In IoT, the sensor data need to consider Sensor Value, Veracity, Volume, Velocity & Variety within the data classification in its context and cannot be treated equal to be cost efficient for security consideration.
Survey of a Symptoms Monitoring System for Covid-19vivatechijri
The Internet of Things (IOT) depicts the organization of actual items that are implanted with sensors, programming, and different advances for the point of interfacing and trading information with different gadgets and frameworks over the web . In this day and age, there are numerous IOT based, these IOT based gadgets and machines range from wearable like brilliant watches to RFID stock following chips. IOT associated gadgets convey by means of organizations or cloud-based stages associated with the snare of Things. Among the applications that Internet of Things (IOT) encouraged to the planet , Healthcare applications are generally imperative . There are numerous wellbeing checking gadgets accessible. These framework comprises two sensors that is Heartbeat and blood heat sensor and furthermore contains Arduino UNO. This versatile gadget will screen heartbeat and blood heat utilizing sensors. The framework utilizes Arduino board which is associated with heart beat sensor and temperature sensor. The framework will take contribution from the guts beat and blood heat sensors and can send the data to Arduino. The Arduino will send the information of two sensors to LCD alphanumeric presentation . This presentation will show the perusing of the heartbeat sensor and blood heat sensor in BPM (Beats Per Minute) and in Celsius or Fahrenheit.
Implementation Of Real Time IoT Based Health monitoring systemkchakrireddy
The main aim of this project is to interconnect the available medical resources and offer smart, reliable, and effective healthcare service to elderly people. Health monitoring for active and assisted living is one of the paradigms that can use the IOT advantages to improve the elderly lifestyle in this project we present an IOT architecture customized for healthcare applications. The proposed architecture collects the data and relays it to the cloud where it is processed and analyzed. Feedback actions based on the analyzed data can be sent back to the user.
Survey of a Symptoms Monitoring System for Covid-19vivatechijri
The Internet of Things (IOT) depicts the organization of actual items that are implanted with sensors, programming, and different advances for the point of interfacing and trading information with different gadgets and frameworks over the web . In this day and age, there are numerous IOT based, these IOT based gadgets and machines range from wearable like brilliant watches to RFID stock following chips. IOT associated gadgets convey by means of organizations or cloud-based stages associated with the snare of Things. Among the applications that Internet of Things (IOT) encouraged to the planet , Healthcare applications are generally imperative . There are numerous wellbeing checking gadgets accessible. These framework comprises two sensors that is Heartbeat and blood heat sensor and furthermore contains Arduino UNO. This versatile gadget will screen heartbeat and blood heat utilizing sensors. The framework utilizes Arduino board which is associated with heart beat sensor and temperature sensor. The framework will take contribution from the guts beat and blood heat sensors and can send the data to Arduino. The Arduino will send the information of two sensors to LCD alphanumeric presentation . This presentation will show the perusing of the heartbeat sensor and blood heat sensor in BPM (Beats Per Minute) and in Celsius or Fahrenheit.
Implementation Of Real Time IoT Based Health monitoring systemkchakrireddy
The main aim of this project is to interconnect the available medical resources and offer smart, reliable, and effective healthcare service to elderly people. Health monitoring for active and assisted living is one of the paradigms that can use the IOT advantages to improve the elderly lifestyle in this project we present an IOT architecture customized for healthcare applications. The proposed architecture collects the data and relays it to the cloud where it is processed and analyzed. Feedback actions based on the analyzed data can be sent back to the user.
RTI Connext DDS messaging software helps evolve standalone systems to integrated distributed systems, connect devices to improve patient outcomes, and replace dedicated point-to-point wiring with networks.
A wide range of additional benefits are possible, including improved diagnosis and safety, delegated care or treatment, and smarter machine assistance for healthcare.
Wireless Mesh Networking - A development patheveryunitone
A brief introduction in applying wireless mesh networking to the geotechnical industry. Presented at the Field Measurements in Geomechanics symposium, Boston, MA in 2007.
[Apologies for the malformed animations]
Implementation Of Real Time IoT Based Health monitoring systemkchakrireddy
This is a project implemented by me and my friends during our final year. It is designed for doctors who are not able to be with the patients all the time. This improves the gap between the patients and the doctors.
IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart RegionsKaran Mitra
The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.
In this paper, we provide the solution for physically challenged people like hearing impaired people .It is a
smart application works for the hearing impaired people. People with hearing loss have move through the
activities of daily living at home, at work and in business situations. People may face the difficulty in hearing
the environment sounds and identifying the sounds. Our main contribution for the hearing impaired people is
to make them understand the type of sounds which is useful to them. The conventional sound recognition
techniques are not directly applicable since the background noise and reverberations are high which leads to
low performance. A deep neural network which is capable of classifying and predicting the information from
unstructured data such as image, text or sounds that makes the machine to get the environmental sounds. In
this paper, a deep learning algorithm called CNN(convolution neural network) which classify the sound audio
clips. This model will results the accuracy of 80% which is higher than the conventional technique .It achieves
good results comparable to other approaches.
deep learning; convolution neural network; feature extraction; sound recognition; sound event
classification.
Smart Water Monitoring System using Cloud Serviceijtsrd
Water is the basic need for survival. Hence, the wastage of it is not tolerable. Water scarcity is the lack of sufficient available water resources to meet water needs within a region. Its effects are spread all over the world and around 2.8 billion people are affected by it. More than 1.2 billion people lack access to clean drinking water. Therefore, water monitoring has become an important subject of matter. The project Water Monitoring System for Smart Village using cloud service, as the name says it is all about monitoring of water right from small villages, townships to entire urban infrastructure. The project deals with the efficient monitoring of water using Internet of Things IoT technology enabled by sensors. The sensor network can be flexible expanded and shrunk according to the requirements of setup. It is used for remotely controlling the water flow, cutting the water supply, monitoring and analyzing the water usage across the nodes, with the help of cloud connectivity. Further, more statistical data can be gathered and can be used by govt. authorities for defining policies, strategies and billing calculations. So ultimately, this will help to conserve and efficiently utilize the natural resource. Using IoT takes into account of waste wastage right from small village to large scale. It can also control the water usage in a precise way. Divya YA "Smart Water Monitoring System using Cloud Service" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21379.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21379/smart-water-monitoring-system-using-cloud-service/divya-ya
Prototyping of Wireless Sensor Network for Precision Agriculture ijcisjournal
Now-a-days the climatic conditions are not same and predictable. More over the wireless sensor network
[1] carved path in many applications. There are many manual methods to cultivate a healthy crop which
involves a lot of manpower. Hence there is a need to design a system for precision agriculture. Precision
agriculture means giving the correct input to the crops at the right time. This paper explains how the real
input is given to the crops according to the environment change [2]. This system design uses Arduino Uno
.The values which are measured by the sensors are transmitted to a centralized device which is Zigbee
(coordinator). After the values received by the Zigbee, according to those values precise decision will be
taken by the experts.
Our future battle field system will have more difficulties to maintain security, because of increasing military competitive. Ability to understand, predict and adopt the vast array of inter-networked things is very difficult. Unwanted fire, unauthorized human intervention and other object movement will play major important role for affecting military environment. This project aims to help our future military environment by introducing new technology LoRaWAN in IoT (Internet of Things). LoRaWAN (Long Range Wide Area Network) is a state -of- art commercial of the self (COTS) technology. This project consist of sensors, embedded microcontrollers equipped with LoRaWAN, embedded processors equipped with LoRaWAN and cloud technology. By introducing this new technology in our future military environment we can easily find out criminal activities and fire hazards.
The vital signs monitor used in hospitals and clinics is used
to monitor the vital organs of a critically ill person
To find out the Blood Pressure measure, a visit to the
doctor is needed. In rural areas, there are no clinics or proper medical facilities available. Also, people cannot afford repeat visits. To tackle these problems, a portable, cost effective and
necessary product which would reduce the burden on the
medical system is needed. Our project provides one
solution. The GSM and WiFi capabilities gives it ease of
accessibility to both doctor as well as patient, reduce
diagnosis and treatment time.
RTI Connext DDS messaging software helps evolve standalone systems to integrated distributed systems, connect devices to improve patient outcomes, and replace dedicated point-to-point wiring with networks.
A wide range of additional benefits are possible, including improved diagnosis and safety, delegated care or treatment, and smarter machine assistance for healthcare.
Wireless Mesh Networking - A development patheveryunitone
A brief introduction in applying wireless mesh networking to the geotechnical industry. Presented at the Field Measurements in Geomechanics symposium, Boston, MA in 2007.
[Apologies for the malformed animations]
Implementation Of Real Time IoT Based Health monitoring systemkchakrireddy
This is a project implemented by me and my friends during our final year. It is designed for doctors who are not able to be with the patients all the time. This improves the gap between the patients and the doctors.
IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart RegionsKaran Mitra
The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.
In this paper, we provide the solution for physically challenged people like hearing impaired people .It is a
smart application works for the hearing impaired people. People with hearing loss have move through the
activities of daily living at home, at work and in business situations. People may face the difficulty in hearing
the environment sounds and identifying the sounds. Our main contribution for the hearing impaired people is
to make them understand the type of sounds which is useful to them. The conventional sound recognition
techniques are not directly applicable since the background noise and reverberations are high which leads to
low performance. A deep neural network which is capable of classifying and predicting the information from
unstructured data such as image, text or sounds that makes the machine to get the environmental sounds. In
this paper, a deep learning algorithm called CNN(convolution neural network) which classify the sound audio
clips. This model will results the accuracy of 80% which is higher than the conventional technique .It achieves
good results comparable to other approaches.
deep learning; convolution neural network; feature extraction; sound recognition; sound event
classification.
Smart Water Monitoring System using Cloud Serviceijtsrd
Water is the basic need for survival. Hence, the wastage of it is not tolerable. Water scarcity is the lack of sufficient available water resources to meet water needs within a region. Its effects are spread all over the world and around 2.8 billion people are affected by it. More than 1.2 billion people lack access to clean drinking water. Therefore, water monitoring has become an important subject of matter. The project Water Monitoring System for Smart Village using cloud service, as the name says it is all about monitoring of water right from small villages, townships to entire urban infrastructure. The project deals with the efficient monitoring of water using Internet of Things IoT technology enabled by sensors. The sensor network can be flexible expanded and shrunk according to the requirements of setup. It is used for remotely controlling the water flow, cutting the water supply, monitoring and analyzing the water usage across the nodes, with the help of cloud connectivity. Further, more statistical data can be gathered and can be used by govt. authorities for defining policies, strategies and billing calculations. So ultimately, this will help to conserve and efficiently utilize the natural resource. Using IoT takes into account of waste wastage right from small village to large scale. It can also control the water usage in a precise way. Divya YA "Smart Water Monitoring System using Cloud Service" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21379.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21379/smart-water-monitoring-system-using-cloud-service/divya-ya
Prototyping of Wireless Sensor Network for Precision Agriculture ijcisjournal
Now-a-days the climatic conditions are not same and predictable. More over the wireless sensor network
[1] carved path in many applications. There are many manual methods to cultivate a healthy crop which
involves a lot of manpower. Hence there is a need to design a system for precision agriculture. Precision
agriculture means giving the correct input to the crops at the right time. This paper explains how the real
input is given to the crops according to the environment change [2]. This system design uses Arduino Uno
.The values which are measured by the sensors are transmitted to a centralized device which is Zigbee
(coordinator). After the values received by the Zigbee, according to those values precise decision will be
taken by the experts.
Our future battle field system will have more difficulties to maintain security, because of increasing military competitive. Ability to understand, predict and adopt the vast array of inter-networked things is very difficult. Unwanted fire, unauthorized human intervention and other object movement will play major important role for affecting military environment. This project aims to help our future military environment by introducing new technology LoRaWAN in IoT (Internet of Things). LoRaWAN (Long Range Wide Area Network) is a state -of- art commercial of the self (COTS) technology. This project consist of sensors, embedded microcontrollers equipped with LoRaWAN, embedded processors equipped with LoRaWAN and cloud technology. By introducing this new technology in our future military environment we can easily find out criminal activities and fire hazards.
The vital signs monitor used in hospitals and clinics is used
to monitor the vital organs of a critically ill person
To find out the Blood Pressure measure, a visit to the
doctor is needed. In rural areas, there are no clinics or proper medical facilities available. Also, people cannot afford repeat visits. To tackle these problems, a portable, cost effective and
necessary product which would reduce the burden on the
medical system is needed. Our project provides one
solution. The GSM and WiFi capabilities gives it ease of
accessibility to both doctor as well as patient, reduce
diagnosis and treatment time.
Sensing-as-a-Service - An IoT Service Provider's PerspectivesDr. Mazlan Abbas
UM-MCMC Connected Communities and Internet of Things (IoT): Building Value through Visibility
at Universiti Malaya (UM)
Wednesday, December 10, 2014 from 8:00 AM to 4:00 PM (MYT)
Kuala Lumpur, Malaysia
Applying Auto-Data Classification Techniques for Large Data SetsPriyanka Aash
In the current data security landscape, large volumes of data are being created across the enterprise. Manual techniques to inventory and classify data makes it a tedious and expensive activity. To create a time and cost effective implementation of security and access controls, it becomes key to automate the data classification process.
(Source: RSA USA 2016-San Francisco)
US20150227118 illustrates the IoT Cloud Big Data AI system for facilitating automatic control of the smart home devices based on past device behavior, current device events, sensor data, and server-sourced data. Cloud-based big data analytics is accessible via a server system for analyzing data associated with persons or buildings in a geographic region about the building, such as local news and weather information and data pertaining to appliances within the geographic region, such as a neighborhood, zip code, and so on. The analyzed data is used to develop the control rules to control smart home devices automatically.
IoT Meets Big Data: The Opportunities and Challenges by Syed Hoda of ParStreamgogo6
Download our special report, IoT Tech for the Manager: http://bit.ly/report1-slideshare
IoT Meets Big Data: The Opportunities and Challenges as presented at the IoT Inc Business' Eighth Meetup. See: http://www.iot-inc.com/iot-meets-big-data-the-opportunities-and-challenges/
In our eighth Meetup we have Syed Hoda, Chief Marketing Officer of ParStream presenting “IoT Meets Big Data: The Opportunities and Challenges”. Come meet other business leaders in the IoT ecosystem and discuss the business issues you face in the Internet of Things.
Presentation Abstract
The Internet of Things (IoT) and Big Data have each made press headlines and continue to be board-level priorities. The intersection of IoT and Big Data is a fascinating area of innovation with tremendous scope for business impact. From industrial sensors to vehicles to health monitors, a huge variety of devices connects to the Internet and share information. At the same time, the cost to store data has dropped dramatically while capabilities for analysis have made huge leaps forward. How can analytics drive business benefits from IoT projects? What are the challenges in storing and analyzing huge amounts of real-world information? How can companies generate more value from their data? We will address these questions and also share our perspectives on innovative technologies enabling new IoT use cases.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Big Data is becoming economically feasible for health care. These slides describe how the cost of sensors, data processing, data storage and data analyzing are falling, how new and better forms of storage and algorithms are being implemented, and what this means for sustainable health care. These changes are enabling a move towards personalized health care.
A presentation on IoT - Internet of Things. Helps in getting a overview about the technology, architecture, platforms & applications used. With one real life example of Philips Hue Lights
Internet 0f Things IoT
An IoT device is a device that has a network with sensors and actuators to enable communication with other devices which can be connected as well as to other computer systems or the internet.
The term “Internet-of-Things” was coined by Kevin Ashton in 2013 but it does not appear until 2016, therefore IoT was first used by researchers at Princeton University. We will discuss what an IoT is a today and what the future holds for it.
What Is an Internet Of Things Device?
Most of us have heard the buzzword “Internet” before in our lives. If you google the Internet of Things and see a plethora of websites about various things such as smart home devices, health monitoring gadgets, drones, and more, then that means there is something new to look out for. So to understand a bit more about what an IoT is altogether you first need to learn how it works and why it’s so popular online.
With the help of these devices, we can talk to our phones and computers as if they were humans. A recent survey by IDC showed that nearly 33 million of us use some kind of wearable technology each year. According to Cisco, it is estimated that by 2020, around half a billion people will have access to their homes using mobile IoT devices.
The fact is that most of us are unaware that what we are getting into is actually IoT. What do you think of this amazing word “Internet”? Or even the name itself, the Internet of things? That’s when you realize how much of our everyday activities can be monitored in real-time.
Why Should You Care About IoT Devices, Especially When They Are On The Go?
The best example of why IoT is important is because it enables better security over your data. Since a lot of everything is now automated and controlled by your own phone, you no longer worry about where your data ends up. Because, according to Gartner forecasts, in 2015 more than 50% of the world will have internet access. This implies that almost one hundred percent of all data is exposed. To prevent this one is required to connect more sophisticated data protection tools and solutions for any kind of business. Therefore, the reason to start implementing proper IoT security and security solutions for your organization.
There are two main benefits that you need to keep in mind when choosing an IoT Security solution for your organization. Firstly, you should definitely consider the features these will offer you like storage and data transfer over different cloud servers, automatic updates, and updates which are highly recommended. Secondly, you may want to check if it includes encryption so that your data is kept safe. This is often essential for organizations that deal with sensitive data. With the right knowledge, it is possible to control data and make sure that your company gets to control them as well.
In order to keep track of your physical location, most IoT applications require users to install either a smartphone application like ‘geolocation’ or a desktop app like ‘
Dumb and Dumber: how smart is your monitoring data?tlevey
Big Data is all the rage right now. Everyone from a social media company to your grandmother's online knitting store is suddenly a big data shop. Application monitoring tools are no exception from this trend – they collect gigabytes of monitoring data from your application every minute. But most of this data is useless. It's dumb data. More data isn't better if the data you're getting from your tools isn't helping you do your job – in fact, it's a real problem.
In this session AppDynamics will cover how to be smarter about collecting monitoring data, and how to ensure that the data we're collecting is intelligent.
Sensors, Wearables, Wi-Fi, Video and other Technologies for Market ResearchersMike Courtney
A look at new technology for conducing marketing research. Sensors, wearable cameras, Wi-Fi and Video analytics and other emerging tools for observing and understanding consumer behavior.
Internet of Things and the Value of Tracking EverythingPaul Barsch
This presentation was given to an executive MBA session at UCSD in April 2016. The session reviewed big data, internet of things, and how companies are gaining value from location, sensor, manufacturing and other data to make better business decisions.
Similar to In IoT systems, the Security System Levels are determined by Data Classifications (20)
Root cause of Magnetic Humming due to TransformerRekaNext Capital
In Audio Design in cassettes, magnetic head picks up magnetic stray fields and cause irritating humming background noise. The 3rd harmonic of 50 Hz gets amplified when the speaker resonance coincides.
This R&D report validates the root cause. The solution was to have a physcial distance, while the current produced units had a wire loop to create a 150Hz pickup coil to phase cancellation manually tuned at PCB.
Assessment of the dynamic characteristics of the Helix Bridge at Marina Bay, ...RekaNext Capital
Modal testing was carried out to determine
the dynamic properties of the bridge. SysEng
(Singapore) Pte Ltd was commissioned
to undertake the modal testing. Professor James Brownjohn from Full Scale Dynamics Ltd was engaged by SysEng as a technical adviser for the modal testing.
Learned how to convert R&D results into a working Prototype. The PhD program was supported by a U.K. SME Industrial Scholarship from Wolf Safety Lamp Co, Sheffield to develop a Portable High Speed Turbo Generator from 55 Watts to 250 Watts within the same packaging. Starting from magnetic materials of Alnico until Rare Earth Samarium Cobalt with different Rotor Design configurations at TRL3. This project was to develop a full scale TRL5 prototype suitable for the product development launch of the Turbolite Model. The design required the development of an Efficient Electric Power Generator Model, a 2 Dimensional Magnetic Field Finite Element Method (FEM) Model from Maxwell' s Equation with Numerical Methods using Fortran IV and Development of over speed protection electronic techniques. The project was successful launched into a full scale product model by the company. In their website, it is mentioned that that product help the company to grow into an international business.
Proof of Concept project for Singapore PUB Water Reclaimation Plant to track staff for both out-door and in-door. Uses ZigBee and Triangluarization to determine position. Works fairly well, but battery consumption is not good
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
In IoT systems, the Security System Levels are determined by Data Classifications
1. Tan Guan Hong
Technology Partner
drtangh@rekanext.com
In the Digital Economy using IoT systems,
Data Classification must be designed in
4th IEEE World Forum on IoT
6 Feb 2018
1
2. Smart Nation Strategy
Smart City
Systems
Smart Citizen
Platforms
Digital
Government
Put in place the
technology and
infrastructure
(Smart Nation Platform)
Deliver better and
anticipatory services to
citizens
Empower citizens to
co-create useful
solutions
2
Data Sharing across stake holders
https://www.tech.gov.sg/Programmes-Partnerships/Programmes-Partnerships/Initiatives/Smart-Nation-Sensor-Platform
3. 3
Traditionally Classified Data is stored in a Secured Data
Centre, the data is extracted through a secured Network to
run in others servers.
Data is transaction and/or event based type
ICTRisk Management
• Data Security
• Network Security
Data Classification determines the Security Level used
No one Size fits all approach
Data
Centre
Data
CentreFirewall
Data Sharing
4. 4
ICT
Data
Centre
Data
CentreFirewall
Sensors & IoT
10,000
Sensors
10,000
Cameras
Sensors generate Data @
Value, Veracity, Volume, Velocity & Variety
Sensors operate in the Physical World and affect by Environmental
conditions
Sensor Data is Analog and streams from 1 – 10,000 data points / sec
A VGA Camera (640x480) @ 10 frames / sec streams out 3M pixel
points / sec
5. 5
• Move Sensor Data Processing into Unclassified Data
Processing Zones. (Sensor Data Acquisition and Signal
Processing domains)
• Only when Processed Information is linked to a pre-
registered CLASSIFIED Database, then only the Paired
information together is CLASSIFIED.
Traditional Data Classification Thinking:
Any Processed Data should be Classified
Cost Efficient way: Manage Security Level through
Appropriate Data Classification in System Design
6. 6
What is Data ?
What is Information ?
Classification of Information is NOT
Classification of Data at all
G1234567A
Data needs context to be Information
G1234567A
is just Alphanumeric and has
no meaning at all by itself
RFID is for a cow
Data Classification
7. 7
NRIC Identity card
Alphanumeric Meta Data
• Name
• Race
• Date of Birth
• Sex
• Country of Birth
• Date of Issue
• Residential Address
• Why should the individual Alphanumeric Metadata be classified ?
• By itself, the individual Metadata contains very general data
• When all the Metadata are linked together, the whole dataset becomes
confidential, tracible to an individual
Data Classification
7 Metadata
to identify a
unique
individual
8. 8
Number of combinations
• Name
• 10
• 30 x 12 x 100 = 36,000
• 2
• 60
• 30 x 12 x 100 = 36,000
• 3,000,000
• Imagine to trace a person, there are 10x36,000x2x60x36,000x3,000,000 possible
combinations. In that Billions combination, there is only one unique person for a
3M population.
• If we use Data Analytics, we will probably reduce this search combinations to trace
that individual
• Hence Data Accuracy and Quality is key
Data Classification
NRIC Identity card
Alphanumeric Meta Data
• Name
• Race
• Date of Birth
• Sex
• Country of Birth
• Date of Issue
• Residential Address
9. 9
Data Correctness and Data Quality
To determine your house address :-
Send 10 people to walk and to find out where is your home address. If I leave out all
other metadata , what is the probability , your address is correct ?
Even if in the same zone, they might come back with different addresses.
Why do we trust the address stated in the NRIC ?
They have Quality control process in the Data Collection system already
Then Sex : Male / Female. How was the meta data confirm ? They use Birth
Certificate information.
How was Sex determine in the Birth Certificate. In hospital by Doctor during your
birth. So there is a Quality control process in place for NRIC. But for Data for IoT,
then how do we trust the data generated for decision making ?
12. 12
Video
Analytics
Secured Ammunition Depot
Name NRIC
Unclassified information
Public Road
Data Classification
Classified information
G1234567ATan Wei Yi999 AA + +
Data
Classification is
about Information
with Context
Video
Analytics 999 AA
Unclassified information
Data Classification
13. 13
Video
Analytics
Secured Ammunition Depot
Name NRIC
Unclassified information
Classified information
Public Road
Data Classification
Classified information
G1234567ATan Wei Yi999 AA + +
+ Storage
Location
Data
Classification is
about Information
with Context
Video
Analytics 999 AA
Unclassified information
Data Classification
14. 14
IoT Sensor Data Flow
Sensor
Video camera
Gateway
Box
Data Centre
UNCLASSIFIED
Normal Security
CLASSIFIED
Higher Security
Data Fusion
Applications
Considerations
• Encryption
• Product assurance & Longevity
• Configuration Management
• Vulnerability Management
• Network Management
• Resiliency
Sensor Data
ProcessingSensor Data
Processing
Firewall Firewall
15. 15
Two-dimensional (2D) camera: These sensors capture data over time frames. Using various video
analytics algorithms, these 2D camera sensors can provide different information. For example, within
the same image, the algorithms can extract information such as (i) people count, (ii) number and
colour of cars (iii) lighting condition, etc. Over time, processed metadata can yield further insights such
as tracking of (iv) people’s movement, (v) dwell time, etc.
IoT Sensor Devices:-
Slow Sensor Data: Temperature, Humidity, Hydrostatic pressure, Strain Gauge, Tilt and Infra-red
sensors acquire data in minutes or hours. These are Quasi-static sensors.
Dynamic (Fast) Sensor Data: Accelerometer provides G m/s2 in milliseconds or faster. Acoustic
sound sensor provides voltage signals over time. When these sensor data are processed in the
Frequency Domain using Fast Fourier Transform, the data can provide Peak Vibration Level at various
Frequencies.
17. High Repeatability
High Accuracy
High Repeatability
Low Accuracy
Low Repeatability
High Accuracy
Low Repeatability
Low Accuracy
Sensor
7
Which
sensor data
do you trust
& faster to
process in
Real Time ?
19. Understanding Measurement Principle is important !
Actual
Temperature
Sampled
Temperature
Displayed
Temperature Temperature don’t
change at all !
If sample too slow
Temperature is
actually
fluctuating
Sensor
20. Understanding Measurement Principle is important !
Actual
Temperature
Sampled
Temperature
Displayed
Temperature Temperature don’t
change at all !
If sample too slow
Temperature is
actually
fluctuating
Sensor
21. Understanding Measurement Principle is important !
Actual
Temperature
Sampled
Temperature
Displayed
Temperature Temperature don’t
change at all !
If sample too slow
Temperature is
actually
fluctuating
Sensor
22. Understanding Measurement Principle is important !
Actual
Temperature
Sampled
Temperature
Displayed
Temperature
Nyquist
Frequency:-
Sample at
least Twice
the Highest
frequency
Temperature don’t
change at all !
If sample too slow
Temperature is
actually
fluctuating
Sensor
23. Accuracy of Information depends :-
Accuracy of Sensor
Maintenance & Calibration of Sensor (Function of Time, Drift, Deterioration )
Video Analytics is Processing of Image Data into Structured Information
Accuracy and Repeatability only in controlled environment
Installation of Sensor
Use of Sensor in its context (monitoring & control function)
Expected functional accuracy for decision making
ICT’s view is sensor data is stable, repeatable and maintenance free !
While an Electronics view is always drift, accuracy and noise
ICT is in Cyber World while Electronics view is deployment into
physical environment which Mother Nature controls)
Sensor
23
24. 24
Transits from Structured to Unstructured Data
In each record, it
is usually in rows
1 Data Point / Minute
1 Data Point / Sec
10 Data Points / Sec
100 Data Points / Sec
1,000 Data Points / Sec
10,000 Data Points / Sec
Velocity of Sensor Data
Structured
Data
Unstructured
Data
SQL
Sensor & IoT
data has Value,
Veracity,Volume,
Velocity & Variety
How to handle
10,000 SQL Data
Points / Sec from
just one Sensor ?
Sensor
25. 25
A Microphone Sensor measuring Voice waveform
Expand the Time scale
10,000 points
1,000 points
1 second
0.1 second
Sensor
26. 26
A Microphone Sensor measuring Voice waveform
Expand the Time scale
10,000 points
1,000 points
In Time Domain:-
Average
Root Mean Square (RMS)
Sound Pressure Level (SPL)
Maximum
Minimum
Signal/Data Processing techniques
can extract 9 Parameters
1 second
0.1 second
Sensor
In this example,
what is data and
information ?
27. 27
A Microphone Sensor measuring Voice waveform
Expand the Time scale
10,000 points
1,000 points
In Time Domain:-
Average
Root Mean Square (RMS)
Sound Pressure Level (SPL)
Maximum
Minimum
Signal/Data Processing techniques
can extract 9 Parameters
1 second
0.1 second
In Frequency Domain:-
Peak Amplitude
Peak Frequency
Harmonics
Weighted Amplitude (Curve A weighting)
Time to Frequency Domain Processing via Fast Fourier Transform
Sensor
In this example,
what is data and
information ?
28. 28
Design for Data Quality and NOT just
Availability of Data alone
Sensor
You could also be Sensing unwanted Noise!
SQL
Physical Sensor output can be affected by
Data corruption from
EMI Noise, Humidity, Temperature, Pressure,
Vibration (Lose connections)
Output of data is taken
from a Database and
usually many trust this
data !
When retrieved from SQL dB, the data is Highly
Repeatable and Accurate !
System is Auditable and Computers don’t lie ! ☺
ICTSensor & IoT
31. Accelerometer
Sensor on
Railway Track
Digitizer
Electro Magnetic Interference from
Motors, Welding Equipment, etc
Digital DataAnalogue Signals
Wanted Sensor Signal
EMI Noise
1.0 G = 0.9 G + 0.1 G
= 0.8 G + 0.2 G
Real Data Noise
Sensor
When train passes over the Railway track, it
generates 1.0 KHz vibration levels
What G number are you
actually measuring ?
Signal to Noise Ratio
31
32. Accelerometer
Sensor on
Railway Track
Digitizer
Electro Magnetic Interference from
Motors, Welding Equipment, etc
Digital DataAnalogue Signals
Use of a Spectrum
Analyzer to check the
Signal to Noise Ratio to
verify Quality of Signal
presented to the Digitizer
Wanted Sensor Signal
EMI Noise
1.0 G = 0.9 G + 0.1 G
= 0.8 G + 0.2 G
Real Data Noise
Sensor
When train passes over the Railway track, it
generates 1.0 KHz vibration levels
What G number are you
actually measuring ?
Signal to Noise Ratio
32
33. 33
Real Impact of Electro-Magnetic Interference (EMI) on
Sensor Information
Sensor
LTA Real Time
Strut Force
Readings
Load(kN)
Lunch Lunch
200 kN
Fluctuating
reduction in
Load = Weight of
15 Merc E200
34. 34
Two-dimensional (2D) camera: These sensors capture data over time frames. Using various video
analytics algorithms, these 2D camera sensors can provide different information. For example, within
the same image, the algorithms can extract information such as (i) people count, (ii) number and
colour of cars (iii) lighting condition, etc. Over time, processed metadata can yield further insights such
as tracking of (iv) people’s movement, (v) dwell time, etc.
IoT Sensor Devices:-
Slow Sensor Data: Temperature, Humidity, Hydrostatic pressure, Strain Gauge, Tilt and Infra-red
sensors acquire data in minutes or hours. These are Quasi-static sensors.
Dynamic (Fast) Sensor Data: Accelerometer provides G m/s2 in milliseconds or faster. Acoustic
sound sensor provides voltage signals over time. When these sensor data are processed in the
Frequency Domain using Fast Fourier Transform, the data can provide Peak Vibration Level at various
Frequencies.
35. 35
Using Camera as a Sensor
• Accurate & Reliable Data
• Outdoor Operating Conditions are
huge challenges
• One Camera gives many Metadata
and is a Contactless Sensor
Camera as a Sensor
37. 37
Less Measurement
Uncertainties
Every Facial Marker measurement has Uncertainty
Measurement
Uncertainties
With higher
Uncertainties, the
recognition is less
reliable. It is like
Noise is added onto
original data
Size of measurement dot indicates uncertainty range
Camera as a Sensor
Outdoor Accuracy
affected by Image
Quality and Lighting
variation
38. 38
Physical World Sensor data have Statistical Variations, while
SQL extracted data is always consistent.
Physical
Object under
measurement Sensor
Video
Analytics
Processed
into
Information
No. of
Sensing
Parameters
< 20 Facial
Sensor
Markers
1 Temperature
Reading
Each Sensing point do
have reading variations
RFID Tag
information
RFID
Reader
1 Digital
information
Sensing
Repeatability
Converts
uV to T oC
Need to
know where
are the
possible
Statistical
Sensing
Errors and
mitigate the
risks
SQL
System
usually takes
one
snapshot
reading and
stores in dB
Always
Repeatable
@ +/- 0 σ