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A Novel Two-Tier Cooperative Caching Mechanism for the Optimization of Multi-Attribute Periodic Queries in Wireless Sensor Networks.

Zhou Z, Zhao D, Shu L, Tsang KF - Sensors (Basel) (2015)

Bottom Line: Usually, certain sensory data may not vary significantly within a certain time duration for certain applications.Leveraging these cooperatively cached sensory data, queries are answered through composing these two-tier cached data.Experimental evaluation shows that this approach can reduce the network communication cost significantly and increase the network capability.

View Article: PubMed Central - PubMed

Affiliation: School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China. zhangbing.zhou@gmail.com.

ABSTRACT
Wireless sensor networks, serving as an important interface between physical environments and computational systems, have been used extensively for supporting domain applications, where multiple-attribute sensory data are queried from the network continuously and periodically. Usually, certain sensory data may not vary significantly within a certain time duration for certain applications. In this setting, sensory data gathered at a certain time slot can be used for answering concurrent queries and may be reused for answering the forthcoming queries when the variation of these data is within a certain threshold. To address this challenge, a popularity-based cooperative caching mechanism is proposed in this article, where the popularity of sensory data is calculated according to the queries issued in recent time slots. This popularity reflects the possibility that sensory data are interested in the forthcoming queries. Generally, sensory data with the highest popularity are cached at the sink node, while sensory data that may not be interested in the forthcoming queries are cached in the head nodes of divided grid cells. Leveraging these cooperatively cached sensory data, queries are answered through composing these two-tier cached data. Experimental evaluation shows that this approach can reduce the network communication cost significantly and increase the network capability.

No MeSH data available.


Comparison of cache hit rates for S1-sparse and S1-dense, where S1-sparse means sparse sub-regions with our cache mechanism, and S1-dense means dense sub-regions with our cache mechanism. Similar to Figure 6, this figure shows that our cooperative caching mechanism benefits the cache hit rates for cached sensory data of the SN (roughly from 30%–65%), especially when sensors nodes are densely deployed in the network.
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f10-sensors-15-15033: Comparison of cache hit rates for S1-sparse and S1-dense, where S1-sparse means sparse sub-regions with our cache mechanism, and S1-dense means dense sub-regions with our cache mechanism. Similar to Figure 6, this figure shows that our cooperative caching mechanism benefits the cache hit rates for cached sensory data of the SN (roughly from 30%–65%), especially when sensors nodes are densely deployed in the network.

Mentions: Figure 10 shows the cache hit rates for the scenarios S1-sparse and S1-dense. Generally, the cache hit rate of S1-sparse (roughly 30%) is lower than that of S1-dense (roughly 65%), although the energy consumption for S1-sparse and S1-dense is almost the same, as shown in Figure 7. Since grid cells are chosen randomly for representing a query region, common grid cells between continuous queries are relatively less in number than those of Figure 6, which induces a smaller value of the cache hit rates. Note that relatively few sensor nodes are involved in S1-sparse, and a minor change of the number of sensor nodes may have a relatively big impact on cache hit rates, which results in a relatively smaller value of cache hit rates for S1-sparse. Consequently, our cache mechanism is more efficient, especially when periodic and continuous queries have more overlapping sub-regions.


A Novel Two-Tier Cooperative Caching Mechanism for the Optimization of Multi-Attribute Periodic Queries in Wireless Sensor Networks.

Zhou Z, Zhao D, Shu L, Tsang KF - Sensors (Basel) (2015)

Comparison of cache hit rates for S1-sparse and S1-dense, where S1-sparse means sparse sub-regions with our cache mechanism, and S1-dense means dense sub-regions with our cache mechanism. Similar to Figure 6, this figure shows that our cooperative caching mechanism benefits the cache hit rates for cached sensory data of the SN (roughly from 30%–65%), especially when sensors nodes are densely deployed in the network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541820&req=5

f10-sensors-15-15033: Comparison of cache hit rates for S1-sparse and S1-dense, where S1-sparse means sparse sub-regions with our cache mechanism, and S1-dense means dense sub-regions with our cache mechanism. Similar to Figure 6, this figure shows that our cooperative caching mechanism benefits the cache hit rates for cached sensory data of the SN (roughly from 30%–65%), especially when sensors nodes are densely deployed in the network.
Mentions: Figure 10 shows the cache hit rates for the scenarios S1-sparse and S1-dense. Generally, the cache hit rate of S1-sparse (roughly 30%) is lower than that of S1-dense (roughly 65%), although the energy consumption for S1-sparse and S1-dense is almost the same, as shown in Figure 7. Since grid cells are chosen randomly for representing a query region, common grid cells between continuous queries are relatively less in number than those of Figure 6, which induces a smaller value of the cache hit rates. Note that relatively few sensor nodes are involved in S1-sparse, and a minor change of the number of sensor nodes may have a relatively big impact on cache hit rates, which results in a relatively smaller value of cache hit rates for S1-sparse. Consequently, our cache mechanism is more efficient, especially when periodic and continuous queries have more overlapping sub-regions.

Bottom Line: Usually, certain sensory data may not vary significantly within a certain time duration for certain applications.Leveraging these cooperatively cached sensory data, queries are answered through composing these two-tier cached data.Experimental evaluation shows that this approach can reduce the network communication cost significantly and increase the network capability.

View Article: PubMed Central - PubMed

Affiliation: School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China. zhangbing.zhou@gmail.com.

ABSTRACT
Wireless sensor networks, serving as an important interface between physical environments and computational systems, have been used extensively for supporting domain applications, where multiple-attribute sensory data are queried from the network continuously and periodically. Usually, certain sensory data may not vary significantly within a certain time duration for certain applications. In this setting, sensory data gathered at a certain time slot can be used for answering concurrent queries and may be reused for answering the forthcoming queries when the variation of these data is within a certain threshold. To address this challenge, a popularity-based cooperative caching mechanism is proposed in this article, where the popularity of sensory data is calculated according to the queries issued in recent time slots. This popularity reflects the possibility that sensory data are interested in the forthcoming queries. Generally, sensory data with the highest popularity are cached at the sink node, while sensory data that may not be interested in the forthcoming queries are cached in the head nodes of divided grid cells. Leveraging these cooperatively cached sensory data, queries are answered through composing these two-tier cached data. Experimental evaluation shows that this approach can reduce the network communication cost significantly and increase the network capability.

No MeSH data available.