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A search strategy of Level-Based Flooding for the Internet of Things.

Qiu T, Ding Y, Xia F, Ma H - Sensors (Basel) (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Software, Dalian University of Technology, Dalian 116620, China. qiutie@dlut.edu.cn

ABSTRACT
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.

No MeSH data available.


Energy consumption in each scenario against threshold P.
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f20-sensors-12-10163: Energy consumption in each scenario against threshold P.

Mentions: We conduct a series of simulations. The simulation results are shown in Figures 18–20. The performance of basic flooding can be found where P = 1. Each point in these figures represents our result obtained from a simulation run containing 1000 broadcast requests. Figure 18 shows the reach ability with different P in every scenario. In Scenario 5, the reach ability grows with the increasing P. When P is small, the reach ability is only about 10%. In other four scenarios, the reach ability in fact reaches about the same level as that of the basic flooding when P is small. In high average degree network, P's value doesn't affect the reach ability seriously, but in a low average degree network, P's value has great effect on the reach ability because if the network's average degree is low, the nodes have few neighbors and it is too sparse to let the packet diffuse in the network.


A search strategy of Level-Based Flooding for the Internet of Things.

Qiu T, Ding Y, Xia F, Ma H - Sensors (Basel) (2012)

Energy consumption in each scenario against threshold P.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472822&req=5

f20-sensors-12-10163: Energy consumption in each scenario against threshold P.
Mentions: We conduct a series of simulations. The simulation results are shown in Figures 18–20. The performance of basic flooding can be found where P = 1. Each point in these figures represents our result obtained from a simulation run containing 1000 broadcast requests. Figure 18 shows the reach ability with different P in every scenario. In Scenario 5, the reach ability grows with the increasing P. When P is small, the reach ability is only about 10%. In other four scenarios, the reach ability in fact reaches about the same level as that of the basic flooding when P is small. In high average degree network, P's value doesn't affect the reach ability seriously, but in a low average degree network, P's value has great effect on the reach ability because if the network's average degree is low, the nodes have few neighbors and it is too sparse to let the packet diffuse in the network.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Software, Dalian University of Technology, Dalian 116620, China. qiutie@dlut.edu.cn

ABSTRACT
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.

No MeSH data available.