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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.


An example of grid division, where 50 sensor nodes are deployed unevenly in the network region and several kinds of attributes are assumed sensed by these sensor nodes. The network region is divided into 25 square grid cells, which are the same in geographical size. The region of a query (for instance, q1) is rewritten into a set of grid cells. For instance, q1.qr can be rewritten into a set of grid cells of {gc0, gc1, gc2, gc5, gc6, gc7}.
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f2-sensors-15-15033: An example of grid division, where 50 sensor nodes are deployed unevenly in the network region and several kinds of attributes are assumed sensed by these sensor nodes. The network region is divided into 25 square grid cells, which are the same in geographical size. The region of a query (for instance, q1) is rewritten into a set of grid cells. For instance, q1.qr can be rewritten into a set of grid cells of {gc0, gc1, gc2, gc5, gc6, gc7}.

Mentions: As an example, Figure 1 shows part of our sample network region, as shown in Figure 2, where 13 sensor nodes are deployed in this sub-region. The lines of arrows reflect the fact that sensor nodes in neighboring grid cells are within their communication radius r. Consequently, the energy consumed for forwarding a packet with the size of k bits from a sensor node (e.g., 47) to a neighboring one (e.g., 49) is computed as Eij(k) = 2 × Eelec × k + εamp ×k × dn = 2×50×k + 0.1 × k × d2, where the parameter d represents the geographical distance between sensor Nodes 47 and 49.


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)

An example of grid division, where 50 sensor nodes are deployed unevenly in the network region and several kinds of attributes are assumed sensed by these sensor nodes. The network region is divided into 25 square grid cells, which are the same in geographical size. The region of a query (for instance, q1) is rewritten into a set of grid cells. For instance, q1.qr can be rewritten into a set of grid cells of {gc0, gc1, gc2, gc5, gc6, gc7}.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-15-15033: An example of grid division, where 50 sensor nodes are deployed unevenly in the network region and several kinds of attributes are assumed sensed by these sensor nodes. The network region is divided into 25 square grid cells, which are the same in geographical size. The region of a query (for instance, q1) is rewritten into a set of grid cells. For instance, q1.qr can be rewritten into a set of grid cells of {gc0, gc1, gc2, gc5, gc6, gc7}.
Mentions: As an example, Figure 1 shows part of our sample network region, as shown in Figure 2, where 13 sensor nodes are deployed in this sub-region. The lines of arrows reflect the fact that sensor nodes in neighboring grid cells are within their communication radius r. Consequently, the energy consumed for forwarding a packet with the size of k bits from a sensor node (e.g., 47) to a neighboring one (e.g., 49) is computed as Eij(k) = 2 × Eelec × k + εamp ×k × dn = 2×50×k + 0.1 × k × d2, where the parameter d represents the geographical distance between sensor Nodes 47 and 49.

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.