions that focus on the whole spectrum of IoT verticals and also the ones dealing with specific IoT verticals. Existing limitations are identified and hints are provided on how to tackle them. As critical research directions, the mechanisms that enable the secure provisioning, and IIoT PaaS interaction with virtualized IoT Infrastructure as-a-Service (IaaS) and fog computing layer are discussed.

scholarly journals Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

2021 ◽  
Author(s):  
Vu Khanh Quy ◽  
Nguyen Van Hau ◽  
Dang Van Anh ◽  
Le Anh Ngoc
Keyword(s):  
Fog Computing ◽  
Healthcare Applications ◽  
Cloud Technology ◽  
Platform As A Service ◽  
Iot Applications ◽  
Smart Healthcare ◽  
Computing Technologies ◽  
Patient Status ◽  
Architectural Framework ◽  
Emergency Scenarios

AbstractThe history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals The FORA Fog Computing Platform for Industrial IoT

2021 ◽  
Vol 98 ◽  
pp. 101727
Author(s):  
Paul Pop ◽  
Bahram Zarrin ◽  
Mohammadreza Barzegaran ◽  
Stefan Schulte ◽  
Sasikumar Punnekkat ◽  
...  
Keyword(s):  
Fog Computing ◽  
Computing Platform ◽  
Industrial Iot

HR strategy during culture change: Building change agency

2015 ◽  
Vol 21 (6) ◽  
pp. 741-754 ◽  
Author(s):  
Llandis Barratt-Pugh ◽  
Susanne Bahn
Keyword(s):  
Cultural Change ◽  
Culture Change ◽  
Change Process ◽  
Relevant Literature ◽  
Critical Role ◽  
Research Process ◽  
Change Agency ◽  
Strategic Approach ◽  
Organisational Values

AbstractThis paper explores the role played by a Human Resources (HR) department orchestrating culture change during the merger of two large State departments with dissimilar cultures. A 2-year case study determined what HR strategies were having the greatest impact on embedding new organisational values to produce a more flexible culture and how these practices could be accelerated. This paper indicates how a more strategic approach by HR departments can support and develop relational managing capability that accelerates cultures change towards a more flexible work environment.This paper describes the context of the change process, the relevant literature, and outlines the research process. The findings from the phases of the data collection are summarised revealing the traumatic perceptions of the change process, but also the instrumental actions of some managers, working creatively with their teams to tackle new tasks and projects. The evidence suggests that these informal practices of task allocation were at the core of change agency in this case study and put the new flexible organisational values into action. The findings illustrate how the organisation moves from valuing managers for their technical competence to valuing managers for their relational competence.The paper then discusses what strategic HR actions were accelerating this process and illuminates the critical role of building managers as change agents. The paper concludes by confirming the need for a strategic approach by HR during organisational change. Building manager capability and supporting informal change agency practices is presented as a core focus for HR during such organisational cultural change programmes.

Role of machine learning and deep learning in securing 5G-driven industrial IoT applications

2021 ◽  
pp. 102685
Author(s):  
Parjanay Sharma ◽  
Siddhant Jain ◽  
Shashank Gupta ◽  
Vinay Chamola
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Iot Applications ◽  
Industrial Iot

scholarly journals CubeSat Communications: Recent Advances and Future Challenges

2020 ◽  
Author(s):  
Nasir Saeed ◽  
Ahmed Elzanaty ◽  
Heba Almorad ◽  
Hayssam Dahrouj ◽  
Tareq Y. Al-Naffouri ◽  
...  
Keyword(s):  
Channel Modeling ◽  
Future Research ◽  
New Paradigm ◽  
Earth Remote Sensing ◽  
Research Directions ◽  
Space Industry ◽  
Recent Advances ◽  
Wide Range ◽  
Future Challenges ◽  
Future Research Directions

<pre><pre>Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. </pre><pre>The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity.</pre><pre>CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system.</pre><pre>This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives.</pre><pre>We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking.</pre><pre>Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.</pre></pre>

scholarly journals Analysis, Deployment and Integration of Platforms for Fog Computing

2020 ◽  
Vol 20 (2) ◽  
pp. e12
Author(s):  
Joaquín De Antueno ◽  
Santiago Medina ◽  
Laura De Giusti ◽  
Armando De Giusti
Keyword(s):  
Data Management ◽  
Sensor Network ◽  
Experimental Work ◽  
Response Times ◽  
Fog Computing ◽  
Data Capture ◽  
Local Data ◽  
Iot Applications ◽  
Flow Of Information ◽  
Large Flow

In IoT applications, data capture in a sensor network can generate a large flow of information between the nodes and the cloud, affecting response times and device complexity but, above all, increasing costs. Fog computing refers to the use of pre-processing tools to improve local data management and communication with the cloud. This work presents an analysis of the features that platforms implementing fog computing solutions should have. Additionally, an experimental work integrating two specific platforms used for controlling devices in a sensor network, processing the generated data, and communicating with the cloud is presented.

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