Description:

With the emergence of the Internet of Things (IoT) and wireless sensor networks (WSNs), sensor devices are deployed across the globe in different applications such as healthcare, industry automation, precision agriculture, smart cities, and intelligent transportations. Recently, Cisco estimated that there would be approximately 850 zettabytes of data generated from IoT devices. However, the issues of connectivity and sensor data quality arise with large scale deployments, where devices are fighting against each other for network resources while at the same time may not even generate high-quality sensor data. These issues will be investigated in this project, by

• Realising a large scale IoT system (hosting multiple applications, e.g., healthcare and building management) through physically establishing or software simulating a large wireless sensor network.
• Generating a large amount of real-time sensor data according to applications requirements and observing/investigating system behaviours in terms of network performance and sensor data quality.
• Designing an adaptive machine learning approach to monitor and control system performance.

 

Type:

Undergraduate

Outcome:

Prerequisites

None

Specialisations

• Computer Systems Engineering
• Software Engineering

Categories

• AI & Machine learning
• Embedded Systems
• Intelligent Systems & Industrial Informatics

Supervisor

• Kevin I-Kai Wang

Co-supervisor

Team

• Tianhang Chen
• Huyue Wang

Lab

Lab allocations have not been finalised