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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 the accumulated energy consumption for multi-attribute query in the whole region (MAQWR), where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. The gradient of the curves represents the ratio of energy consumption for query answering. This figure shows that when the cache size in the SN is relatively small and is not capable of caching all sensory data requested by a certain query, more sensory data should be replaced in the SN according to our data replacement mechanism, as presented in Section 4.2, and more energy is required for answering the query. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine).
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f4-sensors-15-15033: Comparison of the accumulated energy consumption for multi-attribute query in the whole region (MAQWR), where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. The gradient of the curves represents the ratio of energy consumption for query answering. This figure shows that when the cache size in the SN is relatively small and is not capable of caching all sensory data requested by a certain query, more sensory data should be replaced in the SN according to our data replacement mechanism, as presented in Section 4.2, and more energy is required for answering the query. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine).

Mentions: Experiments are conducted to evaluate the performance of these four kinds of queries leveraging our two-tier cooperative caching mechanism. Figure 4 compares the energy consumption of MAQWR, where the number of attributes varies as 1, 3, 5, 7 and 9, respectively. The cache size of the SN is set to 600, and the skewness degree is set to 60%. It is worth mentioning that Figure 4 shows the energy consumed in total from scratch, rather than that at a certain time point. The gradient of a curve corresponds to the energy consumed at a certain time point. The same principle holds for the energy consumption shown in Figures 5, 6–7. Intuitively, more energy is consumed when more attributes are of interest, since more sensor nodes are involved in sensory data gathering and aggregation. The energy consumption for the scenarios, where the number of attributes is 1, 3 or 5, is relatively small and stable. In contrast, the energy consumption for the scenarios, where the number of attributes is seven or nine, increases to a certain extent. Note that sensor nodes involved in the query answering should be 700 (or 900), when the number of interested attributes is seven (or nine). Since the cache size is 600, the sensory data of around 100 (or 300) sensor nodes cannot be cached in the SN. Hence, some sensory data have to be gathered from the network at each time slot, and the sensory data replacement mechanism is always enacted to cache sensory data with the highest popularity. These are the main causes for the increase of energy consumption. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine). Generally, the larger the cache size of the SN, the less the energy consumption in the network is. Caching in the SN should reduce the energy consumption to a certain extent, especially when the query region is relatively large and the number of interested attributes is relatively big.


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 the accumulated energy consumption for multi-attribute query in the whole region (MAQWR), where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. The gradient of the curves represents the ratio of energy consumption for query answering. This figure shows that when the cache size in the SN is relatively small and is not capable of caching all sensory data requested by a certain query, more sensory data should be replaced in the SN according to our data replacement mechanism, as presented in Section 4.2, and more energy is required for answering the query. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine).
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-15-15033: Comparison of the accumulated energy consumption for multi-attribute query in the whole region (MAQWR), where the number of attributes are set to 1, 3, 5, 7 and 9, respectively. The gradient of the curves represents the ratio of energy consumption for query answering. This figure shows that when the cache size in the SN is relatively small and is not capable of caching all sensory data requested by a certain query, more sensory data should be replaced in the SN according to our data replacement mechanism, as presented in Section 4.2, and more energy is required for answering the query. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine).
Mentions: Experiments are conducted to evaluate the performance of these four kinds of queries leveraging our two-tier cooperative caching mechanism. Figure 4 compares the energy consumption of MAQWR, where the number of attributes varies as 1, 3, 5, 7 and 9, respectively. The cache size of the SN is set to 600, and the skewness degree is set to 60%. It is worth mentioning that Figure 4 shows the energy consumed in total from scratch, rather than that at a certain time point. The gradient of a curve corresponds to the energy consumed at a certain time point. The same principle holds for the energy consumption shown in Figures 5, 6–7. Intuitively, more energy is consumed when more attributes are of interest, since more sensor nodes are involved in sensory data gathering and aggregation. The energy consumption for the scenarios, where the number of attributes is 1, 3 or 5, is relatively small and stable. In contrast, the energy consumption for the scenarios, where the number of attributes is seven or nine, increases to a certain extent. Note that sensor nodes involved in the query answering should be 700 (or 900), when the number of interested attributes is seven (or nine). Since the cache size is 600, the sensory data of around 100 (or 300) sensor nodes cannot be cached in the SN. Hence, some sensory data have to be gathered from the network at each time slot, and the sensory data replacement mechanism is always enacted to cache sensory data with the highest popularity. These are the main causes for the increase of energy consumption. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine). Generally, the larger the cache size of the SN, the less the energy consumption in the network is. Caching in the SN should reduce the energy consumption to a certain extent, especially when the query region is relatively large and the number of interested attributes is relatively big.

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.