A greedy model with small world for improving the robustness of heterogeneous Internet of Things
A greedy model with small world for improving the robustness of heterogeneous Internet of Things
复制标题
提高异构物联网鲁棒性的小世界贪心模型
DOI：
10.1016/j.comnet.2015.12.019
复制
发表时间：
2016-06-04
影响因子：
5.6
通讯作者：
Tolba, Amr
中科院分区：
文献类型：
--
作者：
Qiu, Tie;Luo, Diansong;Tolba, Amr
在包含多种类型异构网络的物联网中，鲁棒性是一个重要而具有挑战性的问题。提高拓扑结构的鲁棒性，即承受一定数量的节点故障，对于能量有限的轻量级网络具有重要意义。同时，高性能的拓扑结构也是必不可少的。小世界模型已被证明是一种可行的网络拓扑优化方法。在本文中，我们提出了一种具有小世界属性的贪婪模型（GMSW）用于物联网中的异构传感器网络。首先给出了GMSW中用于区分不同网络节点重要性的两个贪心准则，并在此基础上定义了节点局部重要性的概念。然后，我们提出了一种算法，该算法通过在某些节点之间根据其局部重要性添加捷径来转换网络，使其具有小世界属性。我们的性能评估表明，只需添加少量的快捷方式，GMSW就可以快速地使网络表现出小世界特性。我们还将GMSW与最新的相关研究——汇聚节点定向角模型（DASM）进行了比较，结果表明GMSW在小世界特性和网络延迟方面优于DASM。(C) 2016 Elsevier B.V.版权所有
Robustness is an important and challenging issue in the Internet of Things (IoT), which contains multiple types of heterogeneous networks. Improving the robustness of topological structure, i.e., withstanding a certain amount of node failures, is of great significance especially for the energy-limited lightweight networks. Meanwhile, a high-performance topology is also necessary. The small world model has been proven to be a feasible way to optimize the network topology. In this paper, we propose a Greedy Model with Small World properties (GMSW) for heterogeneous sensor networks in IoT. We first present the two greedy criteria used in GMSW to distinguish the importance of different network nodes, based on which we define the concept of local importance of nodes. Then, we present our algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance. Our performance evaluations demonstrate that, by only adding a small number of shortcuts, GMSW can quickly enable a network to exhibit the small world properties. We also compare GMSW with a latest related work, the Directed Angulation toward the Sink Node Model (DASM), showing that GMSW outperforms DASM in terms of small world characteristics and network latency. (C) 2016 Elsevier B.V. All rights reserved.