INTRODUCTION TO INTERNET OF THINGS

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ECE-429T

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MADE BY-

Dr. Priyanka Gupta �Dr. Monica Bhutani

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It is a system of interrelated, internet-connected objects which are able to collect and transfer data over a wireless network without human intervention. For example, smart fitness bands or watches, driverless cars or drones, smart homes that can be unlocked through smartphones and smart cars, etc.

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What is IoT?

The Internet of Things (IoT) is the network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data with other devices and systems over the internet.

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"Things," in the IoT sense, refer to devices such as

• heart monitoring implants
• biochip transponders on farm animals,
• electric clams in coastal waters,
• automobiles with built-in sensors
• DNA analysis devices for environmental/food/pathogen monitoring
• field operation devices that assist firefighters in search and rescue operations.

These devices collect useful data with the help of various existing technologies and then autonomously flow the data between other devices.

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Historical Background

• In mid 1982 the idea of the system of shrewd gadgets was talked about, with an adjusted Coke machine. This coke machine is adjusted at "Carnegie Mellon University" and turning into the primary Internet-associated apparatus. This machine had the option to report its stock and whether recently stacked beverages were cold.

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• In 1994 Reza Raji clarified the possibility of IoT as "little parcels of information to an enormous arrangement of hubs, to coordinate and mechanize everything from home machines to whole processing plants". After that numerous organizations proposed different arrangements like Microsoft's at Work or Novell's Nest. Bill Joy proposed Device to Device (D2D) correspondence as a piece of his "Six Webs" structures at the World Economic Forum at Davos in 1999.

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In 1999 British technology pioneer Kevin Ashton, co-founder of the Auto-ID Laboratory at MIT, invented the term "The Internet of Things" to describe a system where the Internet is connected to the physical world via ubiquitous sensors, including RFID (Radio-frequency identification).

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Radio-frequency identification (RFID) was seen as a prerequisite for the IoT at that point.

• RFID uses radio waves to identify people or objects.
• It can be found in car keys, employee identification, medical history/billing, highway toll tags and security access cards.

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Vision, Definition, and Conceptual Framework of IoT

Vision: The Internet of Things (IoT) envisions a world where physical devices, systems, and infrastructures are seamlessly integrated into the digital domain, enhancing connectivity and automation. Devices embedded with sensors, actuators, and software communicate with each other, collect and analyze data, and make intelligent decisions with minimal human intervention. The goal is to create a more efficient, automated world that improves productivity, safety, and quality of life.

Definition: The IoT refers to a network of physical objects ("things") embedded with sensors, software, and technologies to collect and share data over the Internet. These objects range from consumer products like wearables and smart home devices to industrial systems like manufacturing equipment and healthcare devices.

Conceptual Framework:

• Perception Layer: Collects data from the environment using sensors, RFID, GPS, etc.
• Network Layer: Transmits data through Wi-Fi, Bluetooth, Zigbee, and 4G/5G networks.
• Application Layer: Processes data to deliver services like smart home automation, industrial control, or healthcare.
• Middleware Layer: Handles data processing and service orchestration across systems.

Conceptual Framework of IOT

Physical Object + Controller, Sensor and Actuators + Internet = Internet of Things (1.1)

Gather + Enrich + Stream + Manage + Acquire + organize and Analyse = Internet of Things Enterprise & Business Applications, Integration and SoA (1.2)

Gather + Enrich + Stream + Manage + Acquire + organize and Analyse = Internet of Things Enterprise & Business Applications, Integration and SoA (1.3)

IoT Conceptual Framework

An IOT reference model CISCO conceptual framework

Oracle’s IoT Architecture

Conceptual Framework and Architectural view

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Three-layer Architecture

The three-layer architecture is a foundational structure for IoT systems, simplifying how data is collected, transmitted, and processed. It includes three main layers:

• Perception Layer (Sensing Layer):
• Function: This is the physical layer responsible for collecting real-time data from the environment using various IoT devices such as sensors, RFID tags, GPS, and actuators.
• Components: Includes sensors for measuring parameters like temperature, motion, light, pressure, humidity, sound, or chemical composition.
• Role: Devices at this layer interact with the physical world and gather raw data.
• Example: A smart thermostat's temperature sensor detects ambient temperature and sends it to the next layer for processing.

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• Network Layer (Data Transmission Layer):
• Function: Responsible for transmitting the data collected by the perception layer to other systems or platforms, such as cloud services, databases, or other devices.
• Components: Communication technologies like Wi-Fi, Bluetooth, Zigbee, 4G/5G cellular networks, LoRa, or wired technologies (Ethernet). It also includes gateways, routers, and switches for routing data.
• Role: Ensures reliable and efficient communication of data between IoT devices and processing systems.
• Example: A smart thermostat sends the temperature data over Wi-Fi to a cloud platform for storage or analysis.
• Application Layer:
• Function: Processes the data collected and transmitted by the previous layers to provide meaningful services or insights to users or systems.
• Components: Software and applications designed to perform data analytics, reporting, or decision-making based on the received data.
• Role: This layer is use case-specific, meaning it delivers services based on the nature of the IoT system. For instance, this could involve monitoring, control systems, or alerting systems.
• Examples:In a smart traffic system, the application layer processes data from traffic cameras and sensors to adjust traffic lights based on real-time conditions.

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Five-layer Architecture

• Perception Layer (Sensing Layer):
• Same as the three-layer architecture, this layer is responsible for collecting raw data through sensors and devices.
• It interacts directly with the physical environment.
• Network Layer (Data Transmission Layer):
• As in the three-layer model, this layer transmits data to the required destinations using communication protocols.
• Processing Layer (Data Aggregation & Analytics):
• Function: This layer is responsible for storing, processing, and managing the data collected from the perception layer. It transforms raw data into valuable insights.
• Components: Data centers, cloud platforms (like AWS IoT, Azure IoT Hub), edge computing systems, big data processing systems, and databases.
• Role: It may employ techniques like data filtering, analytics, machine learning, or AI algorithms to derive patterns and trends from the data.
• Example: In smart manufacturing, the processing layer might run predictive maintenance algorithms that analyze machine data to predict when equipment needs servicing, preventing unexpected breakdowns.
• Edge Computing: Often, processing happens at the "edge" (closer to the source of data) rather than in a central cloud system, which reduces latency and bandwidth usage. For example, a factory sensor might process data locally to detect anomalies in machinery performance.

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• Application Layer:
• Same as in the three-layer architecture, this layer is responsible for providing services to end users based on the data processed by the previous layers.
• The application layer might range from monitoring dashboards, automation systems, or even real-time decision-making tools.
• Example: In a smart agriculture system, the application layer might trigger automated irrigation based on moisture sensor data processed by the system.
• Business Layer:
• Function: This layer governs the business models, strategies, and policies that dictate how IoT systems function, ensuring they align with business objectives. It provides business logic and decision-making processes for interpreting the data processed in the previous layers.
• Components: Business applications, management systems, decision-support systems, and dashboards.
• Role: It determines business-related outcomes like improving operational efficiency, reducing costs, optimizing performance, or creating new revenue streams.
• Example: In a smart city context, the business layer could generate reports that highlight energy savings from smart streetlights and suggest further energy optimization strategies based on the insights.

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Wearable Devices: Fitness trackers (Fitbit), smartwatches (Apple Watch) track health metrics.

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Smart Homes: Devices like Nest thermostats, Philips Hue lighting, and Amazon Echo automate household tasks.

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Industrial IoT (IIoT): Smart factories use sensors to monitor machines, enabling predictive maintenance.

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Smart Cities: IoT devices manage traffic, energy consumption, and public safety.

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Healthcare: IoT enables patient monitoring through connected devices (e.g., glucose monitors, heart rate sensors).

Sources of the IoT

Sensors and Actuators:

• Sensors: Collect environmental data (e.g., temperature, motion, camera images).
• Actuators: Perform actions (e.g., open valve, adjust thermostat).

Communication Protocols:

• MQTT: Low-bandwidth protocol for M2M communication.
• CoAP: Lightweight web protocol for constrained environments.
• HTTP/HTTPS: Standard web protocols.
• WebSockets: Full-duplex communication for real-time applications.

Edge Computing: Reduces latency by processing data closer to where it's generated (on devices or local gateways).

Technology behind IoT

Smart Homes

• Smart home devices enable users to control various functions through apps or voice commands, enhancing convenience and energy efficiency. Features like scheduling allow lights to turn on and off automatically.

Wearables

• Beyond basic activity tracking, wearables can monitor heart rate, sleep patterns, and stress levels. Advanced models may include ECG monitoring and SpO2 sensors for a comprehensive view of health.

Connected Vehicles

• Connected vehicles utilize telematics to monitor driving behavior, fuel efficiency, and vehicle health, providing drivers with actionable insights and alerts. seamless integration with smartphones, allowing access to apps, navigation, and music

Smart Agriculture

• Utilizing data analytics and IoT sensors, farmers can make informed decisions about planting, fertilizing, and harvesting, optimizing yields and reducing waste.

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IOT Examples

IOT Examples

M2M Communication (Machine-to-Machine Communication)

refers to the automated exchange of information between devices without human intervention. It is the foundation of the Internet of Things (IoT), enabling devices to share data and take actions based on the information they receive. M2M communication relies on sensors, embedded systems, communication networks, and software to allow devices to communicate with each other directly.

Key Aspects of M2M Communication:

• Autonomous Data Exchange:
• M2M communication enables devices to autonomously collect and exchange data with other machines or central systems. This communication can occur through wired or wireless networks.
• Devices, such as sensors and actuators, generate data, which is then transmitted to another device for processing, analysis, or response.
• Real-Time Data Transmission:
• M2M communication ensures real-time data transmission, allowing machines to react instantly to changes in the environment or system conditions.
• This is crucial in applications such as industrial automation, where quick responses to data changes improve efficiency and safety.

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• Protocols for M2M Communication:
• M2M communication uses specific protocols optimized for low-power, constrained environments. Common protocols include:
• MQTT (Message Queuing Telemetry Transport): A lightweight protocol for low-bandwidth environments, often used in IoT applications for real-time messaging between devices.
• CoAP (Constrained Application Protocol): A web transfer protocol designed for resource-constrained devices, using minimal resources for communication.
• HTTP/HTTPS: Though not as resource-efficient, these protocols are sometimes used in M2M communication for secure data transmission, especially in web-based applications.

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• Data Processing and Control:
• M2M communication often leads to automated control of devices. For example, a sensor monitoring the temperature in a room can send data to an HVAC system, triggering an automatic adjustment to maintain optimal conditions.
• Similarly, in industrial settings, M2M communication allows machines to coordinate their operations, ensuring seamless production processes without human intervention.

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• Communication Channels:
• M2M communication can occur through various communication channels:
• Wired: Ethernet, fiber optics, or serial connections are used in environments where secure, high-speed communication is essential.
• Wireless: Wi-Fi, Bluetooth, Zigbee, LoRa, 4G/5G, and other wireless communication technologies enable data exchange between remote or mobile devices.

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• Examples of M2M Communication:
• Smart Grid: Energy meters connected to the grid can communicate energy usage in real-time, helping power companies optimize energy distribution.
• Vending Machines: Connected vending machines can send notifications about stock levels or maintenance needs to suppliers, ensuring timely refills and repairs.
• Healthcare: Medical devices can send patient health data, like heart rate or glucose levels, to monitoring systems that alert healthcare providers in case of abnormalities.
• Industrial Automation: Sensors on factory machinery communicate maintenance needs or performance data to a central system, allowing for predictive maintenance and reduced downtime.

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Advantages of M2M Communication:

• Efficiency:
• M2M communication allows devices to make decisions in real-time without waiting for human intervention, leading to faster responses and improved operational efficiency.
• Scalability:
• M2M systems can scale easily as more devices are added to the network. With cloud computing integration, millions of devices can communicate and process data simultaneously.
• Cost Savings:
• Automated M2M systems reduce labor costs by enabling remote monitoring, diagnostics, and management of systems and devices without requiring on-site personnel.
• Predictive Maintenance:
• Through continuous monitoring and data exchange, M2M communication enables predictive maintenance, where machines can schedule repairs before a failure occurs, reducing downtime and improving equipment lifespan.
• Better Decision-Making:
• The vast amount of real-time data generated by M2M communication helps businesses and systems make informed decisions. This is particularly beneficial in sectors like healthcare, manufacturing, and energy management.

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Challenges in M2M Communication:

• Interoperability:
• Different machines and devices often use varied communication protocols and technologies, leading to interoperability challenges. Standardization efforts are essential to address this issue.
• Security:
• As M2M communication happens autonomously and across networks, it is vulnerable to cyber-attacks. Ensuring robust security protocols, such as encryption and authentication, is critical.
• Data Management:
• With the large volume of data generated by M2M communication, it is necessary to have efficient data management systems.

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IoT/M2M systems layers and design standardization

Modified OSI Model for the IoT/M2M Systems

Principles for Connected Devices: IoT/M2M systems layers and design standardization

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Key Design Principles

• Interoperability:
• Devices should be able to work seamlessly with other devices and systems. This can be achieved through standardized communication protocols and data formats.
• Scalability:
• The system should be able to grow without requiring a complete redesign. This includes supporting a growing number of devices and increased data throughput.
• Security:
• Security should be integrated at all layers, including data encryption, secure authentication, and device identity management. Regular updates and vulnerability assessments are also crucial.
• Energy Efficiency:
• Devices should be designed for minimal energy consumption, which is especially important for battery-operated devices. Techniques include low-power communication protocols and efficient data handling.
• Usability:
• User interfaces and interactions should be intuitive. This encompasses the design of both physical devices and software applications.

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Principles for Connected Devices: IoT/M2M systems layers and design standardization

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System Layers

• Device Layer:
• This is the physical layer, consisting of sensors, actuators, and embedded systems. Devices capture data from their environment and can perform actions based on received commands.
• Connectivity Layer:
• This layer is responsible for communication between devices and the network. It includes various protocols such as MQTT, CoAP, HTTP/HTTPS, and network technologies like Wi-Fi, Bluetooth, Zigbee, LoRa, and cellular networks.
• Edge Computing Layer:
• Edge devices process data locally to reduce latency and bandwidth usage. This layer allows for real-time decision-making and can filter and aggregate data before sending it to the cloud.
• Cloud Layer:
• The cloud layer stores and processes data from multiple devices. It often includes data analytics, machine learning models, and the user interface for managing devices and visualizing data.
• Application Layer:
• This layer consists of the software applications that users interact with. These applications provide functionality, analytics, and control over the connected devices.

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Principles for Connected Devices: IoT/M2M systems layers and design standardization

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Design Standardization

• Protocols and Standards:
• Common standards like MQTT, CoAP, and HTTP facilitate communication between devices and services. Standardization helps ensure interoperability and reduces integration complexity.
• Data Formats:
• JSON, XML, and Protocol Buffers are commonly used for data serialization. Standardized data formats allow for easier integration and data exchange.
• Security Standards:
• Standards such as ISO/IEC 27001 for information security management, NIST Cybersecurity Framework, and OWASP IoT Top Ten help guide the implementation of security practices in IoT systems.
• Device Management Standards:
• Standards like OMA Lightweight M2M (LwM2M) provide frameworks for device management, enabling remote management, monitoring, and configuration of IoT devices.
• Interoperability Standards:
• Initiatives like the Open Connectivity Foundation (OCF) and the Industrial Internet Consortium (IIC) promote interoperability standards to enable devices from different manufacturers to work together.

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Connected devices 1st to ith connected to the local network and gateway using the WPAN or LPWAN network protocols

communication technologies

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1. Wireless Communication Technologies

a. Wi-Fi

• Use Cases: Smart homes, industrial automation, and connected appliances.
• Features: High data transfer rates, widely available, but relatively high power consumption.

b. Bluetooth

• Use Cases: Wearables, smart home devices, and short-range connections.
• Features: Low energy consumption (especially Bluetooth Low Energy), short-range, suitable for personal area networks.

c. Zigbee

• Use Cases: Home automation, smart lighting, and sensor networks.
• Features: Low power, mesh networking capability, designed for low data rate applications.

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communication technologies

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d. Z-Wave

• Use Cases: Home automation and security systems.
• Features: Low power, mesh networking, operates in sub-1GHz frequency bands for longer range.

e. LoRa (Long Range)

• Use Cases: Agricultural monitoring, smart cities, and environmental monitoring.
• Features: Long-range communication (up to 15 km), low power consumption, suitable for low data rate applications.

f. NB-IoT (Narrowband IoT)

• Use Cases: Smart meters, smart cities, and tracking applications.
• Features: Optimized for low bandwidth, extended coverage, and energy efficiency, utilizing existing cellular networks.

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Communication technologies

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g. LTE-M (LTE Cat-M1)

• Use Cases: Wearables, asset tracking, and industrial IoT.
• Features: Higher data rates than NB-IoT, supports mobility, low power consumption, and good coverage.

h. 5G

• Use Cases: Smart factories, autonomous vehicles, and smart cities.
• Features: Ultra-low latency, high data rates, massive device connectivity, and support for various applications.

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2. Wired Communication Technologies

a. Ethernet

• Use Cases: Industrial automation, smart buildings, and connected devices in office environments.
• Features: High data transfer rates, reliable, but lacks the flexibility of wireless solutions.

b. Serial Communication (RS-232, RS-485)

• Use Cases: Industrial equipment, sensors, and data loggers.
• Features: Simple and reliable for short distances, suitable for point-to-point connections.

c. Power Line Communication (PLC)

• Use Cases: Smart metering and home automation.
• Features: Utilizes existing power lines for data transmission, reducing installation costs.

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3. Communication Protocols

a. MQTT (Message Queuing Telemetry Transport)

• Use Cases: Lightweight messaging for IoT applications.
• Features: Low bandwidth, supports publish/subscribe messaging pattern, ideal for unreliable networks.

b. CoAP (Constrained Application Protocol)

• Use Cases: Resource-constrained devices, home automation, and sensor networks.
• Features: Web transfer protocol for constrained environments, operates over UDP.

c. HTTP/HTTPS

• Use Cases: Web applications and RESTful APIs.
• Features: Widely used for web services, but can be less efficient for constrained devices due to overhead.

d. AMQP (Advanced Message Queuing Protocol)

• Use Cases: Enterprise integration and messaging.
• Features: Reliable messaging with support for queuing, routing, and security.

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Data enrichment and consolidation

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Key Steps in Data Enrichment

• Data Collection:
• Gather data from various sources, such as IoT devices, sensors, external databases, APIs, and public data sets.
• Data Integration:
• Combine data from different sources to create a more comprehensive dataset. This might involve merging datasets or linking data entries based on unique identifiers.
• Data Transformation:
• Standardize and format data to ensure consistency. This includes normalizing values, converting units, and ensuring uniform data types.
• Contextualization:
• Add contextual information to the data, such as geographic locations, demographic details, or relevant metrics that provide deeper insights.
• Validation:
• Verify the accuracy and reliability of the enriched data to ensure it meets quality standards.

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Ease of designing and affordability

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Ease of Designing

• Modular Architecture:
• Concept: Design systems with modular components that can be easily integrated or replaced.
• Benefits: Simplifies the design process, allows for easy upgrades and scalability, and makes it easier for developers to work on individual parts without affecting the entire system.
• Standardized Protocols:
• Concept: Utilize widely accepted communication protocols (e.g., MQTT, CoAP) and data formats (e.g., JSON, XML).
• Benefits: Reduces complexity by providing clear guidelines for data exchange, facilitating interoperability between devices from different manufacturers.
• User-Friendly Development Tools:
• Concept: Leverage development platforms and toolkits that offer graphical user interfaces and pre-built libraries (e.g., Arduino, Raspberry Pi, Node-RED).
• Benefits: Lowers the learning curve for developers, speeds up the prototyping process, and enables non-experts to participate in IoT project development.

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