scholarly journals Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing

2021 ◽  
Vol 19 (1) ◽  
pp. 513-536
Author(s):  
Mazhar Ali Dootio ◽  
◽  
Abdullah Lakhan ◽  
Ali Hassan Sodhro ◽  
Tor Morten Groenli ◽  
...  
Keyword(s):  
Model Simulation ◽  
Blood Pressure Monitoring ◽  
Fog Computing ◽  
Healthcare Applications ◽  
Linear Integer Programming ◽  
Industrial Internet ◽  
Healthcare Monitoring ◽  
Internet Of Healthcare Things ◽  
Security Network ◽  
The Mathematical Model

<abstract><p>These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.</p></abstract>

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

scholarly journals Implementation of Fog computing in IoT-based healthcare system

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Mirjana Maksimović
Keyword(s):  
Healthcare System ◽  
Low Cost ◽  
Fog Computing ◽  
Healthcare Systems ◽  
Healthcare Sector ◽  
Smart Devices ◽  
New Paradigm ◽  
Healthcare Applications ◽  
Privacy And Security ◽  
Main Challenge

Nowhere do the technology advancements bring improvements than in the healthcare sector, constantly creating new healthcare applications and systems which completely revolutionize the healthcare domain. The appearance of Internet of Things (IoT) based healthcare systems has immensely improved quality and delivery of care, and significantly reduced the costs. At the same time, these systems generate the enormous amount of health-associated data which has to be properly gathered, analyzed and shared. The smart devices, as the components of IoT-driven healthcare systems, are not able to deal with IoT-produced data, neither data posting to the Cloud is the appropriate solution. To overcome smart devices’ and Cloud’s limitations the new paradigm, known as Fog computing, has appeared, where an additional layer processes the data and sends the results to the Cloud. Despite numerous benefits Fog computing brings into IoT-based environments, the privacy and security issues remain the main challenge for its implementation. The reasons for integrating the IoT-based healthcare system and Fog computing, benefits and challenges, as well as the proposition of simple low-cost system are presented in this paper.

scholarly journals Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

2021 ◽  
Vol 11 (22) ◽  
pp. 10996
Author(s):  
Jongbeom Lim
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Industrial Revolution ◽  
Virtual Machines ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Virtualization Technology ◽  
Computing Environments ◽  
One Machine

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

An enhanced composting process with bioaugmentation: Mathematical modelling and process optimization

2021 ◽  
pp. 0734242X2110337
Author(s):  
Tea Sokač ◽  
Anita Šalić ◽  
Dajana Kučić Grgić ◽  
Monika Šabić Runjavec ◽  
Marijana Vidaković ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Model Simulation ◽  
Process Conditions ◽  
Air Flow Rate ◽  
Optimal Process ◽  
Composting Process ◽  
Different Types ◽  
Adiabatic Reactor ◽  
The Mathematical Model ◽  
Good Agreement

In this paper, two different types of biowaste composting processes were carried out – composting without and with bioaugmentation. All experiments were performed in an adiabatic reactor for 14 days. Composting enhanced with bioaugmentation was the better choice because the thermophilic phase was achieved earlier, making the composting time shorter. Additionally, a higher conversion of substrate (amount of substrate consumed) was also noticed in the process enhanced by bioaugmentation. A mathematical model was developed and process parameters were estimated in order to optimize the composting process. Based on good agreement between experimental data and the mathematical model simulation results, a three-level-four-factor Box-Behnken experimental design was employed to define the optimal process conditions for further studies. It was found that the air flow rate and the mass fraction of the substrate have the most significant effect on the composting process. An improvement of the composting process was achieved after altering the mentioned variables, resulting in shorter composting time and higher conversion of the substrate.

Towards the Protection and Security in Fog Computing for Industrial Internet of Things

2021 ◽  
pp. 17-32
Author(s):  
G. Rama Subba Reddy ◽  
K. Rangaswamy ◽  
Malla Sudhakara ◽  
Pole Anjaiah ◽  
K. Reddy Madhavi
Keyword(s):  
Internet Of Things ◽  
Distributed Computing ◽  
Fog Computing ◽  
Security And Privacy ◽  
Safety Measures ◽  
Iot Applications ◽  
Industrial Internet ◽  
Sensor Devices ◽  
Key Enabling Technologies ◽  
Privacy Issues

Internet of things (IoT) has given a promising chance to construct amazing industrial frameworks and applications by utilizing wireless and sensor devices. To support IIoT benefits efficiently, fog computing is typically considered as one of the potential solutions. Be that as it may, IIoT services still experience issues such as high-latency and unreliable connections between cloud and terminals of IIoT. In addition to this, numerous security and privacy issues are raised and affect the users of the distributed computing environment. With an end goal to understand the improvement of IoT in industries, this chapter presents the current research of IoT along with the key enabling technologies. Further, the architecture and features of fog computing towards the fog-assisted IoT applications are presented. In addition to this, security and protection threats along with safety measures towards the IIoT applications are discussed.

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

2021 ◽  
pp. 1203-1221
Author(s):  
Shivlal Mewada ◽  
Sita Sharan Gautam ◽  
Pradeep Sharma
Keyword(s):  
Privacy Preservation ◽  
Artificial Bee Colony ◽  
Model Simulation ◽  
Privacy Preserving ◽  
Global Network ◽  
Sensitive Information ◽  
Healthcare Applications ◽  
Efficient Manner ◽  
Bee Colony ◽  
Proposed Model

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

scholarly journals Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 282 ◽  
Author(s):  
Adrian Korodi ◽  
Ruben Crisan ◽  
Andrei Nicolae ◽  
Ioan Silea
Keyword(s):  
Drinking Water ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Non Invasive ◽  
Data Accumulation ◽  
Industrial Internet ◽  
Reduce Energy Consumption ◽  
And Control

The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

scholarly journals Dynamic analysis of material motion using MATLAB/Simulation environment and test verification

2020 ◽  
Vol 12 (9) ◽  
pp. 168781402095780
Author(s):  
Mei Fang ◽  
Zhihong Yu ◽  
Wenjie Zhang
Keyword(s):  
Dynamic Analysis ◽  
Structural Design ◽  
Simulation Analysis ◽  
Model Simulation ◽  
Maximum Relative Error ◽  
Matlab Simulation ◽  
Simulation Results ◽  
Low Efficiency ◽  
Test Verification ◽  
The Mathematical Model

Throwing device is an important factor that directly affects the performance of chaff cutter. In this work, the dynamic analysis linked with the problem of low efficiency and residue blockage of disc knife chaff cutter is executed. Based on this perspective, the mathematical model, simulation, and testing of the material movement have been carried out. Simulations are performed in MATLAB/Simulink environment. An anemometer records the airflow velocity, which provides data for simulation analysis. The simulation results showed that during the movement along the blade, the material first performs deceleration and then accelerates; in other stages, only deceleration. And finally calculated the throwing distance. To support the presented simulations, an experimental study is conducted. The experimental results are compared with simulation results, the maximum relative error between the simulated value and the experimental value is 9.42%, which verified the correctness of the model. This research provides a theoretical basis for the structural design, parameter optimization, and matching of the chaff cutter.

Cloud Based Wireless Infrastructure for Health Monitoring

2020 ◽  
pp. 34-55 ◽  
Author(s):  
Ajay Chaudhary ◽  
Sateesh Kumar Peddoju ◽  
Suresh Kumar Peddoju
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Healthcare Services ◽  
Power Source ◽  
Vital Role ◽  
Healthcare Applications ◽  
Related Data ◽  
Health Related ◽  
Healthcare Monitoring ◽  
Wireless Infrastructure

The wireless infrastructure based devices can collect data for long period of time even with a tiny power source as they perform specific function of collection of health related data and sending to gateways. The sensing data of healthcare monitoring consumes low power but they had limited computation power to process this data, where the cloud computing plays a vital role and compliment the loophole of wireless infrastructure based systems. In cloud computing with its immense computation power for easily deployment of healthcare monitoring algorithms and helps to process sensed data. As these two technologies did great jobs in their respective fields a conflate framework of these two technologies may lead to a great architecture for healthcare applications. This chapter reviews complete state-of-the-art and several use cases related to healthcare monitoring using different wireless infrastructure and adapting cloud based technologies in providing the healthcare services.

Sign in / Sign up

Export Citation Format

Share Document