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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 popularity-based cooperative caching (PCC) and multiple-attribute query processing (MQP) [33] for the query types of MAQWR and SAQWR, where the number of attributes is set to 1, 3, 5, 7 and 9, respectively. The energy consumption at various time points (TP2, TP4, etc.) are illustrated. Generally, the energy consumption of our PCC is much smaller than that of MQP (roughly 30% less on average for PCC than MQP), especially when the number of query attributes is relatively large. Note that the energy consumption of PCC is quite large at the first time point, since no sensory data have been cached in the SN for reducing the data gathering from the network in real-time to facilitate the query processing.
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f11-sensors-15-15033: Comparison of energy consumption of our popularity-based cooperative caching (PCC) and multiple-attribute query processing (MQP) [33] for the query types of MAQWR and SAQWR, where the number of attributes is set to 1, 3, 5, 7 and 9, respectively. The energy consumption at various time points (TP2, TP4, etc.) are illustrated. Generally, the energy consumption of our PCC is much smaller than that of MQP (roughly 30% less on average for PCC than MQP), especially when the number of query attributes is relatively large. Note that the energy consumption of PCC is quite large at the first time point, since no sensory data have been cached in the SN for reducing the data gathering from the network in real-time to facilitate the query processing.

Mentions: Experiments have been conducted for the comparison of the energy consumption for PCC and MQP [33] with respect to the query types of MAQWR and SAQWR. The number of attributes are set to 1, 3, 5, 7 and 9, respectively. The cache size of the SN is set to 900, and the skewness degree is set to 60%. Figure 11 shows the experimental results, where the energy consumption at certain time points (e.g., TP2, TP4, etc.) is illustrated. Note that the symbol TPi (e.g., i = 2) means the i-th (second) time point. It is worth mentioning that the energy consumption for MQP is almost the same for all time points, whose values are illustrated at the left side of Figure 11. As for our PCC, the energy consumption is quite large at the first time point (denoted INITin Figure 11), since no sensory data have been cached in the SN for reducing the data gathering from the network in real time, and all intermediate nodes (INs) are required to gather sensory data from the corresponding sensor nodes and to cache them locally. The energy consumption at the succeeding time points decreases to a large extent due to the reusability of sensory data cached in the SN for supporting the forthcoming query answering. This figure shows that the energy to be consumed will be in a steady state after around 18 time slots when the number of attributes is one and around 40 time slots when the number of attributes is nine, due to the fact that sensory data cached in the SN and INs can hardly reduce the energy consumed for query processing any further. Generally, our PCC outperforms MQP on energy consumption, 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 popularity-based cooperative caching (PCC) and multiple-attribute query processing (MQP) [33] for the query types of MAQWR and SAQWR, where the number of attributes is set to 1, 3, 5, 7 and 9, respectively. The energy consumption at various time points (TP2, TP4, etc.) are illustrated. Generally, the energy consumption of our PCC is much smaller than that of MQP (roughly 30% less on average for PCC than MQP), especially when the number of query attributes is relatively large. Note that the energy consumption of PCC is quite large at the first time point, since no sensory data have been cached in the SN for reducing the data gathering from the network in real-time to facilitate the query processing.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-15-15033: Comparison of energy consumption of our popularity-based cooperative caching (PCC) and multiple-attribute query processing (MQP) [33] for the query types of MAQWR and SAQWR, where the number of attributes is set to 1, 3, 5, 7 and 9, respectively. The energy consumption at various time points (TP2, TP4, etc.) are illustrated. Generally, the energy consumption of our PCC is much smaller than that of MQP (roughly 30% less on average for PCC than MQP), especially when the number of query attributes is relatively large. Note that the energy consumption of PCC is quite large at the first time point, since no sensory data have been cached in the SN for reducing the data gathering from the network in real-time to facilitate the query processing.
Mentions: Experiments have been conducted for the comparison of the energy consumption for PCC and MQP [33] with respect to the query types of MAQWR and SAQWR. The number of attributes are set to 1, 3, 5, 7 and 9, respectively. The cache size of the SN is set to 900, and the skewness degree is set to 60%. Figure 11 shows the experimental results, where the energy consumption at certain time points (e.g., TP2, TP4, etc.) is illustrated. Note that the symbol TPi (e.g., i = 2) means the i-th (second) time point. It is worth mentioning that the energy consumption for MQP is almost the same for all time points, whose values are illustrated at the left side of Figure 11. As for our PCC, the energy consumption is quite large at the first time point (denoted INITin Figure 11), since no sensory data have been cached in the SN for reducing the data gathering from the network in real time, and all intermediate nodes (INs) are required to gather sensory data from the corresponding sensor nodes and to cache them locally. The energy consumption at the succeeding time points decreases to a large extent due to the reusability of sensory data cached in the SN for supporting the forthcoming query answering. This figure shows that the energy to be consumed will be in a steady state after around 18 time slots when the number of attributes is one and around 40 time slots when the number of attributes is nine, due to the fact that sensory data cached in the SN and INs can hardly reduce the energy consumed for query processing any further. Generally, our PCC outperforms MQP on energy consumption, 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.