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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 energy consumption of our PCC and MQP [33] for the query types of MAQWR and SAQWR, where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. This figure shows that more energy is consumed for our PCC than MQP at the first time point (denoted INIT), while much less energy is consumed afterwards.
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f12-sensors-15-15033: Comparison of energy consumption of our PCC and MQP [33] for the query types of MAQWR and SAQWR, where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. This figure shows that more energy is consumed for our PCC than MQP at the first time point (denoted INIT), while much less energy is consumed afterwards.

Mentions: Figure 12 illustrates the energy consumption for our PCC and MQP [33] at different time points. As previously mentioned, the energy consumption for MQP is the same for all time points. Figure 12 shows that more energy is consumed at the first time point (denoted INIT). The energy consumption for our PCC is much less than that of MQP in the consequent time points. It is evident from this figure that our PCC is more energy efficient than MQP, especially when the query attributes are relatively large in number.


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 energy consumption of our PCC and MQP [33] for the query types of MAQWR and SAQWR, where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. This figure shows that more energy is consumed for our PCC than MQP at the first time point (denoted INIT), while much less energy is consumed afterwards.
© Copyright Policy
Related In: Results  -  Collection

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

f12-sensors-15-15033: Comparison of energy consumption of our PCC and MQP [33] for the query types of MAQWR and SAQWR, where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. This figure shows that more energy is consumed for our PCC than MQP at the first time point (denoted INIT), while much less energy is consumed afterwards.
Mentions: Figure 12 illustrates the energy consumption for our PCC and MQP [33] at different time points. As previously mentioned, the energy consumption for MQP is the same for all time points. Figure 12 shows that more energy is consumed at the first time point (denoted INIT). The energy consumption for our PCC is much less than that of MQP in the consequent time points. It is evident from this figure that our PCC is more energy efficient than MQP, especially when the query attributes are relatively large in number.

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.