– CRN: 32409 / 32410
Credits: 3
Meeting Days/Time: MR, 12:30–1:50 PM
Lecture Room: JONSSON 4104
Laboratory: JEC 5312
Laboratory Access: 7:00 AM – 10:00 PM (access provided to registered students) –>
Websites / Platforms
- Piazza: for accessing lecture material and discussions
- Gradescope: for submitting assignments
- LMS: for quizzes and homeworks
Prerequisites
Basic understanding of computer organization, operating systems, and networks. Ability to write programs in high-level languages (e.g., Python or C/C++).
Instructors
- Professor: Ish Jain
Office Hours: Mondays and Thursdays after class or by appointment
Email: jaini@rpi.edu
– - Teaching Assistant
- Chibuikem Ezemaduka — ezemac@rpi.edu
Office/Lab Hours: Mon–Fri, 4–5 PM –>
Course Description
The course provides an in-depth study of the technologies and protocols involved in building the Internet-of-Things (IoT), with specific focus on networking at the edge of the Internet. This includes understanding of wireless communication and link layer technologies, multi-access and scheduling mechanisms, mobility models, routing in disconnected networks, energy-efficient edge networking, loss-tolerant transport protocols, and their applications to emerging areas such as vehicular networks, RFID systems and smart buildings. The course also discussed IoT Security and data/content distribution, aggregation, and compression. This course has a strong emphasis on hands-on experience utilizing Raspberry Pi’s, Arduino’s, and NI software radio boards, and a significant part of the course assessment will be based on a final project focused on building a wireless-based application such as indoor localization for IoT devices. Students are encouraged to suggest their own individual projects.
Topics (Tentative)
- Review of networking architectures and standards
- Review of wireless PHY layer technologies
- Wireless access technologies for IoT: WiFi, LTE, NB-IoT
- Low-power IoT Wireless Technologies: ZigBee, Z-Wave, BLE, LoRA
- Low-power Backscatter Technologies: RFID
- Routing and network layer protocols for IoT: RPL and 6LoWPAN
- Low-overhead IoT transport: CoAP and MQTT
- Network management for IoT: LWM2M and NETCONF
- IoT mobility models and mobility handling protocols
- IoT security
- Energy-efficient IoT networking
- Sensor data aggregation and analytics
- Applications to vehicular networking, environmental monitoring, healthcare, smart buildings, etc.
Course Texts
No required textbooks. This is an advanced-level course, and much of the material may not be available in textbook form. Reference material will be provided for each lecture.
Supplemental (not required):
- Olivier Hersent, David Boswarthick, and Omar Elloumi, The Internet of Things: Key Applications and Protocols, 2nd ed., Wiley, 2012.
- Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, 1st ed., Wiley, 2007.
- Vijay Garg, Wireless Communications and Networking, Morgan Kauffman, 2007.
- Savo Glisic, Advanced Wireless Communications and Internet: Future Evolving Technologies, 3rd ed., 2011.
- Jack L. Burbank, Julia Andrusenko, Jared S. Everett, and William T. M. Kasch, Wireless Networking: Understanding Internetworking Challenges, Wiley, 2013.
Student Learning Outcomes
The table below presents the Student Learning Outcomes (SLOs) for both ECSE-4660 and ECSE-6660 students. The left column is for ECSE 4660 students, while the right column for ECSE-6660 students expands each with greater depth, advanced applications, and evaluation criteria. | ECSE-4660 | ECSE-6660 | | ———————————————— | ——:| | 1. Understand, design, and evaluate wireless PHY layer technologies for IoT such as Modulation, LoRa, RFID, etc. | 1. Analyze and compare advanced wireless PHY layer technologies (e.g., LoRa, NB-IoT, UWB), including performance trade-offs, spectral efficiency, and deployment considerations in diverse IoT environments.| |2. Understand key wireless access technologies such as Aloha and CSMA used in the Internet-ofThings.| 2. Critically evaluate and simulate advanced MAC protocols (e.g., TDMA, FDMA, SDMA) for IoT, considering scalability, latency, and energy efficiency in dense and heterogeneous networks. | |3. Understand key routing algorithms and protocols used in the Internet-of-Things.| 3. Design and assess routing strategies (e.g., RPL, geographic routing, opportunistic routing) under constraints such as mobility, energy, and intermittent connectivity in large-scale IoT deployments. | |4. Understand low-overhead transport protocols that are suited for IoT applications. | 4. Evaluate and optimize lightweight transport protocols (e.g., CoAP, MQTT, QUIC) for reliability, congestion control, and interoperability in constrained IoT networks.| |5. Understand mobility models and mobility handling protocols for the Internet-of-Things.| 5. Model and simulate mobility patterns in IoT (e.g., vehicular, drone-based, or mobile health networks), and assess the impact on routing, latency, and data consistency. | |6. Understand security mechanisms used for IoT communication over wireless. | 6. Analyze and implement advanced security frameworks for IoT, including lightweight cryptographic protocols, secure bootstrapping, and intrusion detection in resource-constrained environments. | |7. Understand data compression and aggregation methods in sensor IoT networks. | 7. Design and evaluate adaptive data compression, in-network processing, and edge analytics techniques for scalable and energy-efficient IoT data management. | |8. Understand integrated IoT solutions critical in emerging domains such as vehicular networks, RFID systems, and smart buildings. | 8. Architect and critically assess end-to-end IoT systems in complex domains (e.g., smart cities, industrial IoT, cyber-physical systems), focusing on interoperability, scalability, and real-time performance. |
Note that in addition, students at 6000 level will implement practical security considerations in IoT networks through advanced labs and individual projects.
Assessment
| Component | Weight |
|---|---|
| Class Participation | 5% |
| Per-class Quizzes | 5% |
| Homework Assignments | 20% |
| Laboratory Assignments | 30% |
| Final Project (implementation & demonstration) | 40% |
The grade distribution above is the same for both 4660 and 6660 students.
For ECSE-6660 (graduate level): some advanced lab/homework questions and a more advanced project are required.
Projects: groups of two students are typical. 4000- and 6000-level students are discouraged from forming a single group unless approved; in such cases the 6000-level student is expected to complete advanced features independently.
Course Topics & Schedule (Tentative)
The schedule and topics are tentative and may change depending on class progress.
| Lecture # | Topic |
|---|---|
| 1,2 | Intro to network architectures & standards |
| 3,4,5,6 | PHY layer technology review: channel, LoRA, backscatter, OFDM foundation |
| 7,8,9,10 | Wireless access technologies for IoT: WiFi, BLE, LTE, Zigbee |
| 11,12,13, 14,15 | Routing and network layer protocols for IoT: DSR, AODV, IP addressing, DHCP, ARP, IPv6, NAT, Routing, ICMP, RPL. 6LowPAN |
| 16,17 | Low-overhead IoT transport: UDP, TCP, Socket Programming |
| 18,19 | Applications for IoT: HTTP, MQTT, CoAP |
| 20,21,22, 23 | Secure communication of IoT devices over wireless; Secure protocols, firewall |
| 24,25 | Emerging applications |
| 26,27,28 | Final project presentations |
Other Course Policies
Final Project
The project provides an opportunity to immerse yourself in IoT design and application in your area of interest. Projects typically involve up to two students (individual projects are allowed). Groups of three or more require strong justification. Submit a final report at the end of the semester along with a presentation and recorded demo. Upload any software with documentation sufficient for result reproduction. Further details will be shared during the course.
Important: Previously developed products—either from another class or third parties—cannot be used for project credit. Any open-source or third-party software must be explicitly acknowledged and does not count toward credit. Failure to acknowledge such software will be treated as plagiarism per the academic dishonesty policy.
Quizzes, Homework, and Lab Assignments
- Per-class quizzes (Lecture 2 onward) will be assigned on LMS and due the same day at 11:59 PM.
- Homework and lab assignments will be on Piazza; submit solutions on Gradescope by the deadline. Assignments are posted roughly weekly, with homeworks and labs in alternating weeks.
- Late submissions are not accepted, unless approved by the instructor. There is a 20% penalty until late submission deadline on Gradescope (typically 2 days later). First two penalties will be waived. LMS grading is automatic and doesn’t allow late submissions.
- All assignments must be completed independently. Conceptual discussion with classmates/instructor is allowed (e.g. through Piazza), but sharing detailed solutions or final answers is not. Do not copy solutions from any source. Do not knowingly provide your work to be copied.
Attendance / Participation
We encourage a climate of inquiry with active student engagement. While synchronous lecture attendance is not mandatory, attending/watching lectures supports strong performance and an active learning environment.
Submission Policy and Absences
- Job interviews are not approved absences unless pre-approved with documentation from the Dean of Students.
- Upload all homework and lab assignments on Gradescope before the deadline. Late submissions are automatically marked and not accepted. Without a valid excuse, a zero is assigned.
- The final project report and deliverables must be submitted by the final class. With a valid excuse, an Incomplete may be assigned; otherwise, a zero is given.
Academic Integrity
Student–teacher relationships are built on trust. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of academic dishonesty—please review it. All graded work (homework, lab work, or final project deliverables) must represent your own (or your team’s) independently completed work. If help is received, include a note indicating the nature of the collaboration and the names/identities of collaborators.
Violation Consequences: Any incident of academic dishonesty on an exam/assignment will result in at least a score of 0 for the first incident, and a course grade of F for repeated violations—or F even on a first violation depending on severity. An F due to violations will be reported to the Dean of Students and may become a permanent record.