Browsing by Author "Bozkaya E."

Now showing 1 - 6 of 6
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    GA-based Energy Aware Path Planning Framework for Aerial Network Assistance
    (European Alliance for Innovation, 2021) Özçevik' Y.; Bozkaya E.; Akkoç M.; Erol M.R.; Canberk B.
    Aerial networks have enormous potential to assist terrestrial communications under heavy traffic requests for a predictable duration. However, such potential for improving both the performance and the coverage through the use of drones can face a major challenge in terms of power limitation. Hence, we consider the energy consumption characteristic of the components in such networks to provide energy aware flight path planning. For this purpose, a flight path planning scheme is proposed on an underlying topology graph that models the energy consumption of path traversals in the aerial network. In the proposed model, we offer to seek for the minimum energy consumption on a global problem domain during the entire operational time. Thus, we provide a concrete problem formulation and implement a flight path planning with Genetic Algorithms (GA) approach. Moreover, a novel end-system initiated handover procedure is illustrated to preserve connectivity of terrestrial users in the network architecture. In the end, the evaluation of the proposed model is conducted under three different scales of social event scenarios. A comparison with a dummy path planning scheme without energy awareness concerns is presented according to a set of parameters. The evaluation outcomes show that the proposed model is able to save 20% energy consumption, provides 15% less number of terrestrial replenishment, and 18% more average endurance for the topology. Besides, another energy aware path planning scheme in the literature offering a deployment with Bellman Ford algorithm is also included in the evaluation to evaluate the feasibility of the proposed framework for the enhanced problem domain. © 2021 OZCEVIK et al., licensed to EAI. All Rights Reserved.
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    Energy-aware mobility for aerial networks: A reinforcement learning approach
    (John Wiley and Sons Ltd, 2022) Bozkaya E.; Özçevik Y.; Akkoç M.; Erol M.R.; Canberk B.
    With recent advancements in aerial networks, aerial base stations (ABSs) have become a promising mobile network technology to enhance the coverage and capacity of the cellular networks. ABS deployment can assist cellular networks to support network infrastructure or minimize the disruptions caused by unexpected and temporary situations. However, with 3D ABS placement, the continuity of the service has increased the challenge of providing satisfactory Quality of Service (QoS). The limited battery capacity of ABSs and continuous movement of users result in frequent interruptions. Although aerial networks provide quick and effective coverage, ABS deployment is challenging due to the user mobility, increased interference, handover delay, and handover failure. In addition, once an ABS is deployed, an intelligent management must be applied. In this paper, we model user mobility pattern and formulate energy-aware ABS deployment problem with a goal of minimizing energy consumption and handover delay. To this end, the contributions of this paper are threefold: (i) analysis of reinforcement learning (RL)-based state action reward state action (SARSA) algorithm to deploy ABSs with an energy consumption model, (ii) predicting the user next-place with a hidden Markov model (HMM), and (iii) managing the dynamic movement of ABSs with a handover procedure. Our model is validated by comprehensive simulation, and the results indicate superiority of the proposed model on deploying multiple ABSs to provide the communication coverage. © 2021 John Wiley & Sons, Ltd.
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    Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network
    (Elsevier B.V., 2023) Bozkaya E.; Erel-Özçevik M.; Bilen T.; Özçevik Y.
    The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm. © 2023 Elsevier B.V.
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    Energy-Aware Task Scheduling for Digital Twin Edge Networks in 6G
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bozkaya E.; Bilen T.; Erel-Ozcevik M.; Ozcevik Y.
    With the recent surge in the Internet of Things (IoT) devices and applications, computation offloading services in Mobile Edge Computing (MEC) have provided the significant potential to upcoming 6G networks for a better Quality of Service (QoS). However, IoT devices are typically resource and energy-constrained, so this challenge can be compensated by incorporating energy-efficient approaches into the solution. Digital Twin is a candidate technology to reshape the future of the industry and energy-efficiently manage tremendous growth in data traffic at the network edge. Thus, we propose a Digital Twin Edge Network (DTEN) architecture for energy-aware task scheduling. More specifically, we formulate an energy optimization problem and identify a set of computation strategies to minimize both the task processing time and energy consumption. Due to being NP-hard, we compare it by Warehouse Location Problem (WLP) and solve it with the genetic algorithm-based approach in an energy and time-efficient manner. To achieve these, we present our digital twin-assisted energy-aware task scheduling algorithm by using both real-time and historical data in virtualization and service layers. After this, IoT devices can compute their tasks locally or offload to the edge/cloud server with the assistance of digital twins of the physical assets. Simulations are carried out to show the superiority of the proposed energy-aware task scheduling algorithm in terms of the task processing time and consumed energy in DTEN. © 2023 IEEE.
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    Optimal Location Assignment for Data-Driven Warehouse Towards Digital Supply Chain Twin
    (Institute of Electrical and Electronics Engineers Inc., 2023) Erel-Ozcevik M.; Ozcevik Y.; Bozkaya E.; Bilen T.
    Warehouses, as one of the critical components of supply chain management in Industry 4.0, play an important role in e-commerce operational efficiency. A crucial prerequisite for managing warehouses is to decide the locations of products (blocks) that can maximize overall space utilization, called a Block Location Problem (BLP). BLP basically determines the product locations to achieve maximum space utilization. One of the most innovative approaches to solving BLP is the use of drones as a block transportation strategy. Existing works have been mainly focused on 2D grid models while 3D flight movement is ignored. Thus, in this paper, we develop a novel data-driven warehouse model for digital supply chain twins. For this purpose, a warehouse digital twin (WDT) architecture is defined by creating a virtual replica of a warehouse that contains the features and interactions of its real-world counterpart. Then, we formalize the BLP in a 3D grid model to decide the location of blocks in a warehouse and to provide efficient space utilization by minimizing the energy consumption of drone cargo equipment. Finally, we propose a genetic algorithm-based solution to solve the storage location assignment. Performance evaluation results demonstrate that our proposed algorithm achieves more block utilization and less energy consumption when compared to the greedy solution. © 2023 IEEE.
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    Work-in-Progress: Merkle Tree-Based Secure Routing for Digital Twin-Assisted Aircraft Network in 6G Wireless
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bilen T.; Erel-Ozcevik M.; Bozkaya E.; Ozcevik Y.
    Providing Internet access above-the-clouds has made the development of aircraft networks more important than ever. However, new and emerging Internet applications have increased the challenge of providing seamless and real-time connectivity with traditional routing algorithms for aircraft networks in the upcoming 6G due to the highly-dynamic and unstable topology. Moreover, traditional routing mechanisms are prone to routing attacks, which can increase the packet transfer delay. To this end, in this paper, we present Merkle Tree-based secure routing mechanism with the assistance of digital twin. First, we construct a blockchain-based system with decentralized and distributed characteristics for the clustering of aircraft. Then, we propose a novel Merkle-Tree-based secure routing algorithm by combining real-time and historical data with digital twins. A Merkle tree is a data structure that contains gathered data from sender and receiver aircraft. Merkle root in an aircraft cluster forwards the gathered data to a satellite or a ground station in a secure manner. Accordingly, Secure Hash Algorithm (SHA)-256 is used for data integrity and a tree-based structure is built to reduce the number of transactions so that it is aimed to prevent malicious aircraft attacks. Finally, we show through a simulation environment, how blockchain processing time and the number of transactions can be reduced by meeting the strict Quality of Service (QoS) requirements for aircraft networks in 6G. Furthermore, we also use Proof of Stake (PoS) to reduce the computational time for mining a block as well as reducing the packet delivery ratio and packet transfer delay. © 2023 IEEE.