An efficient mechanism for processing similarity search queries in sensor networks

Chung, Yu‐Chi; Su, I‐Fang; Lee, Chiang

doi:10.1016/j.ins.2010.08.031

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…In order to process similarity search queries efficiently, Chung, et al [6] propose a novel framework over a data-centric storage structure, referred to as the Similarity Search Algorithm (SSA), based on the concept of a Hilbert Curve. The lack of global knowledge about the entire sensor database is identified as one of the major challenges in processing a sensor network similarity search query.…”

Section: Related Workmentioning

confidence: 99%

Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

Ahmed

Gregory

2015

Sensors

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The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called iDistance, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries.

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Section: Related Workmentioning

confidence: 99%

Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

Ahmed

Gregory

2015

Sensors

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“…The Similarity Search Algorithm (SSA) [22] was proposed by Chung, et al based on the Hilbert Curve over a DCS structure. SSA is successful in searching similar data without collecting data from all of the network sensors.…”

Section: Similarity Search Algorithmmentioning

confidence: 99%

Techniques and Challenges of Data Centric Storage Scheme in Wireless Sensor Network

Ahmed

Gregory

2012

JSAN

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Storing, collecting and querying data across miniaturized battery powered Wireless Sensor Networks (WSN) is a key research focus today. Distributed Data-Centric Storage (DCS), an alternate to External Storage (ES) and Local Storage (LS), is thought to be a promising and efficient storage and search mechanism. There has been a growing interest in understanding and optimizing WSN DCS schemes in recent years, where the range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven this line of research. In this paper, an extensive state-of-the-art study is provided including the prime WSN DCS schemes, challenges that inspired these schemes, as well as drawbacks and shortcomings of existing solutions. In contrast to previous surveys that briefly discuss the contribution of a few WSN DCS mechanisms, we provide a thematic taxonomy in which schemes are classified according to the problems dealt with including range query, similarity search, data aggregation, sensor network field non-uniformity, multi-replication, load balancing and routing algorithm

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“…Liang et al studied the top-K query in wireless sensor networks and proposed a solution which addressed the query response time and its effect on the network lifetime [10]. Chung et al proposed a similarity search algorithm based on the concept of Hilbert curve over a data-centric storage structure for sensor networks [5].…”

Section: Related Workmentioning

confidence: 99%

“…Such results confirm our analysis in Section 4.2. As the figure shows, an appropriate value of f is in interval [5,10]. In the rest results of KNN queries, we use 10 as the default f value.…”

Section: Experiments Environmentmentioning

confidence: 99%