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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.


Saved rebroadcast in each scenario against threshold P.
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f19-sensors-12-10163: Saved rebroadcast in each scenario against threshold P.

Mentions: The saved rebroadcast in different scenarios with different P is shown in Figure 19. The amount of rebroadcast saving decreases proportionally to P, as P id increasing. When P < 0.5, the effect is obvious, and the scheme can save about 50%, but when P > 0.5, the scheme has nearly no effect. Various levels of saved rebroadcasts can be obtained over the scheme, depending on the average degree of nodes in the area. For instance, a scheme in lower degree (in Scenario 3) can offer about 50% savings at P = 0.2; while in a higher degree (in Scenario 1) it only offers about 30%.


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

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

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

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

f19-sensors-12-10163: Saved rebroadcast in each scenario against threshold P.
Mentions: The saved rebroadcast in different scenarios with different P is shown in Figure 19. The amount of rebroadcast saving decreases proportionally to P, as P id increasing. When P < 0.5, the effect is obvious, and the scheme can save about 50%, but when P > 0.5, the scheme has nearly no effect. Various levels of saved rebroadcasts can be obtained over the scheme, depending on the average degree of nodes in the area. For instance, a scheme in lower degree (in Scenario 3) can offer about 50% savings at P = 0.2; while in a higher degree (in Scenario 1) it only offers about 30%.

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.