Research Project: SynergyNet: Enerji İnterneti için Blokzinciri, Akıllı Sözleşme ve Federe Öğrenme
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Contributors
Funders
ID
TB.00665
Authors
Özkasap, Öznur
Faculty Member
Publications
Blockchain-enabled energy trading and battery-based sharing in microgrids
(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Özkasap, Öznur; Zekiye, Abdulrezzak; Bouachir, Ouns; Aloqaily, Moayad; Department of Computer Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Carbon footprint reduction can be achieved through various methods, including the adoption of renewable energy sources. The installation of such sources, like photovoltaic panels, while environmentally beneficial, is cost-prohibitive for many. Those lacking photovoltaic solutions typically resort to purchasing energy from utility grids that often rely on fossil fuels. Moreover, when users produce their own energy, they may generate excess that goes unused, leading to inefficiencies. To address these challenges, this paper proposes innovative blockchain-enabled energy-sharing algorithms that allow consumers -without financial means- to access energy through the use of their own energy storage units. We explore two sharing models: a centralized method and a peer- to- peer (P2P) one. Our analysis reveals that the P2P model is more effective, enhancing the sharing process significantly compared to the centralized method. We also demonstrate that, when contrasted with traditional battery-supported trading algorithm, the P2P sharing algorithm substantially reduces wasted energy and energy purchases from the grid by 73.6%, and 12.3% respectively. The proposed system utilizes smart contracts to decentralize its structure, address the single point of failure concern, improve overall system transparency, and facilitate peer-to-peer payments. © 2024 IEEE.
Spatial and thermal aware methods for efficient workload management in distributed data centers
(Elsevier, 2024) Ali, Ahsan; Özkasap, Öznur; N/A; Department of Computer Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Geographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.
CBWO: a novel multi-objective load balancing technique for cloud computing
(Elsevier, 2025) Hayyolalam, Vahideh; Özkasap, Öznur; Hayyolalam, Vahideh; Department of Computer Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
In cloud computing systems, the growing demand for diverse applications has led to challenges in resource allocation and workload distribution, resulting in increased energy consumption and computational costs. To address these challenges, we propose a novel load-balancing method, namely CBWO, that integrates Chaos theory with the Black Widow Optimization algorithm. Our approach is designed to optimize cloud computing environments by improving energy efficiency and resource utilization. We employ CloudSim for simulations, evaluating key performance metrics such as energy consumption, resource utilization, makespan, task completion time, and imbalance degree. The experimental results demonstrate the superiority of our method, achieving average improvements of 67.28% in makespan and 29.03% in energy consumption compared to existing solutions.
Anonyma: anonymous invitation-only registration in malicious adversarial model
(Elsevier, 2025) Boshrooyeh, Sanaz Taheri; Küpçü, Alptekin; Özkasap, Öznur; Boshrooyeh, Sanaz Taheri; Graduate School of Sciences and Engineering; Department of Computer Engineering; Yes; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; College of Engineering
In invitation-based systems, a new user can register only after obtaining a threshold number of invitations from existing members. The newcomer submits these invitations to the system administrator, who verifies their legitimacy. In doing so, the administrator inevitably learns who invited whom. This inviter–invitee relationship is itself privacy-sensitive information, since knowledge of it can enable inference attacks in which an invitee’s profile (e.g., political views or location) is deduced from the profiles of their inviters. To address this problem, we propose (Formula presented), an anonymous invitation-based system in which even a corrupted administrator, colluding with a subset of members, cannot determine inviter–invitee relationships. We formally define the notions of inviter anonymity and invitation unforgeability, and provide formal proofs that (Formula presented) achieves both against a malicious and adaptive adversary. Our design ensures constant cost for authenticating new registrations, unlike existing approaches where invitation generation and verification incur overhead linear in the total number of members. Moreover, (Formula presented) scales efficiently: once a user joins, the administrator can immediately issue credentials enabling the newcomer to act as an inviter without re-keying existing members. We also design (Formula presented), a cross-network extension that supports anonymous third-party authentication, allowing invitations issued in one system to be used for registration in another.
Distributed asynchronous rendezvous planning on the line for multi-agent systems
(Elsevier, 2024) Özkasap, Öznur; Ozsoyeller, Deniz; Department of Computer Engineering; Yes; College of Engineering
Multi-agent systems have become increasingly significant in various application areas such as search-andrescue, exploration, surveillance, and assembly. In this study, we focus on the asynchronous autonomous rendezvous planning in multi-robot (i.e. multi-agent) systems. The objective is that the robots located in linear environments to gather rapidly at a previously unknown rendezvous location. We consider that no robot knows the positions of the other robots and its own global position. Furthermore, the robot does not know its initial distance to any other robot. Our focus is on the asynchronous case where it is not required the robots to start executing the algorithm simultaneously. We propose and develop a rendezvous planning algorithm, namely MAR, that combines distributed coordination and online motion planning. We theoretically analyze the performance of our algorithm and show that it has a constant competitive ratio. Our extensive simulations demonstrate the performance and scalability through the analysis of the key performance metrics of interest including competitive ratio, distance traveled, total time, number of rounds, and number of meetings. Additionally, we demonstrate the performance and applicability of our algorithm MAR through experimental analysis in a realistic robotic simulator.
