This interdisciplinary program covers smart applications built using embedded systems capable of sensing, actuation, computing, and communication. Internet of things (IoT) is the enabling technology behind many fascinating applications such as smart homes, smart cities, and intelligent transportation systems. In IoT, smart things can act and interact without human intervention which paves the way for an endless range of applications.
Topics covered include IoT applications, embedded systems and sensing, IoT communication protocols, Industrial Internet of Things (IIoT), cloud and edge computing, big data analytics, and IoT security. Students are introduced to embedded systems programming and interfacing. Students also learn how to connect smart things, as well as to the cloud. Through learning big data analytics, students can use advanced analytics and machine learning to process sensor data and build innovative applications. Students are exposed to how IIoT is used in industrial applications using state-of-the-art use cases.
The objectives of the concentration in the Internet of Things program are to:
- Develop the ability to systematically design and implement IoT systems efficiently by making educated design choices.
- Equip students with scientific tools, knowledge, and technologies to be able to build and integrate IoT solutions.
Relationship of the Objectives of the course and University Mission and the Saudi Vision 2030
This area of concentration contributes directly to the diversification of the local economy. In particular, it aids the "National Industrial Development and Logistics Program" of Vision 2030 by developing local talents and expertise in key technological areas including IoT to support local industries.
Students who finished all junior-level courses of the following majors are eligible to enroll in this concentration:
A student of other majors can enroll in this concentration if he can fulfill the prerequisite requirements of all concentration courses. For the concentration to be registered in the students' records, the student should finish all the concentration courses successfully.
|COE 450: Introduction to Smart Systems|
|COE 454: Internet of Things|
|CISE 464: Industrial Internet of Things|
|ICS 474: Big Data Analytics|
Description of Courses
COE 450: Introduction to Smart Systems (3-0-3)
Introduction to smart systems. Sensors and actuators: working principles, classifications, performance, characteristics, interfacing with feedback control, and data acquisition. Embedded systems: architecture, types, and interfacing. Real-time operating systems: components, requirements, configuration, and scheduling. Embedded software: development, software stack, hardware abstraction, and tools. Power management and energy harvesting for embedded systems.
Prerequisite: EE 203 or EE 236
COE 454: Internet of Things (3-0-3)
IoT systems design and architecture: elements of IoT system, potentials, constraints, and applications. IoT access technologies. IoT networking protocols such as 6LoWPAN. IoT application layer protocols, Wireless Personal Area Network (WPAN) such as ZigBee. Low Power Wide Area Network (LPWAN) such as LoRaWAN, Machine-to-Machine (M2M), and Machine-to-Cloud (M2C) communication. IoT network architecture: cloud, fog, and edge layers. IoT system security. Data analytics for IoT.
Prerequisite: COE 344 or ICS 343 or EE 400
CISE 464: Industrial Internet of Things (3-0-3)
Internet of Things (IoT) technology and Industrial Control Systems (ICS) for Industry 4.0, IoT/IIoT reference architectures and data flow, industrial communication technologies and networking protocols, highly distributed system architectures and computing platforms, digital twins, ICS security, predictive analytics, maintenance, and system optimization. Embedded intelligence in end devices to perform local analytics and optimization. Applications of IIoT in various areas such as the energy sector, manufacturing, and smart cities.
Prerequisite: CISE 318 or COE 344 or ICS 343 or EE400
ICS 474: Big Data Analytics (3-0-3)
Foundations of data mining and big-data analytics; Statistical analysis of very large datasets that do not fit on a single computer; Popular tools for analyzing big data (e.g., Apache Hadoop, Apache Spark, and TensorFlow); Real-world applications of big-data analytics in multiple domains.
Prerequisite: (Math 101 or Math 106) and (ISE 205 or STAT 201 or STAT 211 or STAT 212 or STAT 319) and (ICS 102 or ICS 103 or ICS 104)