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. Moreover, energy-harvesting (EH) technology is considered for IoT environment in order to reduce the need for management and enhance maintainability. However, 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. In order to handle this challenge, prior research has made an attempt to solve this problem via variational autoencoder (VAE) and backward-pass rate evaluation method. In this article, we propose a guided-mutation genetic algorithm (GMGA) to derive a sub-optimal relaying topology for IoT systems considering energy-harvesting. Furthermore, we propose a mobility-aware iterative relaying topology algorithm, which calculates the sub-optimal relaying topology of current time frame using the topology result of the previous one. Simulation results verify that our proposed scheme effectively solves formulated IoT network problems compared to other conventional schemes, and also effectively handles IoT environments in terms of mobility.

翻译：物联网（IoT）是一种通信方案，允许各种对象交换多种类型的信息，从而实现家庭自动化、生产管理、医疗保健等功能。此外，为降低管理需求并增强可维护性，物联网环境中考虑了能量采集（EH）技术。然而，由于近年来考虑行人、车辆和无人机等户外元素的环境日益增多，在设计物联网网络管理方案时，考虑移动性变得至关重要。为应对这一挑战，先前研究尝试通过变分自编码器（VAE）和反向传播速率评估方法解决该问题。本文提出一种引导变异遗传算法（GMGA），以推导适用于能量采集物联网系统的次优中继拓扑。此外，我们提出一种考虑移动性的迭代式中继拓扑算法，该算法利用前一时帧的拓扑结果计算当前时帧的次优中继拓扑。仿真结果验证了，与其他传统方案相比，所提方案能有效解决所构建的物联网网络问题，并在移动性方面有效适应物联网环境。