Guided-Mutation Genetic Algorithm for Mobile IoT Network Relay
<|reference_start|>Guided-Mutation Genetic Algorithm for Mobile IoT Network Relay: The Internet of Things (IoT) is a communication scheme which allows various objects to exchange several types of information, enabling functions such as home automation, production management, healthcare, etc. In addition, energy-harvesting (EH) technology is considered for IoT environment in order to reduce the need for management and enhance maintainability. Moreover, since environments considering outdoor elements such as pedestrians, vehicles and drones have been on the rise recently, it is important to consider mobility when designing an IoT network management scheme. However, calculating the optimal relaying topology is considered as an NP-hard problem, and finishing computation for mobility environment before the channel status changes is important to prevent delayed calculation results. In this article, our objective is to calculate a sub-optimal relaying topology for stationary and mobile system within reasonable computation time. To achieve our objective, we validate an iterative balancing time slot allocation algorithm introduced in the previous study, and propose a guided-mutation genetic algorithm (GMGA) that modulates the mutation rate based on the channel status for rational exploration. Additionally, we propose a mobility-aware iterative relaying topology algorithm, which calculates relaying topology in a mobility environment using an inheritance of the sub-optimal relaying topology calculations. Simulation results verify that our proposed scheme effectively solves formulated IoT network problems compared to other conventional schemes, and also effectively handles IoT environments including mobility in terms of minimum rate budget and computation time.<|reference_end|>
@article{kam2024guided-mutation,
    title={Guided-Mutation Genetic Algorithm for Mobile IoT Network Relay},
    author={Gyupil Kam and Kiseop Chung},
    journal={arXiv preprint arXiv:2404.01683},
    year={2024},
    doi={10.1109/ACCESS.2024.3434665},
    archivePrefix={arXiv},
    eprint={2404.01683},
    primaryClass={cs.NI}
}