Browsing by Author "Bozkaya-Aras E."

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    Markov Chain-based Data Privacy Prioritizing for Task Scheduling in Digital Twin Edge Networks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bozkaya-Aras E.; Erel-Özçevik M.
    Edge computing plays a significant role in shaping the future of Internet of Things (IoT) networks by processing high volumes of data at the network edge and meeting the desired latency demand. Although edge computing is regarded as a promising technology for these features as well as its decentralization and supportive functionalities, there is a need to develop systems to protect data security and privacy since users can face the risk of data leakage when they offload data to the edge/cloud server. In order to solve this problem, in this paper, we propose a three-layer digital twin edge network architecture for task scheduling based on edge-cloud computing. The digital twin has been used to map the IoT network to the virtual space, monitor physical assets in real-time, and perform comprehensive network analysis. On this basis, a task scheduling model that prioritizes data privacy has been introduced to prevent users’ privacy-sensitive information from being collected by attackers. The proposed model can both take full advantage of edge and cloud computing and protect data privacy. We classify computation tasks based on data privacy sensitivity and model them using the M/M/1 Markov model in local, edge server, and cloud computations. Then, we propose a data privacy prioritizing task scheduling algorithm combining digital twin and edge-cloud collaboration. This genetic optimization based algorithm significantly increases data privacy by 40% compared to the greedy approach, while keeping the time under an acceptable level. In this way, each task is distributed between the local, edge server, and cloud server. Thanks to the intelligence behavior and repetitive growth of the proposed algorithm, the optimal task scheduling is performed in a digital twin system. Our analysis reveals that data leakage attacks can be significantly decreased with the proposed task scheduling algorithm by minimizing task processing time. © 2024 IEEE.
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    Metaverse-Based Order Picking Optimization for Supply Chain of Things
    (Institute of Electrical and Electronics Engineers Inc., 2024) Erel-Ozcevik M.; Bozkaya-Aras E.; Bilen T.
    In recent years, with the rise of e-commerce and digital services, customers' expectations have affected supply chain management operations. The logistics industry reacts proactively by integrating new technologies into its systems to meet changing customer expectations. Among these technologies, a relatively new concept called Supply Chain of Things (SCoT) is an enabling technology for supply chain operations to increase productivity and automation. However, a large number of customer orders and increasing concerns about operating costs challenge efficient management and quality of service in SCoT. In this regard, this paper addresses the order-picking problem of minimizing delivery time and operating costs, including travel costs and total costs to the employer. Metaverse can be considered a feasible solution to address this problem due to its advantages of seamless and intelligent interaction between the physical world and the digital world. In this paper, we present a metaverse-based system architecture and design the network digital twin to enable intelligent real-time management of the SCoT environment. We formulate the order-picking problem and propose a genetic algorithm-based solution to satisfy customer demands on time with minimal operating costs by creating a digital twin of the supply chain system in the Metaverse. The simulation results show that the proposed solution achieves significant gains compared with baseline strategies in terms of the total cost to the employer while keeping order delivery time under the acceptable customer level. © 2024 IEEE.