Internet Of Things

Systems and Physical Data Analytics

Internet of Things - Systems and Physical Data Analytics

Columbia University course EECS E6765

Zoran Kostic, Professor of Professional Practice, zk2172(at)

Electrical Engineering Department, Data Sciences Institute, Columbia University in the City of New York

Course in a nutshell:

Broad coverage of topics relevant to Internet of Things (IoT). Training students in techniques crucial for productive participation in the development of IoT components, systems and data processing. Significant design project utilizing most popular development platforms used in industry. Semester-long student engagement with industry (engineers, startups founders, corporate executives, IoT analysts) through invited speakers, project guidance, and mentoring.

Focus on Physical Data Analytics:

Data analytics in IoT, Machine learning for IoT, Amazon AWS cloud tools, Amazon AWS IoT tools, Intel Edison Platform

Bulletin Description:

  • Broad study of technical aspects of Internet of Things: architectures, algorithms, channels, devices, networks, protocols, communications, power, data processing, security, and standards. In-depth analysis of several selected use cases across systems, software and hardware. Focus on a significant design project. Participation of contributors from industry.


  • Introduction, motivation, summary of critical applications

  • Communication channels and techniques

  • Wireless technology overview and standards

  • WiFi and cellular: next generation and IoT

  • SW and HW: platforms and development

  • Device architecture

  • Embedded software development

  • Low power devices

  • Protocols

  • Machine to machine communication

  • Networks and internet address management

  • Topologies and localization

  • Cyber-physical Systems

  • Security and privacy

  • Cloud computing and data analytics

  • Energy harvesting

  • Sensors and sensor networks

  • Security and privacy

  • Challenges: business models, monetization, hype

  • Data analytics for IoT

  • Machine learning for IoT

Project Pages

Project Areas

  • Smart Home

  • Smart Buildings

  • Smart Cities

  • Smart Infrastructure

  • Mobility and Transport

  • Energy

  • Smart Grid

  • Healthcare

  • Wearables

  • Data Analytics

  • Sensor Technologies

  • Security

Course: Open to Columbia students

Internet of Things EECS E6765 3 credit graduate-level course

Link to projects

  • Spring 2020, 2019, 2018, 2017, 2016, 2015

  • Prerequisites - suggested: Wireless Communications (ELEN E4703), Computer Networks (CSEE W4119), Advanced Logic Design (CSEE4823), Embedded Systems (CSEE4840), or related courses. Knowledge of programming.


  • Lectures:

    • Presentation of instructional material

  • Lab sessions:

    • Covering critical elements of IoT design

    • Communication platform setup using BTLE, WIFI of Zigbee

    • Connecting to the Internet

    • Enablement of a sensor-based application

    • Data processing

  • Projects:

    • Team-based

    • Students with complementary backgrounds

    • Significant design

    • Reports and presentations to Columbia and NYC community

    • Best could qualify for publications and/or funding

  • Industry participation:

    • Project definition and sponsoring

    • Weekly presentations

    • Interaction with students through mentoring

Books, Tools and Resources

  • BOOKS:

    • Notes

  • Projects platform:

    • Selection of the industrial-grade HW development platforms (possibilities are Intel, Broadcom, Nordic, Raspberry Pi, others)

    • Amazon AWS cloud tools

    • Intel Edison Platform

    • Raspberry PI

    • Facebook Parse

    • Operating system of choice

    • Code development on github, bitbucket

Course sponsored by equipment and financial contributions of:

  • ST Microelectronics, Atmel/Microchip, Broadcom (Wiced platform); Intel (Edison IoT platform), Silicon Labs (IoT platform).