Raja Jurdak is a Professor of Distributed Systems, Chair in Applied Data Sciences, and Director of the Trusted Networks Lab at the School of Computer Science, Queensland University of Technology. He has over 18 years of experience in network and mobility modeling, internet of things (IoT), and blockchain, having established and led the world-leading Distributed Sensing Systems Group at CSIRO’s Data61 from 2011-2019 before joining QUT. He has published over 190 peer-reviewed articles and 2 authored books that have collectively been cited over 7700 times, with an h-index of 39. He was named among the top 0.9% cited researchers globally for the calendar year 2019, Stanford University in 2020. He is TPC chair of IEEE ICBC 2021, serves on the editorial board of Ad Hoc Networks (ERA A-rated), and has been a visiting academic at Oxford and MIT.
Email: r.jurdak@qut.edu.au
DVP term expires December 2024
Presentations
Blockchain for Cyberphysical Systems
This talk explores how Blockchain (BC) technology has the potential to overcome challenges in the current cyber-physical system (CPS) environment. BC is a timestamp ledger of blocks that is used for storing and sharing data in a distributed manner. BC has attracted attention from practitioners and academics in different disciplines, including law, finance, and computer science, due to its use of distributed structure, immutability and security and privacy. However, applying blockchain in a cyber-physical system (CPS) is not straightforward and involves challenges, including lack of scalability, resource consumption, and delay.
This talk will provide a comprehensive study on blockchain for CPS. CPS and the existing solutions in CPS and will outline the limitations are presented. The key features of blockchain and its salient features which makes it an attractive solution for CPS are discussed. The fundamental challenges in adopting blockchain for CPS including scalability, delay, and resource consumption are presented and described. Blockchain applications in smart grids, smart vehicles, supply chain; and IoT Data marketplaces are explored. The future research directions to further improve blockchain performance in CPS is also provided.
Blockchain for Distributed Energy Management
Human society is facing the grand challenges of climate change and ever-increasing energy demand. These challenges require us to re-shape the operation patterns of our energy generation, transmission, and consumption patterns. Electrical power systems hence need to be adapted to operate in a more efficient and sustainable manner, for example, accommodate more renewable energy. With this background, the concept of “smart grid” was proposed in the early 21st century, setting up the strategic goal to develop next-generation power systems.
The power distribution network which is central to smart grids is significantly characterized by high penetration of distributed renewable resources, flexible loads, and advanced sensing infra- structures. The transformation from a centralised to distributed energy generation pattern has led to the emergence of energy prosumers (producers-and-consumers), who are capable of generating and consuming energy simultaneously, for example, a building equipped with solar panels. This naturally raises the need for establishing an energy trading mechanism that is secure, maintains participant privacy, and fosters energy economics.
Recently blockchain has attracted tremendous attention as a means to provide a distributed, secure, and anonymous framework for energy trading. Blockchain employs changeable public keys (PKs) to identify users, thus providing a level of anonymity. However, existing solutions suffer from a lack of privacy, processing and packet overheads, and reliance on trusted third parties to secure the trade. To address these challenges, we propose a secure and private blockchain frame- work. SPB enables the energy producers and consumers to directly negotiate the energy price without third party involvement, while protecting the anonymity of prosumers. SPB includes three novel features:
Mobility and Diffusion in Dynamic Networks
Networks are all around us – from social network relationships to transport and communications networks. They capture the links within our world. These networks can be highly dynamic, where participants move around and their relationships to and interactions with other participants change accordingly. It is therefore very important to capture the movement of network nodes through mobility models in order to be able to forecast the future state of the network. Understanding the node mobility, however, is not enough. Consider a disease spreading through the movement of people, such as COVID-19. While understanding how people move is necessary to understand how the disease spreads, there is also a need to understand the process by which the disease spread and how this process interacts with the people movement process. We refer to this as diffusion modelling, which builds on the underlying mobility modelling. This talk is concerned with diffusion modelling on highly dynamic networks, to cover diffusion processes ranging from disease spread, the spread of computer viruses, or the diffusion of trust in a network.
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