arXiv subjects

Oznur Ozkasap

Publications and source records attributed to Oznur Ozkasap.

11 recordsLinked to original sources

arXiv2025

DRACO: Data Replication and Collection Framework for Enhanced Data Availability and Robustness in IoT Networks

The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected devices, which can lead to data loss. This paper presents DRACO (Data Replication and Collection), a framework that integrates a distributed hop-by-hop data replication approach with an overhead-free mobile sink-based data collection strategy. DRACO enhances data availability, optimizes replica placement, and ensures efficient data retrieval even under node failures and varying network densities. Extensive ns-3 simulations demonstrate that DRACO outperforms state-of-the-art techniques, improving data availability by up to 15% and 34%, and replica creation by up to 18% and 40%, compared to greedy and random replication techniques, respectively. DRACO also ensures efficient data dissemination through optimized replica distribution and achieves superior data collection efficiency under varying node densities and failure scenarios as compared to commonly used uncontrolled sink mobility approaches namely random walk and self-avoiding random walk. By addressing key IoT data management challenges, DRACO offers a scalable and resilient solution well-suited for emerging use cases.

cs.NI↗

arXiv2024

Synergistic Integration of Blockchain and Software-Defined Networking in the Internet of Energy Systems

Peer-to-peer (P2P) energy trading, Smart Grids (SG), and electric vehicle energy management are integral components of the Internet of Energy (IoE) field. The integration of Software-Defined Networks (SDNs) and Blockchain (BC) technologies into the IoE domain offers potential benefits that have only been studied in the literature in a few works. In this paper, we investigate the state-of-art solutions that leverage both SDNs and blockchain within the realm of the IoE. We categorize these solutions based on the method of integrating SDN and BC into two categories. The first category is the blockchain for SDN, where blockchain enhances the SDN directly. The second category is blockchain and SDN, where both technologies are used to enhance the proposed solutions. We identify three distinct blockchain applications based on their usage: decentralizing the SDN control plane, serving as a decentralized platform, and improving security measures. Similarly, we observe that SDN serves as a performance enhancer, a substitute for traditional networking, and solely as a control and management framework. It is posited that integrating SDNs and blockchain into IoE leads to performance enhancements, improves security, enables decentralized operations, and eliminates single points of failure in the SDN control plane. Additionally, some unaddressed issues, such as energy efficiency, smart contract management, and scalability, are discussed as potential future directions.

cs.DC↗

arXiv2024

Analysis of Blockchain Assisted Energy Sharing Algorithms with Realistic Data Across Microgrids

With escalating energy demands, innovative solutions have emerged to supply energy affordably and sustainably. Energy sharing has also been proposed as a solution, addressing affordability issues while reducing consumers' greed. In this paper, we analyse the feasibility of two energy sharing algorithms, centralized and peer-to-peer, within two scenarios, between microgrids within a county, and between microgrids across counties. In addition, we propose a new sharing algorithm named Selfish Sharing, where prosumers take advantage of consumers' batteries in return for letting them consume part of the shared energy. The results for sharing between microgrids across counties show that the dependency on the grid could be reduced by approximately 5.72%, 6.12%, and 5.93% using the centralized, peer-to-peer and selfish sharing algorithms respectively, compared to trading only. The scenario of sharing between microgrids within a county has an average decrease in dependency on the grid by 5.66%, 6.0%, and 5.80% using the centralized, peer-to-peer and selfish algorithms respectively, compared to trading without sharing. We found that trading with batteries and the proposed sharing algorithms prove to be beneficial in the sharing between microgrids case. More specifically, the case of trading and sharing energy between microgrids across counties outperforms sharing within a county, with P2P sharing appearing to be superior.

cs.DC↗

arXiv2023

ANKA: A Decentralized Blockchain-based Energy Marketplace for Battery-powered Devices

For the purpose of enabling, democratizing, and reducing the fees of peer-to-peer energy trading for battery-powered devices, we propose ANKA as a fully decentralized energy marketplace for peers with battery-powered devices. ANKA utilizes state-of-the-art technologies, namely blockchain, smart contracts, and decentralized applications. Within this marketplace, users who possess surplus energy actively offer their excess energy for trading. Concurrently, consumers can readily explore the energy options available and make purchases according to their individual preferences while taking into consideration the location of the offered energy and voltage compatibility. In addition, we provide a comparison between a centralized traditional market and our proposed solution, identifying that the cost of deploying and operating ANKA is less than the centralized approach. We also position ANKA in comparison to the recent blockchain-based decentralized energy marketplaces by considering the metrics of blockchain type, scope, trading entities and the presence of third parties.

cs.DC↗

arXiv2023

AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors

Cryptocurrencies have become a popular and widely researched topic of interest in recent years for investors and scholars. In order to make informed investment decisions, it is essential to comprehend the factors that impact cryptocurrency prices and to identify risky cryptocurrencies. This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters to identify the factors affecting a cryptocurrency's price and to find risky cryptocurrencies. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. In addition, we used clustering and classification in order to get a better understanding of a cryptocurrency and classify it as risky or not. The analysis revealed that a significant proportion of cryptocurrencies (39%) disappeared from the market, while only a small fraction (10%) survived for more than 1000 days. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume. Moreover, we clustered cryptocurrencies into five distinct groups using their on-chain parameters, which provides investors with a more comprehensive understanding of a cryptocurrency when compared to those clustered with it. Finally, by implementing multiple classifiers to predict whether a cryptocurrency is risky or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.

q-fin.ST↗

arXiv2021

MER-SDN: Machine Learning Framework for Traffic Aware Energy Efficient Routing in SDN

Software Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of information is generated in the controller of an SDN based network. Machine learning gives the ability to computers to progressively learn from data without having to write specific instructions. In this work, we propose MER-SDN: a machine learning framework for traffic-aware energy efficient routing in SDN. Feature extraction, training, and testing are the three main stages of the learning machine. Experiments are conducted on Mininet and POX controller using real-world network topology and dynamic traffic traces from SNDlib. Results show that our approach achieves more than 65\% feature size reduction, more than 70% accuracy in parameter prediction of an energy efficient heuristics algorithm, also our prediction refine heuristics converges the predicted value to the optimal parameters values with up to 25X speedup as compared to the brute force method.

cs.NI↗

arXiv2021

Demo -- Zelig: Customizable Blockchain Simulator

As blockchain-based systems see wider adoption, it becomes increasingly critical to ensure their reliability, security, and efficiency. Running simulations is an effective method of gaining insights on the existing systems and analyzing potential improvements. However, many of the existing blockchain simulators have various shortcomings that yield them insufficient for a wide range of scenarios. In this demo paper, we present Zelig: our blockchain simulator designed with the main goals of customizability and extensibility. To the best of our knowledge, Zelig is the only blockchain simulator that enables simulating custom network topologies without modifying the simulator code. We explain our simulator design, validate via experimental analysis against the real-world Bitcoin network, and highlight potential use cases.

cs.CR↗

arXiv2021

Edge Intelligence for Empowering IoT-based Healthcare Systems

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.

cs.LG↗

arXiv2020

LogDos: A Novel Logging-based DDoS Prevention Mechanism in Path Identifier-Based Information Centric Networks

Information Centric Networks (ICNs) have emerged in recent years as a new networking paradigm for the next-generation Internet. The primary goal of these networks is to provide effective mechanisms for content distribution and retrieval based on in-network content caching. The design of different ICN architectures addressed many of the security issues found in the traditional Internet. Therefore, allowing for a secure, reliable, and scalable communication over the Internet. However, recent research studies showed that these architectures are vulnerable to different types of DDoS attacks. In this paper, we propose a defense mechanism against distributed denial of service attacks (DDoS) in path-identifier based information centric networks. The proposed mechanism, called LogDos, performs GET Message logging based filtering and employs Bloom filter based logging to store incoming GET messages such that corresponding content messages are verified, while filtering packets originating from malicious hosts. We develop three versions of LogDos with varying levels of storage overhead at LogDos-enabled router. Extensive simulation experiments show that LogDos is very effective against DDoS attacks as it can filter more than 99.98 % of attack traffic in different attack scenarios while incurring acceptable storage overhead.

cs.NI↗

arXiv2020

Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading

Scalability and security problems of the centralized architecture models in cyberphysical systems have great potential to be solved by novel blockchain based distributed models.A decentralized energy trading system takes advantage of various sources and effectively coordinates the energy to ensure optimal utilization of the available resources. It achieves that goal by managing physical, social and business infrastructures using technologies such as Internet of Things (IoT), cloud computing and network systems. Addressing the importance of blockchain-enabled energy trading in the context of cyberphysical systems, this article provides a thorough overview of the P2P energy trading and the utilization of blockchain to enhance the efficiency and the overall performance including the degree of decentralization, scalability and the security of the systems. Three blockchain based energy trading models have been proposed to overcome the technical challenges and market barriers for better adoption of this disruptive technology.

cs.DC↗

arXiv2016

Multi-Hop Cluster based IEEE 802.11p and LTE Hybrid Architecture for VANET Safety Message Dissemination

This paper proposes a hybrid architecture, namely VMaSC-LTE, combining IEEE 802.11p based multi-hop clustering and the fourth generation cellular system, Long Term Evolution (LTE), with the goal of achieving high data packet delivery ratio and low delay while keeping the usage of the cellular architecture at the minimum level. In VMaSC-LTE, vehicles are clustered based on a novel approach named VMaSC: Vehicular Multi-hop algorithm for Stable Clustering. From the clustered topology, elected cluster heads operate as dual-interface nodes with the functionality of IEEE 802.11p and LTE interface to link VANET to LTE network. Using various key metrics of interest including data packet delivery ratio, delay, control overhead and clustering stability, we demonstrate superior performance of the proposed architecture compared to both previously proposed hybrid architectures and alternative routing mechanisms including flooding and cluster based routing via extensive simulations in ns-3 with the vehicle mobility input from the Simulation of Urban Mobility (SUMO). The proposed architecture also allows achieving higher required reliability of the application quantified by the data packet delivery ratio at the cost of higher LTE usage determined by the number of cluster heads in the network.

cs.NI↗