Course image ECS6264 IoT Operating System
Option Module-Semester 2

The operating system (OS) is the system software that manages computer hardware and software resources and provides common services for programs. Considering the resource constraints of typical sensor nodes in wireless sensor networks, a new approach is required for OS design in WSN. The operating systems for WSNs should be flexible component-based and application-specific specially designed for these types of networks. It should also support concurrent programs with very low memory requirements. This module focuses on this special type of operating system.

Course image ECS6265: MODELLING AND FABRICATION TECHNIQUES
Option Module-Semester 2

Creativity, together with the making of ideas into fruition, is essential for progress. Today the evolution from an idea to its application can be facilitated by the implementation of Fabrication Laboratories, or FabLabs, having affordable digital tools for prototyping. FabLabs aiming at scientific research and invention are now starting to be established inside Universities and Research Centers to support STEM education and for community development. In this module we teach the basics of handling the fabrication of PCBs prototype casing using 3D printers.

Course image ECS6264: IoT OPERATING SYSTEMS
Option Module-Semester 2

The operating system (OS) is the system software that manages computer hardware and software resources, and provides common services for programs. Considering the resource constraints of typical sensor nodes in wireless sensor network, a new approach is required for OS design in WSN. The operating systems for WSNs should be flexible component based and application specific specially designed for these types of networks. It should also support concurrent programs with very low memory requirements. This module focuses on this special type of operating system.

Course image ECS6263: EDGE AND DISTRIBUTED COMPUTING
Option Module-Semester 2

This module is concerned with the design and implementation of embedded and distributed analytics for IoT applications such as: predictive maintenance, person-centered health analytics, anomaly detection, adaptive automatic scheduling, self-driving cars, smart cameras, and so on. A particular focus will be set on Artificial Intelligence (AI) and Machine Learning(ML) algorithms. Embedded/Edge and Distributed analytics has gained a lot of attention in the last years as a solution to solve problems created by Cloud-based analytics such as the big data deluge, data Privacy & Security, limited bandwidth for data streaming transmission and large latency for feedback to edge devices. After completing this module, the student will have gained the necessary skills to undertake research in embedded AI and ML. Furthermore he/she will be able to design and prototype a basic embedded and distributed analytics application

Course image ECS6261: ULTRA-LOW POWER DESIGN TECHNIQUES FOR IoT DEVICES
Option Module-Semester 2

Welcome to the Module "ECS6261: ULTRA-LOW POWER DESIGN TECHNIQUES FOR IoT DEVICES"

This module aims at inculcating techniques of minimizing the amount of energy required to actually operate IoT devices, networks and systems by in part; minimizing the number of required IoT sensors that needed to cover specific area, in using a true battery-free device, by designing and implementing energy harvesting circuits that can help IoT sensors utilize energy harvested directly from the environment; e.g. Radio Frequency (RF), light, motion, and vibration, or integrated/hybrid architectures of these. Standards and optimization techniques for ultra-low power and energy harvesting design architectures shall be discussed and prototyped.