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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.


Average hops of search to find target nodes.
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f26-sensors-12-10163: Average hops of search to find target nodes.

Mentions: Figure 26 shows the average hops of search to find target nodes. We can observe that the average hops of search are nearly the same as the average level shown in Table 2. The average hops for LBF is less compared with that of basic flooding. If the number of data packets is larger, the energy consumption of transmitting data is much less than that of basic flooding. This can extend lifetime of network a lot. With the scale of network improved, the performance becomes better.


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

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

Average hops of search to find target nodes.
© Copyright Policy
Related In: Results  -  Collection

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

f26-sensors-12-10163: Average hops of search to find target nodes.
Mentions: Figure 26 shows the average hops of search to find target nodes. We can observe that the average hops of search are nearly the same as the average level shown in Table 2. The average hops for LBF is less compared with that of basic flooding. If the number of data packets is larger, the energy consumption of transmitting data is much less than that of basic flooding. This can extend lifetime of network a lot. With the scale of network improved, the performance becomes better.

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.