Cloudsim distributed processing of probabilistic top-k queries in wireless sensor networks

Jan 12, 20140 likes152 views

This document summarizes a paper on distributed processing of probabilistic top-k queries in wireless sensor networks. It introduces the concepts of sufficient and necessary sets, which can facilitate localized data pruning in clusters. It then describes three algorithms - sufficient set-based, necessary set-based, and boundary-based - for intercluster query processing with bounded communication rounds. Finally, it presents an adaptive algorithm that dynamically switches between the three algorithms to minimize transmission cost, and experimental results showing the algorithms significantly reduce data transmission with small constant communication rounds, with the adaptive algorithm performing near-optimally under various conditions.

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DISTRIBUTED PROCESSING OF PROBABILISTIC TOP-K QUERIES IN
WIRELESS SENSOR NETWORKS

ABSTRACT:

In this paper, we introduce the notion of sufficient set and necessary set for distributed
processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two
concepts have very nice properties that can facilitate localized data pruning in clusters.

We develop a suite of algorithms, namely, sufficient set-based (SSB), necessary set-based
(NSB), and boundary-based (BB), for intercluster query processing with bounded rounds of
communications in responding to dynamic changes of data distribution in the network.

We develop an adaptive algorithm that dynamically switches among the three proposed
algorithms to minimize the transmission cost. We show the applicability of sufficient set and
necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured
network topologies.

Experimental results show that the proposed algorithms reduce data transmissions significantly
and incur only small constant rounds of data communications. The experimental results also
demonstrate the superiority of the adaptive algorithm, which achieves a near-optimal
performance under various conditions.

This paper introduces the concepts of sufficient and necessary sets for distributed processing of probabilistic top-k queries in wireless sensor networks. It develops three algorithms - sufficient set-based, necessary set-based, and boundary-based - for intercluster query processing with bounded communication rounds to respond to dynamic data changes. It also develops an adaptive algorithm that switches between the three algorithms to minimize transmission costs. Experimental results show the proposed algorithms significantly reduce data transmissions with only small constant communication rounds, and the adaptive algorithm achieves near-optimal performance under various conditions.

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This paper introduces the concepts of sufficient and necessary sets for distributed processing of probabilistic top-k queries in wireless sensor networks. It develops three algorithms - sufficient set-based, necessary set-based, and boundary-based - to facilitate localized data pruning with bounded intercluster communication rounds in response to dynamic data changes. It also develops an adaptive algorithm that switches between the three algorithms to minimize transmission costs. Experimental results show that the proposed algorithms significantly reduce data transmissions while incurring only small constant communication rounds, and the adaptive algorithm achieves near-optimal performance under various conditions.

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This paper introduces the concepts of sufficient and necessary sets for distributed processing of probabilistic top-k queries in wireless sensor networks. It develops three algorithms - sufficient set-based, necessary set-based, and boundary-based - to facilitate localized data pruning with bounded intercluster communication rounds in response to dynamic data changes. It also develops an adaptive algorithm that switches between the three algorithms to minimize transmission costs. Experimental results show that the proposed algorithms significantly reduce data transmissions while incurring only small constant communication rounds, and the adaptive algorithm achieves near-optimal performance under various conditions.

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Distributed Processing of Probabilistic Top-k Queries in Wireless Sensor Networks introduces the concepts of sufficient and necessary sets to facilitate localized data pruning in sensor network clusters. It develops three algorithms - sufficient set-based, necessary set-based, and boundary-based - for intercluster query processing with bounded communication rounds. An adaptive algorithm is also introduced to minimize transmission costs under dynamic data distributions. Experimental results show the proposed algorithms significantly reduce data transmission with only small constant communication rounds.

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