Skyline Query Processing in Sensor Network Based on Data Centric Storage

Abstract

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges

“…A technique of efficient skyline recomputation after data updates in a big data environment is developed in [ 10 ]. The WSNs were also one of the major target environments of research, because the skyline query and its variants are very useful in monitoring applications [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

“…Song et al [26] propose a skyline query processing algorithm exploiting the key features of DCS. The algorithm processes a query in three stages on the basis of four assumptions: (1) choice of DCS framework is limited to KDDCS [27], GDCS [28] and DIM [29]; (2) all nodes are identified by unique ID; (3) each node knows the geographic location of its own and its neighbor nodes; and (4) each node knows the range of data stored in its neighbor nodes.…”