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An Intelligent Computing Method for Contact Plan Design in the Multi-Layer Spatial Node-Based Internet of Things

机译:基于多层空间节点的物联网中联络计划设计的智能计算方法

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摘要

Computational Intelligence (CI) has been addressed as a great challenge in recent years, particularly the aspects of routing, task scheduling, and other high-complexity issues. Especially for the Contact Plan Design (CPD) that schedules contacts in dynamic and resource-constrained networks, a suitable CI algorithm can be exchanged for a high-quality Contact Plan (CP) with the appropriate computational overhead. Previous works on CPD mainly focused on the optimization of satellite network connectivity, but most of them ignored network topology characteristics. In this paper, we study the CPD issue in the spatial node based Internet of Things (IoT), which enables the spatial nodes to deliver data cooperatively via intelligent networking. Specifically, we first introduce a Multi-Layer Space Communication Network (MLSCN) model consisting of satellites, High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground stations, on which a Time-Evolving Graph (TEG) is used to illustrate the CPD process. Then, according to the characteristics of each layer in the MLSCN, we design the corresponding CPs for the inter-layer contacts and intra-layer contacts. After that, a CI algorithm named as Multidirectional Particle Swarm Optimization (MPSO) is proposed for inter-layer CPD, which utilizes a grid-based initialization strategy to expand the diversity of individuals, in which a quaternary search method and quaternary optimization are introduced to improve efficiency of particle swarms in iterations and to ensure the continuous search ability, respectively. Furthermore, an optimized scheme is implemented for the intra-layer CPD to reduce congestion and improve transmission efficiency. Simulation results show that the proposed CPD scheme can realize massive data transmission with high efficiency in the multi-layer spatial node-based IoT.
机译:近年来,计算智能(CI)已被视为一项巨大的挑战,特别是在路由,任务调度和其他高复杂性方面。特别是对于计划在动态和资源受限的网络中的联系人进行安排的联系计划设计(CPD),可以将适当的CI算法交换为具有适当计算开销的高质量联系计划(CP)。以前关于CPD的工作主要集中在优化卫星网络连接性上,但是大多数都忽略了网络拓扑特征。在本文中,我们研究了基于空间节点的物联网(IoT)中的CPD问题,该问题使空间节点可以通过智能网络协作地交付数据。具体来说,我们首先介绍由卫星,高空平台(HAP),无人飞行器(UAV)和地面站组成的多层空间通信网络(MLSCN)模型,在该模型上使用了时变图(TEG)说明CPD流程。然后,根据MLSCN中每一层的特性,我们为层间触点和层内触点设计相应的CP。在此基础上,提出了一种用于层间CPD的CI算法,称为多方向粒子群优化(MPSO),它利用基于网格的初始化策略来扩展个体的多样性,其中引入了四元搜索方法和四元优化。分别提高粒子群的迭代效率和确保连续搜索能力。此外,针对层内CPD实施优化方案以减少拥塞并提高传输效率。仿真结果表明,所提出的CPD方案可以在基于多层空间节点的物联网中实现高效的海量数据传输。

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