Suppr超能文献

WSNs 中的快速邻居发现算法。

A Fast Neighbor Discovery Algorithm in WSNs.

机构信息

College of Computer Science, Sichuan University, Chengdu 610065, China.

School of Mathematics and Information Engineerging, Chongqing University of Education, Chongqing 400065, China.

出版信息

Sensors (Basel). 2018 Oct 3;18(10):3319. doi: 10.3390/s18103319.

Abstract

With the quick development of Internet of Things (IoT), one of its important supporting technologies, i.e., wireless sensor networks (WSNs), gets much more attention. Neighbor discovery is an indispensable procedure in WSNs. The existing deterministic neighbor discovery algorithms in WSNs ensure that successful discovery can be obtained within a given period of time, but the average discovery delay is long. It is difficult to meet the need for rapid discovery in mobile low duty cycle environments. In addition, with the rapid development of IoT, the node densities of many WSNs greatly increase. In such scenarios, existing neighbor discovery methods fail to satisfy the requirement in terms of discovery latency under the condition of the same energy consumption. This paper proposes a group-based fast neighbor discovery algorithm (GBFA) to address the issues. By carrying neighbor information in beacon packet, the node knows in advance some potential neighbors. It selects more energy efficient potential neighbors and proactively makes nodes wake up to verify whether these potential neighbors are true neighbors, thereby speeding up neighbor discovery, improving energy utilization efficiency and decreasing network communication load. The evaluation results indicate that, compared with other methods, GBFA decreases the average discovery latency up to 10 . 58 % at the same energy budget.

摘要

随着物联网(IoT)的快速发展,作为其重要支撑技术之一的无线传感器网络(WSN)得到了更多的关注。邻居发现是 WSN 中不可或缺的过程。WSN 中的现有确定性邻居发现算法确保在给定的时间内可以获得成功的发现,但平均发现延迟较长。在移动低占空比环境中,很难满足快速发现的需求。此外,随着物联网的快速发展,许多 WSN 的节点密度大大增加。在这种情况下,现有的邻居发现方法在相同能量消耗的条件下无法满足发现延迟的要求。本文提出了一种基于群组的快速邻居发现算法(GBFA)来解决这些问题。通过在信标分组中携带邻居信息,节点可以提前了解一些潜在的邻居。它选择更节能的潜在邻居,并主动让节点醒来验证这些潜在邻居是否为真正的邻居,从而加快邻居发现速度,提高能量利用率效率,并降低网络通信负载。评估结果表明,与其他方法相比,GBFA 在相同的能量预算下,将平均发现延迟降低了 10.58%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5889/6210503/4427653a9150/sensors-18-03319-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验