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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 MAQWR, where the cache size of the SN is set to 500, 600, 700, 800 and 900, respectively. Similar to Figure 8, when the cache size of the SN is relatively small and is not capable of caching all sensory data requested by a certain query, the cache hit rates for sensory data cached in the SN decrease significantly (roughly from 95% down to 70%).
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f9-sensors-15-15033: Comparison of cache hit rates for MAQWR, where the cache size of the SN is set to 500, 600, 700, 800 and 900, respectively. Similar to Figure 8, when the cache size of the SN is relatively small and is not capable of caching all sensory data requested by a certain query, the cache hit rates for sensory data cached in the SN decrease significantly (roughly from 95% down to 70%).

Mentions: Figures 5 and 9 show the energy consumption and cache hit rates for MAQWR, where: (i) the cache size of the SN is set to 500, 600, 700, 800 and 900, respectively; and (ii) the number of attributes interested in queries is seven. Sensory data of around 700 sensor nodes are able to be cached in the SN. When the cache size is more than 700, much less energy (roughly 40%∼66% of a decrease) is consumed, as shown in Figure 5, and cache hit rates are much higher (roughly 25% of an increase) as shown in Figure 9. As discussed, the sensory data replacement mechanism and real-time data gathering from the network are the main causes of more energy consumption and cache hit rates dropping.


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 MAQWR, where the cache size of the SN is set to 500, 600, 700, 800 and 900, respectively. Similar to Figure 8, when the cache size of the SN is relatively small and is not capable of caching all sensory data requested by a certain query, the cache hit rates for sensory data cached in the SN decrease significantly (roughly from 95% down to 70%).
© Copyright Policy
Related In: Results  -  Collection

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

f9-sensors-15-15033: Comparison of cache hit rates for MAQWR, where the cache size of the SN is set to 500, 600, 700, 800 and 900, respectively. Similar to Figure 8, when the cache size of the SN is relatively small and is not capable of caching all sensory data requested by a certain query, the cache hit rates for sensory data cached in the SN decrease significantly (roughly from 95% down to 70%).
Mentions: Figures 5 and 9 show the energy consumption and cache hit rates for MAQWR, where: (i) the cache size of the SN is set to 500, 600, 700, 800 and 900, respectively; and (ii) the number of attributes interested in queries is seven. Sensory data of around 700 sensor nodes are able to be cached in the SN. When the cache size is more than 700, much less energy (roughly 40%∼66% of a decrease) is consumed, as shown in Figure 5, and cache hit rates are much higher (roughly 25% of an increase) as shown in Figure 9. As discussed, the sensory data replacement mechanism and real-time data gathering from the network are the main causes of more energy consumption and cache hit rates dropping.

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.