School OR Institute
Melbourne Burwood Campus
As Internet of Things (IoT) grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables intelligent interactions between IoT devices such as sensors/actuators, mobile smart phones, smart vehicles to name a few. Context management platforms (CMP) are emerging as a promising solution to deliver the contextual intelligence for IoT in a generic and standardised way. However, a comprehensive solution that allows IoT devices and services to publish, consume, monitor, and share context across an IoT ecosystem is still in its infancy.
The student will be part of Centre for IoT Ecosystem Research and Experimentation.
An important aspect of context processing that has not been fully addressed in existing CMPs is context prediction. Context- and situation prediction is referred to the process of exploiting expected future context of IoT entities based on the historical context and real-time awareness of current variables. A CMP that supports context prediction offers distinct advantages to the context consumers, which enables a range of new use-cases, services and applications. Hence, it is important to investigate, design, and implement a generic mechanism that allows context prediction of future context of IoT entities. The student will be expected to develop theoretical models for context- and situation prediction, validate them with prototype development and evaluation. The prototype will be integrated with Context-as-a-Service IoT platform.
Applications will remain open until a candidate has been appointed
This scholarship is available over 3 years.
- Stipend of $27,596 per annum tax exempt (2019 rate)
- International students only: Tuition fee and overseas health coverage for the duration of 4 years
To be eligible you must:
- be either a domestic or international candidate
- meet Deakin's PhD entry requirements
- be enrolling full time and hold an honours degree (first class) or an equivalent standard master's degree with a substantial research component.
Please refer to the research degree entry pathways page for further information.
Additional desirable criteria include:
- Background in distributed systems, web programming, AI, machine learning, semantic computing is desirable. Strong software engineering skills and knowledge of Java, C++ and Python are important.
How to apply
Please apply using the expression of interest form