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个人信息Personal Information
教授
硕士生导师
性别:男
毕业院校:大连工学院
所在单位:软件学院、国际信息与软件学院
电子邮箱:mhl@dlut.edu.cn
A Search Strategy of Level-Based Flooding for the Internet of Things
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论文类型:期刊论文
发表时间:2012-08-01
发表刊物:SENSORS
收录刊物:SCIE、PubMed、Scopus
卷号:12
期号:8
页面范围:10163-10195
ISSN号:1424-8220
关键字:Internet of Things; query processing; flooding; search; energy efficiency
摘要:This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales.