Modern cities are a complex overlay of multiple systems, such as transport and power grids, that can be represented as networks. The concept of smart cities involves digitization of these networks to generate data around the dynamic context and interactions among entities across these cities, in order to improve operational efficiency, provide better services, and create value for the population. The transition to smart cities involves challenges such as providing transparency and trust in the generated data, protecting the sensitive information of stakeholders, and creating reliable models that are representative of realistic populations dynamics. In this talk, I will discuss our work on designing trusted networks using blockchain for balancing trust and transparency in connected vehicles, smart grids, as well an integrated transport-energy network called the Internet of Mobile Energy. I will also cover our work on using transport and mobility data from smart cities to predict disease spread risk.

Raja Jurdak is a Professor of Distributed Systems and Chair in Applied Data Sciences at Queensland University of Technology, and Director of the Trusted Networks Lab. He received a PhD in information and computer science from the University of California, Irvine. He previously established and led the Distributed Sensing Systems Group at CSIRO’s Data61, where he maintains a visiting scientist role. He also spent time as visiting academic at MIT and Oxford University in 2011 and 2017. His research interests include trust, mobility and energy-efficiency in networks. Prof. Jurdak has published over 190 peer-reviewed publications, including two authored books most recently on blockchain in cyberphysical systems in 2020. He serves on the editorial board of Ad Hoc Networks, and on the organizing and technical program committees of top international conferences, including Percom, ICBC, IPSN, WoWMoM, and ICDCS. He was TPC co-chair of ICBC in 2021. He is a conjoint professor with the University of New South Wales, and a senior member of the IEEE.

Breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspect of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing. Nevertheless, the advent of a new breed of intelligent devices ranging from drones to self-driving vehicles, makes cloud-based ML inadequate. This talk will present the vision of distributed edge intelligence featuring key enablers, architectures, algorithms and some recent results.

Dr. Mehdi Bennis is an Associate Professor at the Centre for Wireless Communications, University of Oulu, Finland, an Academy of Finland Research Fellow and head of the intelligent connectivity and networks/systems group (ICON). His main research interests are in radio resource management, heterogeneous networks, game theory and machine learning in 5G networks and beyond. He has co-authored one book and published more than 200 research papers in international conferences, journals and book chapters. He has been the recipient of several prestigious awards including the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2016 Best Tutorial Prize from the IEEE Communications Society, the 2017 EURASIP Best Paper Award for the Journal of Wireless Communications and Networks, the all-University of Oulu award for research, In 2019 Dr Bennis received the IEEE ComSoc Radio Communications Committee Early Achievement Award. Dr. Bennis is an IEEE Fellow.

The Intelligent Transportation System (ITS) has become essential for the economical and technological development of a country. The maturity of communication technologies (Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V)) and the amalgamation of smart grids, electric vehicles (EVs) and energy trading resulted in a storm of research opportunities for green ITS. In addition, the combination of vehicular communication technologies and ITS enable efficient selection of EV charging stations (CS) and scheduling EVs charging requirements in real-time. However, the untrusted centralized nature of energy markets and EV charging infrastructures result in several privacy and security threats to EV user’s private information. These security and privacy threats include targeted advertisements, privacy leakage, selling data to a third party, etc. In this work, we propose BlockEV, a blockchain-based efficient CS selection protocol for EVs to ensure the security and privacy of the EV users, availability of the reserved time slots at CSS, high Quality of Service (QoS) and enhanced EV user comfort. First, a blockchain-based framework is introduced to implement secure charging services and trusted reservation for EVs with the execution of a smart contract. Second, we focus on efficient CS selection and propose a mechanism for EVs to select the CS locally without sharing private information to CS, while fulfilling their service requirements. Evaluations show that the proposed BlockEV is scalable with significantly low blockchain transaction and storage overhead.

Kaiwen Zhang is an Assistant Professor in the Department of Software and IT Engineering at ÉTS Montréal (University of Québec). Previously, he was an Alexander von Humboldt postdoctoral fellow in Computer Science at the TU Munich (2015-2017) and a member of the Middleware Systems Research Group. Dr. Zhang obtained his B.Sc. and M.Sc. at McGill University in Montréal and his Ph.D. at the University of Toronto. His research interests include blockchain technologies, publish/subscribe systems, massively multiplayer online games, and software-defined networking. Dr. Zhang’s expertise lies at the intersection of distributed systems, networking, and data management. His research is published in premier distributed systems conferences such as IEEE ICDCS and ACM Middleware.