FLEXIGY project aims at creating a Virtual Energy Community concept that will operate “behind the meters” – i.e. the development of flexibility management services to allow energy aggregators to involve energy resources with private bilateral contracts, thus enabling the use of distributed energy resources in a collaborative way, with significantly larger community benefits when compared to individual optimization, supported by a cyber-physical infrastructure required to support load flexibility management with community-wide optimization mechanisms (which includes storage management, production management and load management), thus ensuring the security and satisfaction levels for the economic viability of the proposed solution. In this project energy transactions will be based on the blockchain concept and virtual currencies, in order to increase trust and end-user engagement.
In this meeting Luis Lino Ferreira and Rafael Rocha presented their work on the Flex Offer concept and discussed with the other partners the architecture for the system.
 

Ramiro Robles represented CISTER/ISEP in the F2F technical board meeting of the project SCOTT held at the premises of our partner NXP in Munich, Germany on the 7th of February 2019.
This meeting was mainly focused on how the different WPs and Technology lines of the project will answer the comments of the reviewers provided in the official feedback after the first year of the project. In addition, the meeting was also focused on refining the impact and exploitation definition of the project highlighting the main measurable outcomes expected of the project and how we will achieve the target values.
CISTER/ISEP acted as the chair of the technical board, leader of the reference architecture of the project and representative of the aeronautics domain.
 

During the last month four new PhD students joined CISTER.

Hajar and Amir from Iran, Ênio from Brasil and Jingjing from China, are already aiming their brain power at the next three years, in which they will be doing research in topics such as reliable 5G communications, cooperative autonomous control, and multiprocessor platforms for embedded safety critical systems.

Hajar Baghcheband was born in Shiraz, Iran. She received the B.Sc. in computer software engineering from IAU of Shiraz and the M.Sc. degrees in the field of Information Technology from Graduate University of Advanced Technology (GUAT). Her master thesis was granted by Iran Telecommunications Research Center (ITRC) and focused on applying data mining techniques to intrusion detection system. Since 2009, she was lecturer at higher education institutes and colleges of Shiraz.
In 2016, her new role in company as project management and database administrator started which their project shortlisted for World Summit Award.
Since September 2018, Hajar started to pursue her education toward PhD in Computer Engineering at the Faculty of Engineering of the University of Porto. Her research interests are Machine Learning, Multi agent System, Artificial Intelligence, Data mining, Business Intelligence, Decision Support system and Database.

Amir Hossein Farzamiyan was born in Shiraz, Iran, in 1985. He received both the B.Sc. and the M.Sc. degrees in the field of Electrical and Electronic engineering. He is a graduate with strong communication and organizational skills gained in his study, research and work experience in communication networks and Wireless Communication, now seeking to pursue his study toward PhD in the field of telecommunication.
His research interests are Wireless (Radio) Access Networks, 5G Networks, Internet of Things, Software Defined Network, Advanced Wireless Communications, Cyber-Security, Network Packet Routing Protocol, Network Topology.

Ênio Filho was born in 1984 in Brazil. He has a BSc degree in Automation and Control (2006) and a MSc degree in Mechatronics Systems (2012) both from Universidade de Brásilia (UnB). He is an assistant professor at Instituto Federal de Goiás (IFG), in Brazil.
He is pursuing a PhD in Electrical and Computer Engineering at the Faculty of Engineering of the University of Porto (PDEEC).His biggests interests are in autonomous vehicles, communication networks and artificial intelligence in real time applications.

Jingjing Zheng was born in 1991 in Hubei, China. From a very young age he showed enormously passionate about computers and mathematics. During his college years, he participated in China Undergraduate Mathematical Contest in Modeling three times, and obtained the Second Prize in Jiangxi Province, the First Prize in Jiangxi Province, the Second Prize in the Nation, respectively.
In 2015 he began to study Control Science and Engineering at the Guangdong University of Technology, where he had done the master thesis of Research on Resource Allocation and Multi-agent Co-optimization for Dynamic Heterogeneous Multi-core Processors, and having obtained a good result through peer reviews. After, he received the M.S.degree from Guangdong University of Technology, China, in June 2018.
Currently his research interests are in Real Time Embedded Systems, Multi-core processors and Optimization Algorithm.
 

Robert I. Davis gave a distinguished seminar on "Transferring Real-Time Systems Research into Industrial Practice"
During this talk, Dr Robert I. Davis discussed two impact case studies where real-time systems research has been successfully transferred into industrial practice. In each case, the technology created was translated into viable commercial products and led to significant advances in the automotive electronics and avionics domains, providing substantial returns on investment for the companies using the technology.

Robert I. Davis is a Reader in the Real-Time Systems Research Group at the University of York, UK. Robert received his PhD in Computer Science from the University of York in 1995. Since then he has founded three start-up companies, all of which have succeeded in transferring real-time systems research into commercial products. Robert’s research interests include the following aspects of real-time systems: scheduling algorithms and analysis for single processor, multiprocessor and networked systems; analysis of cache related pre-emption delays, mixed criticality systems, and probabilistic hard real-time systems.

PHOTOS:
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Stefano Zanero from Politecnico Di Milano presented a talk on “Breaking the Laws of Robotics: Attacking Industrial Robots”, where he discussed vulnerabilities of industrial robots and the work achieved by his research group to address potential attacks. Stefano presented a recent attack performed by his students to alter the precision of an industrial robot which can lead to potential harm to products and even users. Various use cases were discussed including an attack via insecure web interfaces, weak authentication protocols, exposure of industrial routers, etc. The impact of existing domain specific languages in this area was also discussed during this talk. More information around this work is available at robosec.org.

Stefano Zanero received a PhD in Computer Engineering from Politecnico di Milano, where he is currently an associate professor. His research focuses on malware analysis, cyberphysical security, and cybersecurity in general. Besides teaching "Computer Security" and "Computer Forensics" at Politecnico, he has an extensive speaking and training experience in Italy and abroad. He co-authored over 70 scientific papers and books. He is a Senior Member of the IEEE (for which he sits on the MGA board), the IEEE Computer Society (for which he is a member of the Board of Governors), and a lifetime senior member of the ACM. Stefano co-founded the Italian chapter of ISSA (Information System Security Association). He has been named a Fellow of ISSA and sits in its International Board of Directors. Stefano is also a co-founder and chairman of Secure Network, a leading information security consulting firm based in Milan and in London; a co-founder of 18Months, a cloud-based ticketing solutions provider; and a co-founder of BankSealer, a startup in the FinTech sector that addresses fraud detection through machine learning techniques.

PHOTOS:
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