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.

翻译：物联网（IoT）是一种允许各类对象交换多种信息的通信方案，可实现家庭自动化、生产管理、医疗保健等功能。此外，为降低管理需求并提升可维护性，物联网环境中引入了能量收集（EH）技术。近年来，考虑到行人、车辆和无人机等室外元素的场景日益增多，在设计物联网网络管理方案时，必须将移动性纳入考量。然而，计算最优中继拓扑被视为NP难问题，且为避免计算结果滞后，必须在信道状态变化前完成移动环境下的计算。本文旨在合理计算时间内，为静态与移动系统计算出次优中继拓扑。为实现此目标，我们验证了先前研究中提出的迭代平衡时隙分配算法，并设计了一种引导变异遗传算法（GMGA），该算法基于信道状态调整变异率以实现理性探索。此外，我们提出了一种移动感知迭代中继拓扑算法，该算法通过继承次优中继拓扑计算结果，在移动环境下进行中继拓扑计算。仿真结果表明，相较于传统方案，我们提出的方案能有效解决所构建的物联网网络问题，并在最低速率预算与计算时间方面，高效处理包含移动性的物联网环境。