AGNI COLLEGE OF TECHNOLOGY

DEPARTMENT OF CIVIL ENGINEERING

SUB CODE /SUB NAME :OCS352/IOT CONCEPTS

AND APPLICATIONS
UNIT V IOT APPLICATIONS
 IOT APPLICATIONS

•Business models for the internet of things,

•Smart city,
•Smart mobility and transport,
•Industrial IoT,
•Smart health,
•Environment monitoring and surveillance,
•Home Automation,
• Smart Agriculture.
BUSINESS MODELS FOR THE
INTERNET OF THINGS

Understanding the Internet of Things

Definition: Network of physical Key components: devices, Examples: Smart homes (Nest),
objects ("things") embedded with connectivity, data analytics, and wearable devices (Fitbit),
sensors, software, and other applications. industrial sensors, connected
technologies for connecting and cars (Tesla).
exchanging data with other
devices and systems over the
Internet.
Business Model : Equipment Sales

Description: Traditional Examples: Smart Advantages: Disadvantages: Limited

model of selling IoT thermostats, security Straightforward recurring revenue,
devices with a one-time cameras, smart revenue generation. commoditization.
purchase fee. appliances.
Business Model: Subscription Services

Description: Recurring revenue model where Examples: Home security systems (ADT),
customers pay a subscription fee for access to connected car services (OnStar), remote patient
IoT device functionality and data services. monitoring.

Advantages: Predictable revenue streams, Disadvantages: Requires ongoing service and

customer retention. support, customer churn risk.
Business Model: Data Monetization

Description: Collecting Examples: Usage Advantages: High- Disadvantages: Privacy

and analyzing data pattern analysis for margin revenue concerns, regulatory
generated by IoT urban planning, potential, leveraging compliance, ethical
devices, then selling environmental existing data assets. considerations.
anonymized data monitoring data for
insights to third parties. scientific research.
Business Model:
Outcome-Based
Service
Description: Customers pay based on the specific outcomes
achieved by IoT solutions, rather than the devices
themselves.

Examples: Predictive maintenance services in

manufacturing, precision agriculture improving crop yields.

Advantages: Strong value proposition, alignment with customer goals.

Disadvantages: Complex pricing models, requires accurate

outcome measurement.
Business Model:
Platform as a Service
(PaaS)

Description: Examples: AWS Advantages:

Providing a IoT, Azure IoT Hub, Scalability,
platform for Google Cloud IoT ecosystem
developers to build Platform. development,
and deploy IoT recurring revenue.
applications.

Disadvantages:
High upfront
investment,
requires strong
technical expertise.
Challenges and Future Tre

Security and Privacy: Protecting IoT devices and

data from cyber threats, adhering to privacy
regulations.

Interoperability: Ensuring seamless communication

between different IoT devices and systems.

Scalability: Managing the increasing volume of data

generated by IoT devices.

Trend: Edge computing will drive new possibilities.

 SMART CITY

What is a Smart City?

Technology-driven Citizen-centric

A city using technology and data to improve Smart cities prioritize connectivity, efficiency, and
services and operations, encompassing aspects sustainability, striving to create a more livable and
like transportation, energy, waste management, enjoyable environment for residents.
and public safety.
What is IoT (Internet of
Things)?

A vast network of physical devices embedded with

sensors, software, and connectivity, enabling data
collection, analysis, and automation.

This technology fuels smart city applications, enabling

real-time monitoring, predictive analysis, and dynamic
responses to urban challenges.
Smart Traffic Management

Data-driven Solutions Improved Efficiency

IoT sensors embedded in roads collect real-time Pilot projects have demonstrated reductions in
traffic data, enabling adaptive traffic signals that commute times by 15-20%, showcasing the
optimize flow based on congestion levels. potential to improve urban mobility.
Smart Waste Management

IoT-enabled smart bins monitor fill levels, triggering alerts

when full, optimizing collection routes, and reducing
waste collection costs by 25-40%.

This approach improves sanitation, reduces

environmental impact, and promotes resource efficiency,
making cities more sustainable.
Smart Lighting

IoT-controlled streetlights dynamically adjust brightness

based on ambient light levels and occupancy, maximizing
energy savings and reducing light pollution.
Environmental Monitoring

Air quality sensors provide real-time 1

pollution data, enabling cities to
proactively address air quality issues and
protect public health.
2 Water quality monitoring systems detect
contamination and leaks, ensuring the
safety and sustainability of water
resources.
Benefits and Challenges

Increased efficiency, improved citizen services,

enhanced sustainability, and economic growth
are key benefits of smart city IoT applications.

Challenges include data security and privacy,

interoperability of systems, infrastructure costs,
and bridging the digital divide.
Future Trends in Smart
City IoT
5G connectivity provides faster data transmission and
lower latency, enabling real-time interactions and
seamless data flow in smart cities.

Edge computing enables real-time data processing

and analysis, enhancing responsiveness and reducing
reliance on centralized servers.

AI and machine learning are transforming smart

city operations, powering predictive maintenance,
optimizing traffic flow, and enhancing citizen
services.
SMART MOBILITY AND TRANSPORT

Defining Smart Mobility and Transport

Traditional transportation systems lack connectivity Smart transportation systems utilize IoT devices,
and data integration. data analytics, and user interfaces for improved
efficiency and safety.
IoT Applications in Smart Vehicles

Real-time vehicle diagnostics and predictive Over-the-air software updates enhance vehicle
maintenance help prevent breakdowns. features and security.

Enhanced driver assistance systems increase V2X communication allows vehicles to

safety and reduce accidents. communicate with each other and infrastructure.
IoT-Enabled Smart
Infrastructure

Intelligent traffic Smart parking Connected public

management solutions simplify transit provides
systems optimize parking and reduce real-time
traffic flow and search time. information and
reduce congestion. improves efficiency.

Smart street
lighting adjusts
brightness based
on environmental
conditions.
Public Transit
Optimization with IoT

1 Real-time tracking provides passengers with

accurate arrival times.

2 Predictive maintenance optimizes bus and train schedules

3 Automated fare collection simplifies payment and

reduces wait times.

4 Integration with MaaS platforms enables seamless

multi-modal travel.
Logistics and Supply
Chain Enhancement

Real-time tracking of goods and assets ensures

visibility and efficiency.

Temperature and condition monitoring protects

perishable goods and maintains quality.

Optimized routing and delivery schedules reduce

delivery times and costs.

Predictive maintenance minimizes downtime and

maximizes fleet efficiency.
Data Analytics and Insights

Collecting and processing data from Predictive analytics improves traffic

IoT devices provides valuable flow and congestion management.
insights. 1 2

4 3
Personalized user experiences Demand forecasting optimizes
enhance customer satisfaction. public transportation capacity.
Security and Privacy Considerations

Cybersecurity threats require robust protection Data privacy regulations must be adhered to,
measures for connected transportation systems. ensuring data confidentiality and security.

Encryption and authentication protocols safeguard Secure over-the-air updates ensure ongoing
sensitive information. security and system integrity.
Challenges and
Opportunities
Interoperability and standardization are essential for
seamless integration.

Infrastructure costs and investment require careful

planning and funding.

Regulatory and policy frameworks must be

established to guide development and adoption.

Public acceptance and adoption is crucial for

widespread impact.
INDUSTRIAL IOT

What is Industrial IoT (IIoT)?

Definition Key Components

IIoT leverages IoT technologies to improve efficiency, Sensors, connectivity (e.g., cellular, LoRaWAN), cloud
productivity, and operations in industrial sectors. platforms, data analytics, and machine learning.
IIoT Application:
Predictive
Maintenance
1 Unplanned downtime 2 IIoT-enabled sensors
costs manufacturers an monitor equipment to
estimated $50 billion detect anomalies
annually. indicating potential
failures.

3 Reduces downtime by up to 30% and maintenance costs

by up to 25%.
IIoT Application:
Smart Manufacturing

Concept

Integrating IoT sensors, data analytics, and automation to

create a highly efficient and responsive manufacturing
environment.

Benefits

Improved production efficiency, reduced waste, faster

response to changing market demands, and enhanced
product quality.
IIoT Application:
Supply Chain
Optimization

Track and trace goods in real- Monitor temperature and

time using GPS, RFID, and humidity to ensure product
other sensor technologies. integrity.
IIoT Application:
Smart Agriculture

1 Precision Farming

Using sensors to monitor soil moisture,

temperature, and nutrient levels to optimize
irrigation and fertilization.

2 Livestock Monitoring

Tracking animal health and behavior using

wearable sensors.
IIoT Application:
Energy Management

Deploy smart meters and sensors to monitor

energy usage in real-time.

Identify inefficiencies and automate energy

management systems.

Reduce energy consumption and lower carbon emissions.

Challenges of IIoT
Implementation
Data Security

Protecting sensitive data from cyber threats.

Interoperability

Ensuring compatibility between different devices and systems.

Skills Gap

Finding and training personnel with the necessary expertise.

Initial Investment

Justifying the upfront cost of IIoT implementation.

Overcoming the Challenges

Data Security
Interoperability
Implement robust security 1
Adopt open standards and
measures, including encryption, 2
protocols to facilitate seamless
access controls, and regular
integration.
security audits.

Initial Investment 4 Skills Gap

Start with small-scale pilot 3 Invest in training programs to

projects to demonstrate the develop the necessary skills.
value of IIoT.
SMART HEALTH

Core Components of Smart Health IoT

Wearable Sensors Remote Mobile Apps Cloud Platforms

Monitoring
Track vital signs, Devices Interface for data Secure storage, data
activity levels, sleep Transmit data from visualization, analytics, and
patterns (e.g., Fitbit, home to healthcare communication, and interoperability.
Apple Watch). providers (e.g., blood personalized insights.
pressure monitors,
glucose meters).
Use Case: Remote Patient Monitoring (R

Proactive Care Improved Outcomes

Continuous monitoring of patients with chronic RPM programs reduce hospital readmissions by
conditions (e.g., diabetes, heart failure) at home. 25% and improve patient satisfaction by 40%
(Health Affairs).
Use Case:
Medication
Adherence
1 Smart pill bottles, 2 Real-time tracking of
wearable reminders, medication intake and
and digital apps to alerts for missed doses.
improve medication
adherence.

3 Digital interventions improve adherence rates by 50%

in chronic diseases (Journal of Medical Internet
Research).
Use Case: Chronic Disease Managemen

Integrated Solutions Personalized Insights

Manage diabetes, hypertension, asthma, and Coaching and support through mobile apps and
other chronic conditions. wearable devices.
Benefits of Smart
Health IoT

Improved Patient Reduced Increased Patient

Outcomes: Healthcare Costs: Engagement:
Proactive Fewer Empowering
monitoring, early hospitalizations, patients to take
intervention, and emergency room control of their
personalized care. visits, and health.
unnecessary
procedures.
Security and Privacy
Challenges

1 Protecting sensitive patient data from cyber threats.

2 Ensuring compliance with HIPAA and other

regulations.

3 Implementing robust security measures:

encryption, access controls, and regular audits.
Interoperability and
Standards

Need for seamless data exchange between

different IoT devices and healthcare systems.

Adopting industry standards such as HL7 FHIR

and IHE profiles.

Promoting open APIs and data sharing agreements.

Future Trends in
Smart Health IoT
AI and Machine Learning: Predictive analytics,
personalized recommendations, and automated
diagnostics.
5G Connectivity: Faster data transmission, lower
latency, and improved reliability.

Edge Computing: Processing data closer to the

source for real-time insights.

Blockchain Technology: Secure and transparent

data sharing, supply chain management, and
identity verification.
 ENVIRONMENTAL MONITORING
AND SURVEILLANCE
The Environmental Challenge: Why Mon

Quantifiable Data Specific Concerns

Understanding the severity of pollution levels, Beyond overall trends, specific environmental
including air, water, and noise pollution, is vital for concerns like deforestation rates, species
targeted interventions. WHO reports over 7 million extinction, and the impacts of climate change
deaths annually due to air pollution, and the EPA require immediate attention. For example, the
tracks over 10,000 water contamination incidents Great Barrier Reef has lost over 50% of its coral
in the U.S. each year. since 1998 due to rising ocean temperatures.
IoT Solution: Real-Time Environmental M

Sensor Functionality Data Transmission

IoT sensors work by collecting data on various Sensors transmit data to a gateway using various
environmental parameters like air quality (PM2.5, methods such as LoRaWAN, cellular networks, or
ozone), water quality (pH, dissolved oxygen), and satellite connections. LoRaWAN, for instance,
weather (temperature, humidity). offers a range of up to 10 kilometers with a
battery life of up to 10 years.
Key IoT Technologies for
Environment
Surveillance
1 Sensors
Electrochemical sensors (accuracy ±2%), optical sensors, and
acoustic sensors capture precise environmental data.

2 Communication
LPWANs (LoRaWAN, Sigfox), NB-IoT, and 5G (low latency)
enable reliable data transmission across vast distances.

3 Data Analytics
Edge computing enables real-time data processing, while
cloud-based AI/ML facilitates predictive modeling.

4 Power
Solar power and long-lasting batteries (up to 10 years) ensure
continuous sensor operation. Energy harvesting is also being
explored.
Benefits of IoT-Based Environment Mon

Real-Time Data Cost Efficiency Improved Accuracy

Gain immediate insights for Reduce reliance on manual Precise data from calibrated
timely action, allowing for monitoring and labor costs, sensors minimize human
rapid responses to leading to significant savings error, leading to more
environmental changes. (up to 40% cost reduction). accurate environmental
assessments (25% accuracy
improvement over manual
methods).

Predictive Analysis Remote Monitoring

Anticipate potential environmental hazards like Access data from anywhere, empowering
pollution spikes or flood risks, enabling environmental managers to take proactive
proactive mitigation measures. action regardless of location.
Case Study 1: Smart City Air Quality
Monitoring (Example: London)

Problem Solution Results

London faced high levels of air A network of over 500 air quality Real-time pollution maps were
pollution, posing health risks to its sensors was deployed throughout created, identifying pollution
residents. London, providing real-time data. hotspots. This data-driven approach
led to policy decisions that reduced
pollution in key areas by 15%. Clarity
Movement's air quality monitors
were deployed city-wide.
Case Study 2: Precision
Agriculture for Water
Management (Example:
California)
1 Problem

California grappled with water scarcity and inefficient irrigation

practices, leading to water waste and reduced crop yields.

2 Solution

Over 1000 farms in California adopted IoT solutions, deploying

soil moisture sensors and weather stations to optimize
irrigation.

3 Results

Optimized irrigation schedules led to a significant reduction in

water consumption (20-30% water savings) and increased crop
yields (10-15% yield increase). Sentek soil moisture probes were
used to measure volumetric water content.
Challenges and Mitigation Strategies

Data Security

Encryption and secure communication protocols like AES-256 encryption ensure data integrity and confidentiality.

Scalability

Choosing the right network infrastructure and utilizing edge computing solutions (LoRaWAN can support
thousands of devices per gateway) address scalability challenges.

Sensor Calibration & Maintenance

Regular calibration and remote diagnostics (annual calibration budget of 5% of sensor cost) ensure accurate and

Power Management

Energy-efficient sensors and solar power integration (extending sensor life by 2-3x) address power management

Data Overload

Data filtering and edge processing techniques effectively reduce data volume by 60% to streamline data manage
The Future of IoT in
Environmental
Surveillance
AI and Machine Learning

Integration with AI and machine learning will enable predictive

modeling for proactive environmental management.

Drone and Satellite Imagery

Drones and satellite imagery will be used for large-scale

environmental monitoring, reducing costs by 50% compared
to manned aircraft.
Emerging Technologies

New sensor technologies are being developed to address

emerging environmental threats, such as microplastics
detection.
Global Adoption

Governments and businesses are increasingly adopting

IoT solutions for environmental monitoring and
sustainability initiatives.
 SMART HOMES

The Core Technologies Behind Smart Ho

IoT Devices Connectivity Cloud Platforms Mobile Apps

Smart sensors, Wi-Fi, Bluetooth, Centralized data User interface for

actuators, and Zigbee, and Z-Wave processing, storage, controlling and
controllers like smart provide and remote access monitoring devices via
thermostats, lights, communication using platforms like smartphones, with
and door locks, enable between devices, with AWS IoT, Google Cloud 80% of users
automation and Z-Wave offering low IoT, and Azure IoT Hub. interacting via mobile
remote control. latency for reliable apps (Statista).
control.
Use Case 1: Smart
Lighting and Energy
Management

Automated Smart thermostats Real-time energy

lighting schedules learn user consumption
based on preferences and monitoring and
occupancy and adjust alerts provide
ambient light can temperature valuable insights
save 30-40% accordingly, into energy usage.
energy (U.S. saving 10-12% on
Department of heating and 15%
Energy). on cooling (Nest).
Use Case 2: Home
Security and
Surveillance
Smart security systems with motion detectors,
door/window sensors, and surveillance cameras offer
enhanced security and peace of mind.

Remote monitoring via smartphone allows users to check

on their homes from anywhere.

Integration with emergency services provides rapid

response in case of an emergency.

Facial recognition and advanced AI capabilities enable

object detection and anomaly detection, further enhancing
security.
Use Case 3:
Entertainment and
Automation
1 Smart TVs, voice-controlled speakers, and multi-room
audio systems enhance the entertainment experience.

2 Voice assistants, like Amazon Echo and Google Home

(70% market share), control devices and access
information.

3 Personalized entertainment experiences are made

possible through user preferences and data analysis.

4 Automated blinds and shades enhance comfort and

energy efficiency.
Security and Privacy
Considerations

IoT devices are vulnerable to hacking and data

breaches with 20% having known security flaws
(Ponemon Institute).

Strong passwords, firmware updates, and network

security are crucial for mitigating risks.

Data encryption and privacy policies, including

GDPR compliance, are essential for protecting user
information.

User awareness and best practices are key to

reducing vulnerabilities and protecting privacy.
The Future of Home
Automation
AI and Machine Learning will enable predictive
maintenance, personalized experiences, and adaptive
automation.
5G connectivity will offer faster speeds, lower latency,
and increased device capacity.

Integration with other smart devices and services

will create a truly interconnected ecosystem.

Potential applications in healthcare, elderly care,

and accessibility will improve quality of life.
Getting Started with Home Automation

1
Assess Needs

Identify your specific requirements and goals.

2
Choose Devices

Select devices and platform that align with your needs.

3
Installation

Consider professional installation or DIY options.

4
Budget

Set a realistic budget based on your chosen devices and features.

SMART AGRICULTURE

Key IoT Applications in Agriculture

Precision Farming Livestock Farm Automation Supply Chain

Monitoring Optimization
Sensors monitor soil Autonomous tractors
conditions, weather, Wearable sensors and drones reduce Tracking and tracing of
and crop health. Real- track animal health, labor costs and agricultural products
time analytics optimize behavior, and location. increase efficiency. from farm to consumer
irrigation, fertilization, Early disease Robotic harvesting improve transparency
and pest control. detection and systems improve and food safety.
automated feeding productivity.
improve productivity.
Precision Farming in Detail: Optimizing
Crop Management

1 Soil Moisture Sensors 2 Weather Stations 3 Drones and Aerial

Imagery
Real-time data helps Localized weather data
optimize irrigation enables proactive measures High-resolution images
schedules, reducing water against frost, heat stress, provide insights for crop
consumption by up to 30%. and disease outbreaks, health assessment, weed
increasing yields. detection, and yield
mapping, optimizing
fertilizer application.
Livestock Monitoring: Ensuring
Animal Welfare and Productivity

Wearable Sensors GPS Tracking Smart Feeding Systems

Monitoring vital signs, activity Real-time location tracking of Automated feeding based on
levels, and rumination livestock prevents theft and individual animal needs
patterns enables early disease loss, optimizes grazing improves feed conversion
detection and optimized management, and reduces ratio, reduces waste, and
feeding strategies. labor costs. minimizes environmental
impact.
Farm Automation: Enhancing Efficiency
and Reducing Labor

GPS-guided autonomous Robotic harvesting Smart irrigation systems Drones monitor crops
tractors perform precise systems gently handle use real-time sensor autonomously and apply
planting, spraying, and fruits and vegetables, data for optimized water pesticides strategically,
harvesting, reducing increasing harvesting use and reduced labor reducing chemical usage
labor costs and speed and reducing costs, improving crop and minimizing
improving efficiency. labor costs. yields. environmental impact.
Data Analytics and
Decision Support

1 Data Collection

IoT sensors generate massive amounts of data. Cloud-

based platforms provide scalability and accessibility for
data management and storage.

2 Data Processing

Advanced analytics algorithms transform raw data into

actionable insights. Machine learning models predict
crop yields, disease outbreaks, and market trends.

3 Decision Support Systems

User-friendly dashboards and mobile apps provide real-

time information and recommendations, enabling
farmers to make informed decisions.
Challenges and Opportunit

High Initial Investment

Cost of sensors, infrastructure, and software can be a

barrier. Financing options and government subsidies can
help.

Data Security and Privacy

Protecting sensitive data from cyberattacks is crucial.

Robust security measures and data encryption are
essential.

Connectivity Issues

Reliable internet connectivity is required. Rural areas

often lack adequate infrastructure. Satellite-based
solutions can provide connectivity in remote locations.
The Future of Smart Agriculture

The future of agriculture is data-driven, automated, and connected. Embrace smart agriculture to create a
more sustainable and efficient food system for the future.
THANK YOU !