Overview
The Internet of Things course offered by Cognixia introduces participants to advanced IoT concepts, methodologies, and protocols used for communication. This includes next-generation, IoT-friendly applications, physical-layer protocols, and widely accepted IoT frameworks and standards. The program covers popular, service-rich cloud platforms and focuses on how to build and deploy IoT solutions. Practical use-cases and case studies are included to ensure that the participant develops an ability to work through real-life scenarios.
What You'll Learn
- IoT technology and tools
- Core concepts and background technologies
- Features of the IoT landscape
- Sensors, microcontrollers and communication interfaces to design and build IoT devices
- Designing and building a network based on the client server
- Publishing/subscribing to connect, collect data, monitor and manage assets
- Writing device, gateway and server-side scripts and apps, enabling them to aggregate and analyze sensor data
- Selecting application-layer protocols and web services architectures for a seamless integration of various components within an IoT ecosystem
- Reviewing standard development initiatives and reference architectures
- Deploying various types of analytics on machine data to define context, find faults, ensure quality and extract valuable actionable insights
- Cloud infrastructure, services, APIs and architectures of commercial and industrial cloud platforms
- Prevalent computing architectures, including distributed, centralized, and edge/fog computing
Curriculum
- Embedded Systems
- Computer Networks
- M2M (Machine to Machine Communication)
- Internet of Everything (IoE)
- Machine Learning
- Distributed Computing
- Artificial Intelligence
- Industrial Automation
- Interoperability, Identification, Localization, Communication
- Software Defined Assets
- Market statistics
- Early adopters
- Roadmap
- Development
- Deployment and monetization of applications as service
- Knowledge discovery process
- DIKW pyramid and relevance to IoT
- Microcontrollers: Cost, performance and power consumption
- Commercial microcontroller-based development boards
- Selection criteria and trade-offs
- Industrial networks, M2M networks
- Transducer: Sensor and Actuator
- Types of sensors
- Sampling
- Analog to digital conversion
- Selection criteria of sensor and ADC
- Data acquisition, storage and analytics
- Signals and systems
- Signal processing, systems classification, sampling theorem
- Ensuring quality and consistency of data
- Real-time analytics
- Understanding fundamental nuances of IoT and Big Data
- Usage of IoT data in various business domains to gain operational efficiency
- Edge analytics
- Data aggregation on edge gateway
- Sensor nodes
- Sensor node architecture
- WSN/M2M communication technologies
- Bluetooth, Zigbee and WiFi communication technologies
- Cellular communication and LPWAN (LoRa and LoRaWAN) technologies
- Topologies
- Applications
- IoT reference architectures
- Standardization initiatives
- Interoperability issues
- IoT design considerations
- Architectures device, network and cloud
- Centralized vs distributed architectures
- Networks, communication technologies and protocols
- Smart asset management: Connectivity, visibility, analytics, alerts
- Public, private and hybrid cloud platforms and deployment strategy
- Industrial Gateways
- Commercial gateways solutions from various vendors
- Cloud-based gateway solutions
- IaaS, SaaS, PaaS models
- Cloud components and services
- Device management
- Databases, visualization
- Reporting
- Notification/alarm management
- Security management
- Cloud resource monitoring and management
- Example platforms: ThingSpeak, Pubnub, AWS IoT
- AWS IoT Services – Device registry, Authentication and authorization, Device gateway, Rules engine, Device shadow
- Standards and best practices
- Common vulnerabilities
- Attack surfaces
- Hardware and software solutions
- Open-source initiatives
- Descriptive, diagnostic, predictive, and prescriptive
- Analytics using Python advance packages: NumPy, SciPy, Matplotlib, Pandas and Sci-kit learn
- Case studies and roadmap
- Cold chain monitoring
- Asset tracking using RFID and GPRS/GPS
- Programming micro-controllers (Arduino, NodeMCU)
- Building HTTP and MQTT based M2M networks
- Interfacing Analog and Digital sensors with microcontrollers for real-time data acquisition, as well as storage and analysis on IoT endpoints and edges
- Developing microcontroller-based applications to understand event-based, real-time processing and in-memory computations
- Setting up Raspberry Pi as gateway to aggregate data from thin clients
- Python programming on Raspberry Pi to analyze collected data
- GPIO programming using Python and remote monitoring /control
- Pushing collected data to cloud platforms
- Uploading data on local gateway as cache
- Uploading data on cloud platforms
- Monitoring and controlling devices using android user apps and Bluetooth interfaces
- Building wireless sensor networks using WiFi
- Remote controlling machines using cloud based apps
- Remote controlling machines using device based apps through cloud as an intermediate node
- Interfacing Raspberry Pi with AWS IoT Gateway service to exchange messages
- Data cleaning, sub-setting and visualization
- Set of Python exercises to demonstrate descriptive and predictive analytics
- Development Boards
- Raspberry Pi 3
- Arduino Mega (ATMega2560) with USB cable
- ESP8266 NodeMcu
- Electronic Components
- Sensors – Analog temperature sensor (LM35)
- IR Proximity Sensor
- Switches – Push Button (10)
- Breadboard
- LEDs (10)
- Resistors (10)
- Connecting leads (25)
- Memory Card (16 GB)
- HDMI – VGA Converter
- 1A Power Adapter
- Communication Modules
- WiFi – ESP01
- Bluetooth – HC05
Who should attend
- IT professionals
- Electrical and electronics engineers
- Designers
- Solution architects
- Existing and budding entrepreneurs keen to build smart solutions for customers
- Fresh graduates who meet the prerequisite criteria