Ioan Salomie

Prof. Ioan Salomie

Professor at Computer Science Department, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu street, Cluj-Napoca, Romania

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 January 2018 in Sensors
Verified
Journal Article
Open access
PDF file
Claudia Daniela Antal (Pop)Claudia Daniela Antal (Pop)
Claudia Daniela Antal (Pop)
Tudor CioaraTudor Cioara
Tudor Cioara
Antal MarcelAntal Marcel
Antal Marcel
Ionut AnghelIonut Anghel
Ionut Anghel
Ioan SalomieIoan Salomie
Ioan Salomie
MBMassimo Bertoncini
Massimo Bertoncini
In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

27 February 2021 in Future Internet
Verified
Journal Article
Open access
PDF file
Claudia Daniela Antal (Pop)Claudia Daniela Antal (Pop)
Claudia Daniela Antal (Pop)
Tudor CioaraTudor Cioara
Tudor Cioara
Ionut AnghelIonut Anghel
Ionut Anghel
Antal MarcelAntal Marcel
Antal Marcel
Ioan SalomieIoan Salomie
Ioan Salomie
The Distributed Ledger Technology (DLT) provides an infrastructure for developing decentralized applications with no central authority for registering, sharing, and synchronizing transactions on digital assets. In the last years, it has drawn high interest from the academic community, technology developers, and startups mostly by the advent of its most popular type, blockchain technology. In this paper, we provide a comprehensive overview of DLT analyzing the challenges, provided solutions or alternatives, and their usage for developing decentralized applications. We define a three-tier based architecture for DLT applications to systematically classify the technology solutions described in over 100 papers and startup initiatives. Protocol and Network Tier contains solutions for digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scalability and Interoperability Tier solutions address the scalability and interoperability issues with a focus on blockchain technology, where they manifest most often, slowing down its large-scale adoption. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design and implementation of traditional systems.

Ionut AnghelIonut Anghel
Ionut Anghel
Tudor CioaraTudor Cioara
Tudor Cioara
Dorin MoldovanDorin Moldovan
Dorin Moldovan
Antal MarcelAntal Marcel
Antal Marcel
Claudia Daniela Antal (Pop)Claudia Daniela Antal (Pop)
Claudia Daniela Antal (Pop)
Viorica Rozina ChifuViorica Rozina Chifu
Viorica Rozina Chifu
The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

5 October 2020 in Sensors
Verified
Journal Article
Open access
PDF file
Claudia Daniela Antal (Pop)Claudia Daniela Antal (Pop)
Claudia Daniela Antal (Pop)
Antal MarcelAntal Marcel
Antal Marcel
Tudor CioaraTudor Cioara
Tudor Cioara
Ionut AnghelIonut Anghel
Ionut Anghel
Ioan SalomieIoan Salomie
Ioan Salomie
Nowadays, the adoption of demand response programs is still lagging due to the prosumers’ lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of transparency, openness, improved control, and increased active participation of prosumers. Nevertheless, even though in general the transparency of the public blockchain is a desirable feature in the energy domain, the prosumer energy data is sensitive and rather private, thus, a privacy-preserving solution is required. In this paper, we present a decentralized implementation of demand response programs on top of the public blockchain which deals with the privacy of the prosumer’s energy data using zero-knowledge proofs and validates on the blockchain the prosumer’s activity inside the program using smart contracts. Prosumer energy data is kept private, while on the blockchain it is stored a zero-knowledge proof that is generated by the prosumer itself allowing the implementation of functions to validate potential deviations from the request and settle prosumer’s activity. The solution evaluation results are promising in terms of ensuring the privacy of prosumer energy data stored in the public blockchain and detecting potential data inconsistencies.

10 July 2019 in Sensors
Verified
Journal Article
Open access
PDF file
Claudia Daniela Antal (Pop)Claudia Daniela Antal (Pop)
Claudia Daniela Antal (Pop)
Antal MarcelAntal Marcel
Antal Marcel
Tudor CioaraTudor Cioara
Tudor Cioara
Ionut AnghelIonut Anghel
Ionut Anghel
DSDavid Sera
David Sera
MBMassimo Bertoncini
Massimo Bertoncini
Nowadays, it has been recognized that blockchain can provide the technological infrastructure for developing decentralized, secure, and reliable smart energy grid management systems. However, an open issue that slows the adoption of blockchain technology in the energy sector is the low scalability and high processing overhead when dealing with the real-time energy data collected by smart energy meters. Thus, in this paper, we propose a scalable second tier solution which combines the blockchain ledger with distributed queuing systems and NoSQL (Not Only SQL database) databases to allow the registration of energy transactions less frequently on the chain without losing the tamper-evident benefits brought by the blockchain technology. At the same time, we propose a technique for tamper-evident registration of smart meters’ energy data and associated energy transactions using digital fingerprinting which allows the energy transaction to be linked hashed-back on-chain, while the sensors data is stored off-chain. A prototype was implemented using Ethereum and smart contracts for the on-chain components while for the off-chain components we used Cassandra database and RabbitMQ messaging broker. The prototype proved to be effective in managing a settlement of energy imbalances use-case and during the evaluation conducted in simulated environment shows promising results in terms of scalability, throughput, and tampering of energy data sampled by smart energy meters.

26 March 2019
Verified
Book Chapter
Open access
PDF file
SVSalvatore Vitabile
Salvatore Vitabile
MMMichal Marks
Michal Marks
DSDragan Stojanović
Dragan Stojanović
SPSabri Pllana
Sabri Pllana
Jose M. MolinaJose M. Molina
Jose M. Molina
Ioan SalomieIoan Salomie
Ioan Salomie
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions.