Internet of  Things (IOT) Training

Chapter 1:Introduction

• Case Studies from Nest, CISCO and top industries
• IoT adaptation rate in North American & and how they are aligning their future
• business model and operation around IoT
• Broad Scale Application Area
• Smart House and Smart City
• Industrial Internet
• Smart Cars
• Wearables
• Home Healthcare
• Business Rule Generation for IoT
• 3 layered architecture of Big Data — Physical (Sensors), Communication , and Data
• Intelligence

Chapter 2: All about Sensors – Electronics

• Basic function and architecture of a sensor — sensor body, sensor mechanism, sensor calibration, sensor maintenance, cost and pricing structure,
• legacy and modern sensor network — all the basics about the sensors
• Development of sensor electronics — IoT vs legacy, and open source vs traditional PCB design style
• Development of sensor communication protocols — history to modern days. Legacy protocols like Modbus, relay, HART to modern day Zigbee, Zwave, X10,Bluetooth, ANT, etc.
• Business driver for sensor deployment — FDA/EPA regulation, fraud/tempering detection, supervision, quality control and process management
• Different Kind of Calibration Techniques — manual, automation, infield,
• secondary primary and calibration — and their implication in IoT
• Powering options for sensors — battery, solar, Witricity, Mobile and PoE
• Hands on training with single silicon and other sensors like temperature, pressure, vibration, magnetic field, power factor etc.

Chapter 3: Fundamental of M2M communication — Sensor Network and Wireless protocol

• What is a sensor network? What is ad-hoc network?
• Wireless vs. Wireline network
• WiFi- 802.11 families: N to S — application of standards and common vendors.
• Zigbee and Zwave — advantage of low power mesh networking.
• Long distance Zigbee.
• Introduction to different Zigbee chips.
• Bluetooth/BLE: Low power vs high power, speed of detection, class of BLE.
• Introduction of Bluetooth vendors & their review.
• Creating network with Wireless protocols such as Piconet by BLE
• Protocol stacks and packet structure for BLE and Zigbee
• Other long distance RF communication link
• LOS vs NLOS links
• Capacity and throughput calculation
• Application issues in wireless protocols — power consumption, reliability, PER,QoS,LOS
• Hands on training with sensor network
  1. PICO NET- BLE Base network
  2. Zigbee network-master/slave communication
  3. Data Hubs : MC and single computer ( like Beaglebone ) based datahub

CHAPTER 4:Review of Electronics Platform, production and cost projection

• PCB vs FPGA vs ASIC design-how to take decision
• Prototyping electronics vs Production electronics
• QA certificate for IoT- CE/CSA/UL/IEC/RoHS/IP65: What are those and when needed?
• Basic introduction of multi-layer PCB design and its workflow
• Electronics reliability-basic concept of FIT and early mortality rate
• Environmental and reliability testing-basic concepts
• Basic Open source platforms: Arduino, Raspberry Pi, Beaglebone, when needed?
• RedBack, Diamond Back

CHAPTER 5:Conceiving a new IoT product- Product requirement document for IoT

• State of the present art and review of existing technology in the market place
• Suggestion for new features and technologies based on market analysis and patent issues
• Detailed technical specs for new products- System, software, hardware, mechanical, installation etc.
• Packaging and documentation requirements
• Servicing and customer support requirements
• High level design (HLD) for understanding of product concept
• Release plan for phase wise introduction of the new features
• Skill set for the development team and proposed project plan -cost & duration
• Target manufacturing price

CHAPTER 6:Introduction to Mobile app platform for IoT

• Protocol stack of Mobile app for IoT
• Mobile to server integration –what are the factors to look out
• What are the intelligent layer that can be introduced at Mobile app level ?
• iBeacon in IoS
• Window Azure
• Linkafy Mobile platform for IoT
• Axeda
• Xively

CHAPTER 7:Machine learning for intelligent IoT

• Introduction to Machine learning
• Learning classification techniques
• Bayesian Prediction-preparing training file
• Support Vector Machine
• Image and video analytic for IoT
• Fraud and alert analytic through IoT
• Bio –metric ID integration with IoT
• Real Time Analytic/Stream Analytic
• Scalability issues of IoT and machine learning
• What are the architectural implementation of Machine learning for IoT

CHAPTER 8:Analytic Engine for IoT

• Insight analytic
• Visualization analytic
• Structured predictive analytic
• Unstructured predictive analytic
• Recommendation Engine
• Pattern detection
• Rule/Scenario discovery — failure, fraud, optimization
• Root cause discovery

CHAPTER 9:Security in IoT implementation

• Why security is absolutely essential for IoT
• Mechanism of security breach in IOT layer
• Privacy enhancing technologies
• Fundamental of network security
• Encryption and cryptography implementation for IoT data
• Security standard for available platform
• European legislation for security in IoT platform
• Secure booting
• Device authentication
• Firewalling and IPS
• Updates and patches

CHAPTER 10:Database implementation for IoT : Cloud based IoT platforms

• SQL vs NoSQL-Which one is good for your IoT application
• Open sourced vs. Licensed Database
• Available M2M cloud platform
• Axeda
• Xively
• Omega
• NovoTech
• Ayla
• Libellium
• CISCO M2M platform
• AT &T M2M platform
• Google M2M platform

CHAPTER 11:A few common IoT systems

• Home automation
• Energy optimization in Home
• Automotive-OBD
• IoT-Lock
• Smart Smoke alarm
• BAC ( Blood alcohol monitoring ) for drug abusers under probation
• Pet cam for Pet lovers
• Wearable IOT
• Mobile parking ticketing system
• Indoor location tracking in Retail store
• Home health care
• Smart Sports Watch

CHAPTER 12: Big Data for IoT

• 4V- Volume, velocity, variety and veracity of Big Data
• Why Big Data is important in IoT
• Big Data vs legacy data in IoT
• Hadoop for IoT-when and why?
• Storage technique for image, Geospatial and video data
• Distributed database
• Parallel computing basics for IoT