Browsing by Author "Dag, Tamer"

Now showing 1 - 5 of 5

Citation - WoS: 1
Citation - Scopus: 4
An Improved Hybrid Stable Election Routing Protocol for Large Scale Wsns
(IEEE, 2021) Hamad, Lana I. S.; Dağ, Tamer; Dag, Tamer; Gucluoglu, Tansal
In the recent years, wireless sensor networks have become prevalent in a diverse range of applications. Throughout the massive usage of wireless sensor networks, some applications require sensing and/or data collection over large areas of interest. In such applications maximizing the network lifetime thus an efficient energy consumption becomes very critical. In this paper, an improved routing protocol for large-scale WSNs based on the well-known Stable Election Protocol is proposed. With this work, the existing Stable Election Protocol is enhanced by introducing low-cost relay nodes that help to increase the lifetime and the throughput of the network. The communication between the sensor nodes and the base station is established through cluster heads and relay nodes. With the relay nodes, the simulation results show an increase in the network stability period by 34.5% and the throughput by 23%.
Citation - WoS: 0
Citation - Scopus: 0
Improving Supply Chain Management Processes Using Smart Contracts in the Ethereum Network Written in Solidity
(Mdpi, 2024) Yigit, Eren; Dağ, Tamer; Dag, Tamer
This paper investigates the potential of integrating supply chain management with blockchain technology, specifically by implementing smart contracts on the Ethereum network using Solidity. The paper explores supply chain management concepts, blockchain, distributed ledger technology, and smart contracts in the context of their integration into supply chains to increase traceability, transparency, and accountability with faster processing times. After investigating these technologies' applications and potential use cases, a framework for smart contract implementation for supply chain management is constructed. Potential data models and functions of a smart contract implementation improving supply chain management processes are discussed. After constructing a framework, the effects of the proposed system on supply chain processes are explained. The proposed framework increases the reliability of the supply chain history due to the usage of DLT (distributed ledger technology). It utilizes smart contracts to increase the manageability and traceability of the supply chain. The proposed framework also eliminates the SPoF (Single Point of Failure) vulnerabilities and external alteration of the transactional data. However, due to the ever-changing and variable nature of the supply chains, the proposed architecture might not be a one-size-fits-all solution, and tailor-made solutions might be necessary for different supply chain management implementations.
Citation - WoS: 1
Citation - Scopus: 2
Increasing Energy Efficiency of Wsns Through Optimization of Mobile Base Station Locations
(IEEE, 2021) Abbas, Sahar S. A.; Dağ, Tamer; Dag, Tamer; Gucluoglu, Tansal
In terms of enhancing overall energy usage, wireless sensor networks (WSNs) can run with minimal energy to extend their lifespan. Due to limited power resources, the optimal base station (BS) location could prolong the overall sensor network's lifetime. In this paper, an algorithm to find the optimal location of BS is proposed. The concept of BS virtual locations grid is used, where BS virtual locations grid within the network's area is created. To find an optimal BS location, the distances between all sensor nodes from virtual locations in the grid are considered, where one of these virtual locations will be chosen as the optimal location. Consequently, BS changes its location to another optimal location each specific number of iteration according to the number of alive sensor nodes within the network (BS mobility). The proposed algorithm is applied to the Stable Election Protocol (SEP) with two and three energy levels. Using the original SEP with two and three energy levels protocols in terms of the network's lifetime and energy consumption, the performance of the algorithm is compared. It is observed that, decreased energy consumption has been achieved, as well as the lifetime of the network has been significantly improved.
Citation - WoS: 1
Citation - Scopus: 2
Modulated Relay Based Stable Election Protocol for Large-Scale Wireless Sensor Networks
(Wiley, 2023) Hamad, Lana I. S.; Dağ, Tamer; Dag, Tamer; Gucluoglu, Tansal
Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy-efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R-SEP (MR-SEP) for large-scale WSN-based IoT applications. MR-SEP is based on the well-known stable election protocol (SEP). MR-SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi-hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR-SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR-SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.
Predicting User Purchases From Clickstream Data: a Comparative Analysis of Clickstream Data Representations and Machine Learning Models
(IEEE-Inst Electrical Electronics Engineers inc, 2025) Tokuc, A. Aylin; Dag, Tamer
Predicting purchase events from e-commerce clickstream data is a critical challenge with significant implications for optimizing marketing strategies and enhancing customer experience. This study addresses this challenge by systematically evaluating and comparing multiple data representations - aggregated session attributes, recent user actions, and hybrid combinations - which bridges gaps in the existing literature and demonstrates the superiority of hybrid approaches. Unlike prior research, which typically focuses on single representations, our approach combines aggregated session-level summaries with granular, sequential user actions to capture both long-term and short-term behavioral patterns. Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. Notably, the hybrid representation with LightGBM achieved superior predictive performance, significantly outperforming alternative methods. Feature importance analysis revealed key factors influencing purchase likelihood, such as time since the last event, session duration, and product interactions. This study provides actionable insights into real-time marketing interventions by demonstrating the practical utility of hybrid data representations and efficient tree-based models. Our findings offer a scalable and interpretable framework for e-commerce platforms to enhance purchase predictions and optimize marketing strategies.