TY - GEN
T1 - Distributed operator placement and data caching in large-scale sensor networks
AU - Ying, Lei
AU - Liu, Zhen
AU - Towsley, Don
AU - Xia, Cathy H.
PY - 2008
Y1 - 2008
N2 - Recent advances in computer technology and wireless communications have enabled the emergence of stream-based sensor networks. In such sensor networks, real-time data are generated by a large number of distributed sources. Queries are made that may require sophisticated processing and filtering of the data. A query is represented by a query graph. In order to reduce the data transmission and to better utilize resources, it is desirable to place operators of the query graph inside the network, and thus to perform in-network processing. Moreover, given that various queries occur with different frequencies and that only a subset of sensor data may actually be queried, caching intermediate data objects inside the network can help improve query efficiency. In this paper, we consider the problem of placing both operators and intermediate data objects inside the network for a set of queries so as to minimize the total cost of storage, computation, and data transmission. We propose distributed algorithms that achieve optimal solutions for tree-structured query graph topologies and general network topologies. The algorithms converge in Lmax(H Q + 1) iterations, where Lmax is the order of the diameter of the sensor network, and HQ represents the depth of the query graph, defined as the maximum number of operations needed for a raw data to become a final data. For a regular grid network and complete binary tree query graph, the complexity is O(√N log2 M), where N is the number of nodes in the sensor network and M is the number of data objects in a query graph. The most attractive features of these algorithms are that they require only information exchanges between neighbors, can be executed asynchronously, are adaptive to cost change and topology change, and are resilient to node or link failures.
AB - Recent advances in computer technology and wireless communications have enabled the emergence of stream-based sensor networks. In such sensor networks, real-time data are generated by a large number of distributed sources. Queries are made that may require sophisticated processing and filtering of the data. A query is represented by a query graph. In order to reduce the data transmission and to better utilize resources, it is desirable to place operators of the query graph inside the network, and thus to perform in-network processing. Moreover, given that various queries occur with different frequencies and that only a subset of sensor data may actually be queried, caching intermediate data objects inside the network can help improve query efficiency. In this paper, we consider the problem of placing both operators and intermediate data objects inside the network for a set of queries so as to minimize the total cost of storage, computation, and data transmission. We propose distributed algorithms that achieve optimal solutions for tree-structured query graph topologies and general network topologies. The algorithms converge in Lmax(H Q + 1) iterations, where Lmax is the order of the diameter of the sensor network, and HQ represents the depth of the query graph, defined as the maximum number of operations needed for a raw data to become a final data. For a regular grid network and complete binary tree query graph, the complexity is O(√N log2 M), where N is the number of nodes in the sensor network and M is the number of data objects in a query graph. The most attractive features of these algorithms are that they require only information exchanges between neighbors, can be executed asynchronously, are adaptive to cost change and topology change, and are resilient to node or link failures.
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U2 - 10.1109/INFOCOM.2007.151
DO - 10.1109/INFOCOM.2007.151
M3 - Conference contribution
AN - SCOPUS:51349108839
SN - 9781424420261
T3 - Proceedings - IEEE INFOCOM
SP - 1651
EP - 1659
BT - INFOCOM 2008
T2 - INFOCOM 2008: 27th IEEE Communications Society Conference on Computer Communications
Y2 - 13 April 2008 through 18 April 2008
ER -