scholarly journals An Analytical Survey of WSNs Integration with Cloud and Fog Computing

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2625
Author(s):  
Qaisar Shaheen ◽  
Muhammad Shiraz ◽  
Shariq Aziz Butt ◽  
Abdullah Gani ◽  
Muazzam A. Khan
Keyword(s):  
Fog Computing ◽  
Battery Life ◽  
Data Streaming ◽  
Time Data ◽  
Computing Power ◽  
Battery Power ◽  
Effective Combination ◽  
Consumption Data ◽  
Security Checks ◽  
Comprehensive Study

Wireless sensor networks (WSNs) are spatially scattered networks equipped with an extensive number of nodes to check and record different ecological states such as humidity, temperature, pressure, and lightning states. WSN network provides different services to a client such as monitoring, detection, and runtime decision-making against events occurrence. However, the WSN network still has some limitations in computing power, storage resources, and battery life, which make the network is restricted for data transformation. It is due to less supportive battery power, and limited memory of nodes. The integration of WSN and cloud offers an open, adaptable, and more reconfigurable stage for different security checks and regulating requirements. In this paper, we discovered how WSN and cloud computing (CC) are integrated and help to accomplish different goals. Additionally, a comprehensive study about procedures and issues for an effective combination of WSN-CC is presented. This work also presents the work proposed by the research community for WSN-CC. Besides, we explored the integration of WSN/IoT with Fog computing (FC). Based on investigations, WSN integration with Fog computing (FC) has many benefits with respect to latency, energy consumption, data processing, and real-time data streaming. FC is not a substitute for distributed computing, so far it is utilized to improve the productivity of the sensor.

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.

scholarly journals MEASURING PERFORMANCE OF DATABASE SYSTEMS FOR APPLICATIONS OF IOT

2020 ◽  
Vol 4 (5) ◽  
pp. 49-52
Author(s):  
Samson Paul K ◽  
Ateeq Ahmed H ◽  
Emmanuel Raju A
Keyword(s):  
Real Time ◽  
Fog Computing ◽  
Database Systems ◽  
Streaming Data ◽  
Data Streaming ◽  
Time Data ◽  
Geographical Regions ◽  
The Right ◽  
Database Platform ◽  
Day By Day

Choosing the right database platform(s) for IoT solutions is daunting. First, IoT solutions can be distributed across geographical regions. As opposed to a centralized cloud-based solution, more solutions are adopting a combination of fog computing at the edge and cloud computing. As such, your database platforms must offer you the flexibility to process the data at the edge and synchronize between the edge servers and the cloud. Second, depending on your IoT use cases, the capabilities you want in your database could range from real-time data streaming, data filtering and aggregation, near-zero latency read operations, instant analytics, high availability, geo distribution, schema flexibility and so on. This article walks you through the four steps in choosing the right database platforms for your IoT solutions. The amount of data stored in IoT databases increases as the IoT applications extend throughout smart city appliances, industry and agriculture. Contemporary database systems must process huge amounts of sensory and actuator data in real-time or interactively. Facing this first wave of IoT revolution, database vendors struggle day-by-day in order to gain more market share, develop new capabilities and attempt to overcome the disadvantages of previous releases, while providing features for the IoT.

scholarly journals Anonymization Framework for IoT Resource Discovery based on Edge Centric Privacy Model

2020 ◽  
Vol 10 (2) ◽  
pp. 255-567
Keyword(s):  
Fog Computing ◽  
Close Correlation ◽  
Resource Discovery ◽  
Cloud Services ◽  
Cloud System ◽  
Data Streaming ◽  
Time Data ◽  
Sensitive Data ◽  
User Centric ◽  
Real Time Data Processing

As the efficacy of Internet of Things is expeditiously growing, maintaining privacy with respect users and applications has become a significant aspect. Since the data is getting generated at tremendous rate that includes Sensitive data (any data considered as private by the Data-owner) which has to be hidden, especially the data collected from the Crowd-Source. Due to resource-constrained sensing devices, IoT infrastructures use Edge devices for real-time data processing. Protecting sensitive data from malicious activity becomes a key factor, as all the communication flows through insecure channels. To develop security infrastructures for IoT and distributed Edge networks, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. The method presented herein is applied to an amusement park scenario, which is modeled as a typical smart IoT network. Here, data from sensors and social networks can boost smart lighting to provide citizens with an elegant and safe environment. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. Firstly, a standalone IoT-edge system is developed, and later, an integrated IoT-based edge-cloud system is designed to compare the systems’ effectiveness. The implementation results show a close correlation between the standalone edge and dual mode edge system. However, the edge-cloud system provides more flexibility and capability to counter the sensitive data streaming and analytics services within the constrained IoT framework. In this paper we have developed a system that uses fog computing approach to perform various tasks and filters the sensitive data, thus helps in preserving privacy.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

Real-Time Data Streaming Architecture and Intelligent Workflow Management for Processing Massive Ecological Videos

2013 ◽  
Author(s):  
Gayathri Nadarajan ◽  
Cheng-Lin Yang ◽  
Yun-Heh Chen-Burger ◽  
Yu-Jung Cheng ◽  
Sun-In Lin ◽  
...  
Keyword(s):  
Real Time ◽  
Workflow Management ◽  
Data Streaming ◽  
Time Data ◽  
Real Time Data

Fog Computing and Virtualization

2018 ◽  
pp. 53-67
Author(s):  
Siddhartha Duggirala
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Main Concern ◽  
Time Data ◽  
Human Machine Interaction ◽  
Processing Power ◽  
New Generation ◽  
And Storage ◽  
Virtual Services ◽  
Machine Interaction

The essence of cloud computing is moving out the processing from the local systems to remote systems. Cloud is an umbrella of physical/virtual services/resources easily accessible over the internet. With more companies adopting cloud either fully through public cloud or hybrid model, the challenges in maintaining a cloud capable infrastructure is also increasing. About 42% of CTOs say that security is their main concern for moving into cloud. Another problem, which is mainly problem with infrastructure, is the connectivity issue. The datacenter could be considered as the backbone of cloud computing architecture. Handling this new generation of requirements of volume, variety, and velocity in IoT data requires us to evaluate the tools and technologies. As the processing power and storage capabilities of the end devices like mobile phones, routers, sensor hubs improve, we can increase leverage these resources to improve your quality and reliability of services. Applications of fog computing is as diverse as IoT and cloud computing itself. What IoT and fog computing have in common is to monitor and analyse real-time data from network connected things and acting on them. Machine-to-machine coordination or human-machine interaction can be a part of this action. This chapter explores fog computing and virtualization.

Unique Taxonomy for Evaluating Fog Computing Services

2021 ◽  
pp. 165-177
Author(s):  
Akashdeep Bhardwaj ◽  
Sam Goundar
Keyword(s):  
Fog Computing ◽  
Research Work ◽  
Cloud Service ◽  
Time Data ◽  
Computing Services ◽  
Last Mile ◽  
Technology Applications ◽  
Service Consumer ◽  
Cloud Network ◽  
Best Fit

Cloud computing has slowly but surely become the foremost service provider for information technology applications and platform delivery. However, Cloud issues continue to exist, like cyberattacks, slow last mile latency, and clouds lack client-centric and location-aware applications to process real time data for efficient and customized application delivery. As an alternative, Fog Computing has the potential to resolve these issues by extending the Cloud service provider's reach to the edge of the Cloud network model, right up to the Cloud service consumer. This enables a whole new state of applications and services which increases the security, enhances the cloud experience and keeps the data close to the user. This research article presents a review on the academic literature research work on Fog Computing, introduces a novel taxonomy to classify cloud products based on Fog computing elements and then determine the best fit Fog Computing product to choose for the Cloud service consumer.

At Sensor Diagnosis for Smart Healthcare

2018 ◽  
Vol 10 (4) ◽  
pp. 1-13
Author(s):  
Chetna Laroiya ◽  
Vijay Bhushan Aggarwal
Keyword(s):  
Joint Probability ◽  
Sliding Window ◽  
Communication Technologies ◽  
Data Streaming ◽  
Round Trip ◽  
Time Data ◽  
Smart Healthcare ◽  
Body Sensors ◽  
Round Trip Delay

In order to implement IoT-based health-care for improved quality of life, we have to deal with sensor and communication technologies. In this article, the authors propose an approach to analyse real-time data streaming from a patient's surface body sensors, which are to be looked upon in a small sliding window frame. Time series analysis of data from the sensors is effective in reducing the round-trip delay between patient and the medical server. Two algorithms are for the sensor, and odd measures are proposed based on joint probability and joint conditional probability. The proposed algorithms are to be SQL compliant, as traces of at-sensor UDBMS alongside elementary capabilities supports databases with a meagre amount of SQL, which is evident in the literature.

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