IoT Industry Adaptation of AI.ppt

IoT Industry Adaptation of AI
Dr.M.Pyingkodi
Dept of MCA
Kongu Engineering College
Erode,Tamilnadu,India

IoT Industry Adaptation of AI
AI uses machine learning and deep learning to better analyses data and make
decisions.
• To production data to improve failure prediction and maintenance planning.
• Preventing Future Problems
• More accurate demand forecasting and less material waste.
• Preventing future problems – equipment functioning.
• Forecasting of raw material price.
• Quality control.
• Creative Generating
ML algorithms are employed to mimic the design process utilized by engineers.
Using this technique, manufacturers may quickly produce hundreds of design options
for a single product.
• Robotics
Dr.M.Pyingkodi, Assistant Professor(Sr.G), MCA Department, Kongu Engineering College, Erode, Tamilnadu, India

AI and IOT
• AI enables the IOT device to use gathered big data to better analyze learn and make decisions
without the need for a human.
• To create more efficient IOT operations to improve human machine interactions and enhance data
migration and analytics.
AI – Simulation of human intelligence processing by machine.
Example
natural language processing
Speech recognition
Machine vision.
Applications
 Smart cities
 Smart retail
 Smart home
 Enterprise and Industrial
 Social media and human resources
 Autonomous delivery robots
 Healthcare
 Robots in manufacturing
 Self-driving car
 Retail analytics

IOT and AI – Industry 4.0
Industrial Internet of Things(IIOT) and Cyber Physical Systems
Smart, autonomous systems – Uses
Computer-Based Algorithm to Monitor and Control Physical Things like,
Machinery
Robots
Vehicle
Industry 4.0
Manufacturers are integrating new technologies, including
Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their
production facilities and throughout their operations.
smart factories are equipped with advanced sensors, embedded software and robotics that collect
and analyze data and allow for better decision making

Industry 4.0 Technologies
ERP
• Business process management tools
• Manage info across an organization
IoT
Connections between physical objects like sensors/machine with internet
IIoT
Industrial Internet of Things
Connections between people, data and machines – related to manufacturing.
Big Data
• Large set of structured/unstructured data
• Compiled, stored, organized and analyzed to reveal patterns, trends, associations and
opportunities.
Artificial Intelligence
• To a computer’s ability to perform tasks.
• Make decisions
• Historically require some levels of human Intelligence.

M2M
• Machine to Machine
• Communication – between two separate machines through wireless/wired network.
Digitization
The process of collecting and converting different types of information into digital format.
Smart Factory
Invests in and leverages industry 4.0 technology, solutions and approaches.
Machine Learning
• Ability that computers have to learn.
• Improve on their own through AI.
Cloud Computing
To the practice of using interconnected remote servers hosted on the Internet to Store,
Manage and Process Information.
Real-time data processing
Abilities if computer systems and machines to continuously and automatically process data.
Provide real time / near time outputs and insights.

Ecosystems
Entire Operations performed in the industry
Inventory planning
Financials
Customer relationships
Supply chain management
Manufacturing execution
Cyber Physical Systems (CPS)
Industry 4.0 – enabled manufacturing environment offers
Real time data collection
Analysis
Transparency across every aspect of manufacturing operation.

Benefits Of AI For IoT
1.Reduction in Downtime
Downtime- to the period of time in which a company's factory is not producing product.
• Increase productivity, lower costs decrease accidents.
• Production - series of processes
• One process is dependent on another process.
Snowball effect
If one process is down /delay serving other dependent processes so in a waiting stage.
• Factory- is not producing anything until that single process is fixed.
• Effect the markets where products are out of stock due to the failure
• Find ways to reduce this downtime.
AI Works For
• Trigger can be given by AI Self-diagnostic tests
• Sending emergency alerts to the Technicians
• Reassigning
• Resetting time line for other dependent processes
• Alerting the entire supply chain about the process change.

Benefits of AI for IoT
2. Preventative measures to reduce downtime
With AI assist – prevent downtime
1. ML
Analyzing data generated by these machines and train them
Taking preventing actions given through triggers
2. Identify maintenance requirements
performance maintenance
3. Identify patterns – causes disruption and schedule predictive maintenance
Predictive Maintenance
Minimizing downtime in production
Uses data collected from all machinery
AI solutions analyze incoming data and monitors all machines in manufacturing
Causes of Downtime
• Inefficient processes
• Human error
• Supply chain disruptions
• Inaccurate maintenance

3. Operational Efficiency
By reducing downtime and using preventative measures
-achieve operation efficiency
Predictive analysis on supply and demand side.
based on- AI Defect
• Historical data
• Current market conditions
• Political stability
• International market influence
• Catastrophic events occur in other parts of world
Catastrophic events -involving /causing sudden great damage/suffering Unfortunate
events.
These predictions used
Schedule a production capacity
Purchase of raw material
Plan workforce
Arrange transport capacity etc.,

4. Increased Risk Management
Organization struggling for not able to predict risks in future.
• Understanding of the data in hand
• Ability to decode various internal/external factors – May effect the organization
AI - trigger a rapid response
- prevent the large losses
5.New product and services
Use of AI – open up new opportunities to launch new products and services

Use of AI in IoT with Real Life Example : IIOT
Industrial Internet of Things devices in manufacturing are in automation, remote monitoring, supply
chain optimization, digital twins, and predictive maintenance.
• Increase productivity and uptime.
• Improve process efficiencies.
• Accelerate innovation.
• Reduce asset downtime.
• Enhance operational efficiency.
• Create end-to-end operational visibility.
• Improve product quality.
• Reduce operating costs.
• Optimize production scheduling.
• Improve overall equipment effectiveness (OEE).

Use of AI in IoT with Real Life Example : Smart Farming
• Built for monitoring the crop field with the help of sensors (light, humidity, temperature, soil moisture, crop
health, etc.) and automating the irrigation system.
• Location systems like GPS and Geographical Information Systems (GIS) and Satellite Imagery
• Sensors for monitoring humidity, water levels, Soil Ph, Sunshine, and temperature
• Agriculture specific software that merges agronomy and cybernetic to make farm management hassle-free
• Communication via Cellular IoT solutions and Low-power wide-area networks (LPWANs)
• Data Analysis systems that provide farmers real-time data on crop and animal health
Sensors -Soil, water, light, humidity, temperature management
Software - Specialized software solutions that target specific farm types or applications agnostic IoT platforms
Connectivity: cellular, LoRa
Location: GPS, Satellite
Robotics: Autonomous tractors, processing facilities
Data analytics: standalone analytics solutions, data pipelines for downstream solutions
Applications:
Precision Farming - IoT-based approaches that make farming more controlled and accurate
Precision Livestock Farming – Monitors
Automation in Smart Greenhouses
Agricultural Drones

use of AI in IoT with Real Life Example : Self driving Vehicles
Sense the road, map the road, negotiate your place on the road.
IoT can connect all types of device to the Internet to share information and use added-value. Autonomous
vehicles are thus connected to share information from the on-board sensors, as well as from smart phones of
pedestrians and cyclists, traffic sensors, parking detectors, etc.
Acoustic Sensors
used to collect sound, pressure and vibration data
Ultra Sonic Sensor
Ultra Sonic sensors are most suitable for shorter range.
RADAR Sensor
electromagnetic waves in the radio spectrum frequency.
to measure the distance of objects over wide distances.
LiDAR
LiDAR stands for Light detection and ranging.
easuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor
CAMERA
Camera can detect traffic signs, Traffic lights, pedestrian movement, lane markers, and temperature in case of
thermal camera’s.
GPS
GPS sensors are receivers with antennas that use a satellite-based navigation system with a network of 24
satellites in orbit around the earth to provide position, velocity, and timing information

Use of AI in IoT with Real Life Example : HealthCare
Improving Diagnostic Accuracy
Remote Patient Monitoring
advantage of IoT sensors' internet connectivity to provide doctors and nurses with updates on patient
vitals.
Reducing Need For Follow-Up Visits
track patients' health once they leave the hospital, reducing the need for follow-up visits.
Reducing Wait Times
Identifying Critical Patients
Tracking Medical Equipment

IoT Industry Adaptation of AI.ppt

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