Visible to the public Biblio

Filters: Author is Tzovaras, Dimitrios  [Clear All Filters]
2020-09-28
Patsonakis, Christos, Terzi, Sofia, Moschos, Ioannis, Ioannidis, Dimosthenis, Votis, Konstantinos, Tzovaras, Dimitrios.  2019.  Permissioned Blockchains and Virtual Nodes for Reinforcing Trust Between Aggregators and Prosumers in Energy Demand Response Scenarios. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe). :1–6.
The advancement and penetration of distributed energy resources (DERs) and renewable energy sources (RES) are transforming legacy energy systems in an attempt to reduce carbon emissions and energy waste. Demand Response (DR) has been identified as a key enabler of integrating these, and other, Smart Grid technologies, while, simultaneously, ensuring grid stability and secure energy supply. The massive deployment of smart meters, IoT devices and DERs dictate the need to move to decentralized, or even localized, DR schemes in the face of the increased scale and complexity of monitoring and coordinating the actors and devices in modern smart grids. Furthermore, there is an inherent need to guarantee interoperability, due to the vast number of, e.g., hardware and software stakeholders, and, more importantly, promote trust and incentivize the participation of customers in DR schemes, if they are to be successfully deployed.In this work, we illustrate the design of an energy system that addresses all of the roadblocks that hinder the large scale deployment of DR services. Our DR framework incorporates modern Smart Grid technologies, such as fog-enabled and IoT devices, DERs and RES to, among others, automate asset handling and various time-consuming workflows. To guarantee interoperability, our system employs OpenADR, which standardizes the communication of DR signals among energy stakeholders. Our approach acknowledges the need for decentralization and employs blockchains and smart contracts to deliver a secure, privacy-preserving, tamper-resistant, auditable and reliable DR framework. Blockchains provide the infrastructure to design innovative DR schemes and incentivize active consumer participation as their aforementioned properties promote transparency and trust. In addition, we harness the power of smart contracts which allows us to design and implement fully automated contractual agreements both among involved stakeholders, as well as on a machine-to-machine basis. Smart contracts are digital agents that "live" in the blockchain and can encode, execute and enforce arbitrary agreements. To illustrate the potential and effectiveness of our smart contract-based DR framework, we present a case study that describes the exchange of DR signals and the autonomous instantiation of smart contracts among involved participants to mediate and monitor transactions, enforce contractual clauses, regulate energy supply and handle payments/penalties.
2020-04-13
Papachristou, Konstantinos, Theodorou, Traianos, Papadopoulos, Stavros, Protogerou, Aikaterini, Drosou, Anastasios, Tzovaras, Dimitrios.  2019.  Runtime and Routing Security Policy Verification for Enhanced Quality of Service of IoT Networks. 2019 Global IoT Summit (GIoTS). :1–6.
The Internet of Things (IoT) is growing rapidly controlling and connecting thousands of devices every day. The increased number of interconnected devices increase the network traffic leading to energy and Quality of Service efficiency problems of the IoT network. Therefore, IoT platforms and networks are susceptible to failures and attacks that have significant economic and security consequences. In this regard, implementing effective secure IoT platforms and networks are valuable for both the industry and society. In this paper, we propose two frameworks that aim to verify a number of security policies related to runtime information of the network and dynamic flow routing paths, respectively. The underlying rationale is to allow the operator of an IoT network in order to have an overall control of the network and to define different policies based on the demands of the network and the use cases (e.g., achieving more secure or faster network).