Search Results For Skylines UPDATED
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Search results for skylines
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Assume that Table 1 is the set of data objects perceived by sensor nodes in a WSN. Figure 1 shows an example, where the query results of a continuous skyline query over Table 1 is depicted in transition from time 15 to 18. We can see from Table 1 that each data object contains two attributes and has an arrival time and an expiration time associated with it. In Figure 1, those data objects connected by line segments are the query results of a continuous skyline query over Table 1. As shown in Figure 1, the query results of a continuous skyline query change dynamically over time.
Then, we study the effect of cardinality on total communication cost (TCC). Figure 5 illustrates that TCC as a function of the cardinality of dataset over independent dataset and anti-correlated dataset when other simulation parameters are set to their default values. The results show that TCC of each method increases accordingly with the cardinality of dataset. This is because the increase in the cardinality of dataset leads to the corresponding increase in the amount of data objects processed and transmitted by sensor nodes. We can see from Figure 5 that EECS is obviously better than the other three methods in terms of TCC. The reason is EECS is optimized for filtering strategy and skyline incremental maintenance.
This work is supported by the National Nature Science Foundation of China (61702368, 61170174), Major Research Project of National Nature Science Foundation of China (91646117) and Natural Science Foundation of Tianjin (17JCYBJC15200, 18JCQNJC0070).
The ever increasing requirements of data sensing applications result in the usage of IoT networks. These networks are often used for efficient data transfer. Wireless sensors are incorporated in the IoT networks to reduce the deployment and maintenance costs. Designing an energy efficient data aggregation method for sensor equipped IoT to process skyline query, is one of the most critical problems. In this paper, we propose two approximation algorithms to process the skyline query in wireless sensor networks. These two algorithms are uniform sampling based approximate skyline query and Bernoulli sampling-based approximate skyline query. Solid theoretical proofs are provided to confirm that the proposed algorithms can yield the required query results. Experiments conducted on actual datasets show that the two proposed algorithms have high performance in terms of energy consumption compared to the simple distributed algorithm. 041b061a72