I am currently a senior lecturer in software engineering at Deakin University, Australia. My research interests are in pervasive computing (IoT/cyber-physical systems) and software engineering. My teaching has typically been related to cloud & mobile applications, IoT and software engineering (so far!).

Potential research projects
I am particularly interested in supervising the following PhD projects:

Design and Development of Inclusive IoT Systems
Building inclusive IoT systems is non-trivial due the complexity of IoT technology, which encompasses heterogenous interconnected sensors, actuators, devices and services that work together. Ensuring inclusivity across this complex network of devices and services requires specialised knowledge and expertise. Furthermore, there are also challenges with identifying diverse user needs, lack of standards and guidelines, costs, education and awareness
See: https://nfernando.org/2023/08/phd-project-design-and-development-of-inclusive-iot-systems/

Quality of Service-Aware Edge-Fog-Cloud technologies for Smart Grid
Edge and fog technologies support efficient real-time data analytics in Smart Grids, enabling immediate insights into power consumption patterns, grid stability, and potential faults. This information can be utilised to optimise energy distribution, predict demand, and prevent potential failures. However, applying edge and fog technologies to a smart grid architecture is non-trivial due to challenges with distributed ownership, connectivity, data fusion, data storage, load-balancing and issues with security and privacy.
See: https://nfernando.org/2023/08/phd-project-quality-of-service-aware-edge-fog-cloud-technologies-for-smart-grid/

Incentive mechanisms for crowdsourcing computing in smart cities
This research will investigate how the citizens of future smart cities can be incentivised to share their computing resources. Smart cities require support for high-velocity and real-time services involving sensor data, such as autonomous vehicles, environmental monitoring and connected wearables, needing geographically distributed computing resources that are fault-tolerant, trustworthy and highly available. One approach to enable the above is to crowdsource computing resources by enabling users to share their own computing devices. An example of a similar concept is BOINC, which is a mobile app version of SETI@home. However, BOINC uses pre-established mobile devices, for large-scale science projects at global level, and users participate due to altruism. Apart from altruism, broader incentives need to be investigated. We aim to explore how smaller scale problems can use the collective computational power at local (city) level.
Specifically, this research will explore:
1. What are the characteristics of problems that can benefit from citizen-powered computing?
2. Why users will participate and how can users be incentivised to participate? and
3. How can such incentive schemes be technically implemented?

A framework for modelling the carbon footprint of IoT projects
The proliferation of network connected, and sensor-enabled consumer electronic devices, and rise of Internet-of-Things (IoT) technology will increase the demands on computing infrastructure for high-velocity data processing and storage. This can have adverse effects on the environment, due to increased carbon footprint caused by electronic manufacturing, energy consumption and e-waste. However, much of the focus in IoT research has been on technical challenges. IoT frameworks that also take the environmental impacts into consideration are lacking.
In this project, we will investigate how to model and estimate the effects of IoT services and applications on the environment. Possible outcomes will be, methods to measure and estimate an IoT product’s carbon footprint over its life cycle, a framework to classify IoT projects according to environmental effects, and a set of tools to measure and simulate long term effects.

Enabling computing in disaster scenarios
Access to computing resources can be crucial during, and in an aftermath of a disaster. For example, aerial and ground photographs can be used to identify hazardous situations via image processing applications, and infrared heat signature processing software can be used
to detect survivors trapped under debris. However, computing and communication infrastructure often becomes unavailable during disaster scenarios. Moreover, in disaster relief scenarios, required support can rapidly
change with respect to the location, range and resourcefulness of service nodes.
This project will investigate an opportunistic computing model, where computation nodes are formed on-demand. Of particular interest are, exploiting available resources in the vicinity (e.g. smartphones, tablets, drones) and/or a ‘move-in-move-out’ computing infrastructure where the computing resources can be moved to provision users on-demand.

I am also interested in the following areas:
– Using blockchain for edge computing services
– Developing human-centric IoT applications
– Developing robust cyber-physical systems