Browsing by Author "Erol M.R."

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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.