IoT ( Internet of Things) Fundamentals & Frontiers


Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. Total IoT devices already exceeded 5B and by 2025, that will exceed 80B.

In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet.

The underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. What changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT.

Course Objective:

Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas.

1. Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane applications

2. M2M Wireless protocols for IoT- WiFi, SigFox,LORA, LPWAN, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one

3. Fundamentals of IoT Middleware functions.

4. Mobile/Desktop/Web app - for registration, data acquisition and control –Available M2M data acquisition platform for IoT—AWS IoT, Azure IoT, Google IoT

5. Security issues and security solutions for IoT.

6. Open source/commercial electronics platform for IoT-Rasberry Pi, Adruino , ArmMbedLPC etc.

7. Open source /commercial enterprise cloud platform such as Microsoft Azure IoT suites, AWS IoT, Google IoT , Siemens MindSphere, Things Works etc.

8. Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health, Smart Cities etc.

Target Audience:

  • Practicing Engineers, Leaders, PhD Students, Faculty Members, Researchers who want to get deeper into IoT
  • Developers who have to get basic ideas of IoT.
  • Managers who are responsible for digital transformations.
  • Prerequisites:

  • Should have basic knowledge of business operation, devices, electronics systems and data systems.
  • Must have basic understanding of software and systems.
  • Basic understanding of Statistics ( in Excel levels).

  • Breakdown of the Module:

    Session 1: Module-1 Introduction, Basics and Case Studies
  • Case Studies from Nest, CISCO and top industries
  • IoT adaptation rate in North American market & how they are aligning their future business model and operation around IoT
  • Broad Scale Application Area
  • Smart house and smart city
  • Industrial Internet (IIoT)
  • Layers of Technologies in IoT
  • Smart Cars
  • Smart Parking
  • Wearables
  • Home healthcare
  • Business Rule generation for IoT
  • 3 layered architecture of Big Data –Physical (Sensors), Communication and Data Intelligence
  • Evolving standards and platform players-brief introductions
  • Session 2: Module-2 Sensors and Hardware
  • 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 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, 6LoPAN, WiFi-x
  • 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, primary and secondary calibration –their implication in IoT
  • Powering options for sensors-Battery, solar, Witricity. Mobile and PoE
  • Energy harvesting solutions for wearables
  • SoC( Sensors on Chips) and MEMS based sensors
  • Sampling rate matching with application – some mathematical exercise
  • Session 3: Module-3 Introduction to 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 each standards and common vendors.
  • LPWAN radios : SigFOX, LoRA
  • 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
  • 6LoWPAN (IPv6 over low power wireless)
  • Other long distance RF communication link
  • LOS vs NLOS links
  • Sensor network packet architecture
  • Capacity and throughput calculation
  • Application issues in wireless protocols- power consumption, reliability, PER, QoS, LOS
  • Autopairing and reconnection
  • Syncing wireless connection with product launch
  • Which applications to use and where
  • Mathematical exercise to find out which network to pick up and where
  • Session 4: Module-4: Mobile app platform & Middleware for IoT
  • What is IoT Middleware- Type and classification
  • Protocol stack of Mobile app for IoT
  • Fundamentals of WAP ( Wireless application protocols)
  • 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
  • Global vs Local ID-GUID concept for secured IoT network
  • IoT-Middleware case study-1 Microsoft Azure IoT Central
  • IoT Middleware case study-2 Google IoT
  • Middleware and Edge analytic
  • Middleware and local Database
  • Security for IoT Middleware
  • Auto-pairing, reconnection, power rebooting of device for 24x7 operation
  • Readymade middleware with API management as case
  • Session 5: Module 5: Machine learning for intelligent IoT
  • Introduction to Machine learning
  • Introduction to digital signal processing
  • Fundamentals of analytics stacks in IoT applications
  • 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
  • Session 6: Module 6: 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
  • Where to do analytics? Cloud vs Edge
  • Session 7:Module 7: Iaas/Paas/Saas for IoT
  • Iaas : Infrastructure as a service-evolving models
  • Mechanism of security breach in IOT layer for Iaas
  • Middleware for Iaas business implementation in healthcare, homeautomation and farming
  • Iaas case study for vehicular information for Auto-insurance and Agriculture
  • Paas: Platform as a service in IoT. Case studies of some of the IoT middleware
  • Saas : Software/System as service for IoT business models
  • European and American (NIST) legislation for security in IoT platform
  • Firewalling and IPS
  • Updates and patches via web-OTA mechanism
  • Microsoft IoT Central as an example of PaaS platform
  • Google IoT, AWS IoT PaaS platform
  • Session 8:Module 8: Database & Platform implementation for IoT : Edge and Cloud based IoT platforms
  • SQL vs NoSQL-Which one is good for your IoT application
  • Open sourced vs. Licensed Database
  • Available M2M cloud platform : Microsoft Cosmos DB, AWS Dynamo DB
  • Basic functionality of IoT Database -Cloud and Edge
  • Real Time Analytic
  • Batch Analytic
  • Data storage
  • Data filtering
  • Rule engine
  • Process mapping
  • Caching of Data for IoT rule implementation
  • Lossless data compression /Data encoding : Huffman and Progressive filtering
  • Session 9: Module 9: A few common IoT systems
  • Home automation
  • Smart Parking
  • Energy optimization
  • Automotive-OBD /Iaas/Paas for Insurance and Car parking
  • Wearable IOT
  • Mobile parking ticketing system
  • Indoor location tracking in Retail store
  • Healthcare Wearable
  • Sports Wearable
  • Session 10:Module 10: Mobile IoT Modem, 4G and 5G for IoT
  • 4G IoT standards for IoT : LTE-M applications, NB-IoT, UNB standard for 3GPP, 4G , LTE CAT-1 IoT
  • 5G IoT standard for IoT : LPWA, eMTC , IMT 2020 5G
  • Detailed architecture of IoT Mobile Modem
  • Session 11: Module 11: Blockchain in IoT : BIoT
  • immutable ledger system – DAG Ledger, Hyper Ledger
  • P2P network, Private and Public Key- basic concepts
  • How ledger system is implemented practically- review of some research architecture
  • Blockchain and distributed IoT database – different proposals- merits and demerits
  • Some practical application examples from smart city, smart machines, smart cars
  • IBM & Samsung – ADEPT – Blockhain for IoT in consumer electronics
  • Guard Time and Intrinsic ID- SRAM PUF – physical unclonable function – complemented by blockchain-based distributed ledger technology.
  • P2P Encrypted Messaging: TeleHash – Distributed File Sharing: BitTorrent – Decentralized Programming Language for Blockchain (for device communicaon & coordinaon):
  • Session 12:Module 12: Managed IoT Service
  • What is managed IoT services ?
  • Auto-Diagnosis of connectivity
  • Monitoring Hardware Health
  • Monitoring edge and cloud analytics
  • Monitoring Gateway services
  • Monitoring Cloud services
  • Monitoring analytic services
  • Monitoring API calls
  • Managing Sensor Calibration/Re-calibration