Abstract
As uncertainty is the inherent character of sensing data, the processing and optimization techniques for Probabilistic Skyline (PS) in wireless sensor networks (WSNs) are investigated. It can be proved that PS is not decomposable after analyzing its properties, so in-network aggregation techniques cannot be used directly to improve the performance. In this paper, an efficient algorithm, called Distributed Processing of Probabilistic Skyline (DPPS) query in WSNs, is proposed. The algorithm divides the sensing data into candidate data (CD), irrelevant data (ID), and relevant data (RD). The ID in each sensor node can be filtered directly to reduce data transmissions cost, since, only according to both CD and RD, PS result can be correctly obtained on the base station. Experimental results show that the proposed algorithm can effectively reduce data transmissions by filtering the unnecessary data and greatly prolong the lifetime of WSNs.
Highlights
It is found that wireless sensor networks (WSNs) have a more and more important impact on the ways to collect and use information from the physical world
For data classification in a candidate data set, our algorithm works as follows: first, it initializes the cumulative probability variable (Line 10); second, the value of n is calculated, where n is the number of τt that can dominate the tuple t (Line 11); third, it finds out all τt that dominate t (Line 12), after which each τt’s dominant probability is calculated (Lines 13–15)
We explored deeply the requirements of Probabilistic Skyline (PS) query algorithm in WSNs and summarized the existing problems in the WSNs
Summary
It is found that wireless sensor networks (WSNs) have a more and more important impact on the ways to collect and use information from the physical world. Many queries in WSNs that rarely need transmitting every piece of sensing data in the local sensor nodes have been well studied to reduce the communication cost and to speed up the computation [9,10,11,12,13,14,15,16], for instance, sliding window skylines in sensor network [11, 12], continuous skyline monitoring in WSNs [10], probabilistic query of uncertain data streams [18, 19], dynamic (or relative) skylines [25], and distributed uncertain skyline query [26] Most of these researches are studied under a centralized system setting. (ii) An efficient algorithm, called Distributed Processing of Probabilistic Skyline (DPPS) query in WSNs, is proposed, which reduces the in-network amount of data transmission by filtering the irrelevant data on the sensor nodes.
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