We use data analysis and visualization capabilities of ThingSpeak, our favorite Internet of Things platform to capture and analyze performance data, to help with performance monitoring and to generate alerts
How do APIs and IoT relate? The answer is not as simple as merely adding an API on top of a dumb device, but rather about understanding the architectural patterns for implementing an IoT fabric. There are typically two or three trends:
Exposing the device to a management framework
Exposing that management framework to a business centric logic
Exposing that business layer and data to end users.
This last trend is the IoT stack, which involves a new shift in the separation of what stuff happens, where data lives and where the interface lies. For instance, it's a mix of architectural styles between cloud, APIs and native hardware/software configurations.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
How do APIs and IoT relate? The answer is not as simple as merely adding an API on top of a dumb device, but rather about understanding the architectural patterns for implementing an IoT fabric. There are typically two or three trends:
Exposing the device to a management framework
Exposing that management framework to a business centric logic
Exposing that business layer and data to end users.
This last trend is the IoT stack, which involves a new shift in the separation of what stuff happens, where data lives and where the interface lies. For instance, it's a mix of architectural styles between cloud, APIs and native hardware/software configurations.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
What we got covered?
1) What Is Industrial IoT
2) Application of Industrial IOT
3) Machine To Machine (M2M)
4) Benefits of Industrial IoT
5) Vendors in Industrial IoT
6) Features of Industrial IoT
This presentation introduces to the world of hardware everyone can use to get stated with Internet of Things (IoT) such as Arduino, Raspberry Pi and ESP8266.
on successful go through of this complete PPT, the learners can be able to understand the Raspberry PI, Raspberry Pi Interfaces(Serial, SPI,I2C) Programming, Python programming with Raspberry PI with the focus of Interfacing external gadgets
Controlling output Reading input from pins.
What we learned from #CMGimPACt Performance and Capacity Conference attendee ...Anoush Najarian
During the #CMGimPACt Performance and Capacity conference, I informally interviewed attendees on what brings them to CMG and how we can serve them better, then analyzed the results using Contextual Interviewing techniques, and created this report.
What we got covered?
1) What Is Industrial IoT
2) Application of Industrial IOT
3) Machine To Machine (M2M)
4) Benefits of Industrial IoT
5) Vendors in Industrial IoT
6) Features of Industrial IoT
This presentation introduces to the world of hardware everyone can use to get stated with Internet of Things (IoT) such as Arduino, Raspberry Pi and ESP8266.
on successful go through of this complete PPT, the learners can be able to understand the Raspberry PI, Raspberry Pi Interfaces(Serial, SPI,I2C) Programming, Python programming with Raspberry PI with the focus of Interfacing external gadgets
Controlling output Reading input from pins.
What we learned from #CMGimPACt Performance and Capacity Conference attendee ...Anoush Najarian
During the #CMGimPACt Performance and Capacity conference, I informally interviewed attendees on what brings them to CMG and how we can serve them better, then analyzed the results using Contextual Interviewing techniques, and created this report.
The presentation outlines a methodology of queuing model-based load testing of large (with thousands users) enterprise applications deployed on premise and in the Cloud
25 Examples of Native Analytics in Modern ProductsKeen
Data is so ubiquitous, we are sometimes oblivious to just how much of it we interact with—and how many companies are making it a core part of their product. Whether you’re aware of it or not, product leaders across industries are using data to drive engagement and prove value to their end-users. From Fitbit and Medium to Spotify and Slack, data is being leveraged not just for internal decision-making, but as an external product offering and differentiator.
We’ve gathered 25 examples of native analytics in modern software to highlight their power and hopefully inspire their further adoption.
The past decade has seen significant advancement in the field of consumer electronics. Various ‘intelligent’ appliances such as cellular phones, air-conditioners, home security devices, home theatres, etc. are set to realize the concept of a smart home. They have given rise to a Personal Area Network in home environment, where all these appliances can be interconnected and monitored using a single controller.
Busy families and individuals with physical limitation represent an attractive market for home automation and networking. A wireless home network that does not incur additional costs of wiring would be desirable. Bluetooth technology, which has emerged in late 1990s, is an ideal solution for this purpose.
Home automation involves introducing a degree of computerized or automatic control to
Certain electrical and electronic systems in a building. These include lighting, temperature
Control etc.
This project demonstrates a simple home automation system which contains a remote mobile host controller and several client modules (home appliances). The client modules communicate with the host controller through a wireless device such as a Bluetooth enabled mobile phone, in this case, an android based Smart phone.
Boston DevOps Days 2016: Implementing Metrics Driven DevOps - Why and HowAndreas Grabner
How can we detect a bad deployment before it hits production? By automatically looking at the right architectural metrics in your CI/CD and stop a build before its too late. Lets hook up your test automation with app metrics and use them as quality gates to stop bad builds early!
Final Year Project For Computer ScienceSimplilearn
This video on Final Year Projects for Computer Science by simplilearn is focused on the industry-relevant projects that will give a waitage for your resume, which are on the on-trend domain according to the current IT standards.
This tutorial will cover the projects on some of the most popular Domains like Artificial intelligence, Machine Learning, Data Science, Web Technology, and IoT.
By the end of this video, you will be able to choose your domain of interest and get an idea about the project you can conduct on that particular domain.
This video will cover the following.
00:00 Introduction to Final Year Projects for Computer Science
00:58 What is Domain and its Importance
01:20 The Top % Booming Domain to Conduct Projects
01:32 Artificial Intelligence
01:51 Project in AI
03:18 Web Technology
03:44 Projects in Web Technology
05:06 Data Science
05:30 Projects in Data Science
06:49 Machine Learning
07:10 Projects in ML
08:33 IOT
08:49 Projects in IOT
10:12 Bonus Projects
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#ComputerScienceProjects #ProjectIdeas #ComputerScienceProject #CSProject #Project #CareerGuidance #SoftSkills #Simplilearn
Simplilearn is one of the world’s leading certification training providers. We partner with companies and individuals to address their unique needs, providing training and coaching that helps working professionals achieve their career goals. We've helped over 1 million professionals and companies across 150+ countries get trained, acquire certifications, and upskill their employees. Our training courses are designed and updated by 2000+ renowned industry experts. Our blended learning approach combines online classes, instructor-led live virtual classrooms, project work, and 24/7 teaching assistance.
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Embedded system & IoT Course | certification Program | Learn and BuildLearn and Build
Introduction to embedded systems and IoT, during this course you will learn how to create IoT web applications like flask ,analytics, google clouds etc. and have many opportunities to explore. Best industry expert will guide your path and become an IoT expert.
Splunk All the Things: Our First 3 Months Monitoring Web Service APIs - Splun...Dan Cundiff
A presentation titled "Splunk All the Things: Our First 3 Months Monitoring Web Service APIs" that Dan Cundiff and Eric Helgeson from Target Corporation gave at Splunk .conf2012.
AWS Summit Berlin 2013 - Big Data AnalyticsAWS Germany
Learn more about the tools, techniques and technologies for working productively with data at any scale. This session will introduce the family of data analytics tools on AWS which you can use to collect, compute and collaborate around data, from gigabytes to petabytes. We'll discuss Amazon Elastic MapReduce, Hadoop, structured and unstructured data, and the EC2 instance types which enable high performance analytics.
SoftElegance Services: Data Science, Data Engineering, Big Data Architecture Daryna Dubitska
Big Data - it’s a huge amount of information which use unique instruments, methods, treatment approach, and system analysis.
SoftElegance can offer you all spectre of Big Data Services!
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. 2
Agenda
The Internet of Things (IoT)
ThingSpeak
A few of our favorite IoT applications
Performance Trends and Alerts on ThingSpeak
3. 3
The Internet of Things is…
The internet of things (IoT) is the internetworking of physical devices, vehicles
(also referred to as "connected devices" and "smart devices"), buildings and
other items—embedded with electronics, software, sensors, actuators, and
network connectivity that enable these objects to collect and exchange data.
https://en.wikipedia.org/wiki/Internet_of_things
4. 4
Things…
Typical “thing”
– A sensor with connectivity
– An actuator with connectivity
They are constrained
– Processing
– Memory
– Protocols
– Clock
– Power
They run code over and over – sometimes they sleep wake up and run
code, and go back to sleep
There are lots of them
5. 5
What is the Internet of Things?
Edge Nodes Exploratory Analysis
Analytic IoT Platform
Deploy analytics
to aggregator
Deploy algorithms to nodes/devices
6. 6
Agenda
The Internet of Things (IoT)
ThingSpeak
A few of our favorite IoT applications
Performance Trends and Alerts on ThingSpeak
7. 7
What is ThingSpeak?
Analytic IoT platform
– Collect data from sensors, “things”
– Visualize data instantly
– Has more than 60,000 users
Analyze data
– MATLAB integration allows users to run
scheduled code on data coming into
ThingSpeak
Act on data
– E.g. send a tweet when the temperature in
your backyard reaches 32 degrees
8. 8
Who is ThingSpeak for?
Makers
Academics
Engineers and scientists
https://thingspeak.com/
9. 9
ThingSpeak: Collecting Data using Channels
For any new data, first login and
create a channel in ThingSpeak
Channels have read and write API
keys and can be public or private
A channel is made up of 8 fields and
can store 8 streams of data (Temp,
Humidity, etc.)
Channels can be updated at a
maximum rate of once every 15
seconds
ThingSpeak Weather Channel
10. 10
Agenda
The Internet of Things (IoT)
ThingSpeak
A few of our favorite IoT applications
Performance Trends and Alerts on ThingSpeak
11. 11
Objectives
Measure, explore, discover weather patterns
Provide niche weather service
Solution
Arduino station with weather sensors
Cloud-based aggregation and analysis
Full example available at
makerzone.mathworks.com
Example 1: Monitoring Weather
12. 12
Fun with Arduino!
Arduino Serial Monitor
records the data from
the temperature sensor,
the pressure sensor,
and the humitidity
sensor, as transmitted
to ThingSpeak!
Placing ice on
the Arduino temperature
sensor to cool it off!
13. 13
Fun with Raspberry Pi
Building a circuit with a
temperature sensor in this
week's
Posting the temperature
readings from the sensor
to ThingSpeak using
Twitter.
14. 14
Objectives
Measure, explore, discover traffic patterns
Provide live local traffic information service
Solution
RaspberryPi + webcam
Automated deployment of vision
algorithms on embedded sensor
Full example available at
makerzone.mathworks.com
Example 2: Monitoring Traffic
16. 16
Agenda
The Internet of Things (IoT)
ThingSpeak
A few of our favorite IoT applications
Performance Trends and Alerts on ThingSpeak
17. 17
Case Study: Performance Optimization of MATLAB Code
A customer ran into slow performance issues with her code in MATLAB.
She saw such slow performance in that she decided to recode her
algorithm in another language. We wanted to show her some simple
techniques in MATLAB that could bring her code down to a more
reasonable running time.
18. 18
What is the problem the user was trying to solve?
The code generates locations on a 2D
grid with dimensions nx1 by nx2
The code iterates through all possible
combinations of these initial and final
positions
23. 23
Focusing in on the Performance Improvements
February 1 – baseline at 23.8s
If we hover over the data points in the trend plots, or zoom in, we will spot the key
optimizations that helped performance of Sarah's code. Initial Code measured at around
23.8s:
26. 26
Focusing in on the Performance Improvements
Optimization #1: February 20 – 22.3s
On February 20, we switched to Code with Preallocation, speeding up to
around 22.3s, or by 6%:
28. 28
Focusing in on the Performance Improvements
Optimization #2: May 20 - down to 0.2s!
On May 2, we implemented the optimization to Vectorize the Inner Two loops; this sped up
the code 100+-fold, to 0.2s.
30. 30
Focusing in on the Performance Improvements
Optimization #3: June 9 – dipped down to 0.06s!
Finally, on June 9, we implemented Vectorize the Inner Three Loops, and the code sped up
to 0.06s, or by 67%.
32. 32
In all, our trends capture pretty awesome 400+-fold performance improvement in the past
few months!
The result: 400x as fast overall!
33. 33
What’s Next?
Use Analysis and Visualization tools on ThingSpeak for more advanced
data analysis like displaying error bars, normalizing performance across a
suite of tests
Set up email alerts in response to changes in performance
Use machine learning for anomaly detection and to generate smart alerts
34. 34
Thank you!
Many thanks to the ThingSpeak team for help with these slides and for
creating and growing ThingSpeak!
A big thank you to Andy Campbell for helping me publish a MATLAB Central
blog post on this
@anoushnajarian
Anoush.Najarian@mathworks.com
linkedin.com/in/anoushnajarian
Editor's Notes
Internet of Things (IoT) describes an emerging trend where a large number of embedded devices (things) are connected to the Internet. These connected devices communicate with people and other things and often provide sensor data to cloud storage and cloud computing resources where the data is processed and analyzed to gain important insights. Cheap cloud computing power and increased device connectivity is enabling this trend.
IoT solutions are built for many vertical applications such as environmental monitoring and control, health monitoring, vehicle fleet monitoring, industrial monitoring and control, and home automation. MATLAB® and Simulink® products support IoT systems by helping you develop and test edge node devices, access and aggregate data, and analyze IoT sensor data.
At a high level, many IoT systems can be described using the diagram above. The left side of the diagram illustrates edge nodes. Edge nodes are devices that collect data and include devices such as wireless temperatures sensors, heart rate monitors, and hydraulic pressure sensors. The middle of the diagram shows the data aggregator. The aggregator collects, processes and stores data from many edge nodes that are often geographically dispersed, and it may have the capability to analyze and take action on the incoming data.
The right side of the diagram depicts the historical analysis of data. In this case, the data is pulled from the aggregator into a software environment to allow researchers to gain insight from the data and to prototype algorithms that may eventually execute on the aggregator or on the edge node device itself.