In this paper, we propose a cognitive capacity harvesting network (CCHN) based Internet of Things (IoT) architecture, which allows the lightweight IoT devices without spectrum monitoring/sensing capabilities to enjoy the benefits of cognitive radio networks (CRNs). We investigate the sum-rate maximization of IoT links in this proposed architecture. In particular, we formulate the considered problem as a three-dimensional (3-D) resource matching between the IoT links, the CR links and the available CR spectrum blocks (CSBs). Then, two approaches, i.e., Hungarian based switching iteration (HBSI) approach and minimum interference clustering based Lagrange relaxation (MICBLR) approach, are proposed to obtain the near-optimal solution. In HBSI approach, the IoT and CR links are divided into a set of IoT and CR links clusters (ICCs). Based on the partition of ICCs, the considered problem can be simplified to a maximum weight bipartite-matching problem and solved by the Hungarian algorithm. Switching iteration is then used to improve the partition of ICCs. To achieve a better tradeoff between the performance and running time, we further propose the MICBLR approach, which contains IoT links clustering according to the minimum interference rule and a Lagrange relaxation (LR) algorithm used to solve the 3-D matching problem between the clusters of IoT links, the CR links, and the available CSBs. Simulations show that the performance of the proposed approaches is close to the exhaustive search (ES) method but with a much shorter running time. Compared with the Nearest sharing based Hungarian (NSBH), Furthest sharing based Hungarian (FSBH), and Random allocation (RA) policies, the proposed approaches can averagely improve the system performance by 33.68%-38.18%.

中文翻译：

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支持认知能力收集网络的物联网三维资源匹配

在本文中，我们提出了一种基于认知容量收集网络 (CCHN) 的物联网 (IoT) 架构，它允许没有频谱监测/传感能力的轻量级物联网设备享受认知无线电网络 (CRN) 的好处。我们研究了这个提议的架构中物联网链路的总速率最大化。特别是，我们将所考虑的问题表述为物联网链路、CR 链路和可用 CR 频谱块 (CSB) 之间的三维 (3-D) 资源匹配。然后，提出了两种方法，即基于匈牙利的切换迭代（HBSI）方法和基于最小干扰聚类的拉格朗日松弛（MICBLR）方法，以获得接近最优的解决方案。在 HBSI 方法中，IoT 和 CR 链路分为一组 IoT 和 CR 链路集群 (ICC)。基于ICC的划分，可以将考虑的问题简化为最大权重二分匹配问题，并通过匈牙利算法求解。然后使用切换迭代来改进 ICC 的划分。为了在性能和运行时间之间取得更好的平衡，我们进一步提出了 MICBLR 方法，该方法包含根据最小干扰规则的 IoT 链路聚类和用于解决集群之间 3-D 匹配问题的拉格朗日松弛 (LR) 算法IoT 链路、CR 链路和可用 CSB 的数量。仿真表明，所提出方法的性能接近穷举搜索（ES）方法，但运行时间要短得多。与基于最近共享的匈牙利 (NSBH)、基于最远共享的匈牙利 (FSBH) 和随机分配 (RA) 策略相比，