Real-time query processing optimisation for wireless sensor networks

Diallo, Ousmane; Rodrigues, Joel J. P. C.; Sène, Mbaye; Xia, Feng

doi:10.1504/ijsnet.2015.069863

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…In order to improve query efficiency, a number of researchers have begun to design distributed air index to optimize spatial query processing [5,[25][26][27][28][29][30][31][32][33][34][35]. In [5], Li presents an efficient method to partition the record-keeping information about the underlying road sensor network and its associated objects, by which a fully distributed air index is developed, called integrated exponential index, based on an extended version of the Hilbert curve.…”

Section: Distributed Index For Spatial Query Processing In Road Networkmentioning

confidence: 99%

DIM: a distributed air index based on MapReduce for spatial query processing in road networks

Jin

Chen

Tung

et al. 2018

J Wireless Com Network

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In this era of mobile application and socialization, location-based services (LBSs) have become unprecedentedly popular and emphasized. By submitting its location to the service provider, the mobile terminal can obtain useful service. As an important technology that can provide location-based services, spatial query processing has become a research hotspot. According to the problems existing in current road network partitioning, first of all, this paper proposes the partition density formula and the optimal road network partitioning method according to the partition density. Based on that, we propose the concepts of partition self-attraction, mutual attraction, and merger factor of partition to effectively merge the partitions with low densities, which can further reduce the number of unnecessary broadcast frames and optimize the index method. Then, we propose the real distributed air index method DIM and kNN spatial query processing algorithm based on the MapReduce platform. According to the DIM index method, the Name Node only stores the prime index, while not storing the data, and each frame consists of defined index and the data in a partition that need to be broadcasted. The mobile user can quickly localize the frame data that they need to obtain according to the main index or the index of current frame, in this way to reduce the tuning time and access latency, which can significantly optimize the query efficiency. Massive experiments have proved the stability and effectiveness of the proposed algorithm.

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Section: Distributed Index For Spatial Query Processing In Road Networkmentioning

confidence: 99%

DIM: a distributed air index based on MapReduce for spatial query processing in road networks

Jin

Chen

Tung

et al. 2018

J Wireless Com Network

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“…Moreover, several works on similarity queries in sensor network are also proposed [25][26][27][28][29][30][31][32]. In this paper, we focus on the spatial search in road sensor networks on the air by proposing a fully distributed air index (called IEI) to process different spatial queries, including kNN query, range query, and CkNN query.…”

Section: Data Broadcast Algorithms For Spatial Search In Road Networkmentioning

confidence: 99%

A novel distributed air index for efficient spatial query processing in road sensor networks on the air

Shu

Zhu

et al. 2016

Int J Communication

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Recent development of wireless technology and smart mobile devices has spurred intense research efforts to address spatial queries. In particular, an increasing interest for tackling spatial query processing in broadcast environments has been observed. To the best of our knowledge, most of the existing work on this problem has assumed an Euclidean space. However, for real applications, query clients move within a road sensor network, where the distance between a data object and a query is determined by the connectivity of the road sensor network. This paper explores the problem of spatial query processing in road sensor networks by means of wireless data broadcast. We present an efficient method to partition the recordkeeping information about the underlying road sensor network and its associated objects, by which we develop a fully distributed air index, called integrated exponential index, based on an extended version of the Hilbert curve. We also propose efficient client-side algorithms to facilitate the processing of several kinds of spatial queries, including kNN query, CkNN query, and range query. Finally, extensive simulation experiments have been conducted to demonstrate the strengths of our proposed techniques. access for relatively heavily loaded systems. A year and a half ago, soccer fans from around the world arrived in Brasilia to watch the 2014 FIFA World Cup. After a game, many people would like to locate good nearby restaurants. Because there could be tons of people doing the search at the same time, an ideal solution in this case would be to provide spatial query processing using data broadcast.Earlier approaches to designing broadcast-based spatial query processing methods [2-7] had assumed that MCs and searched objects were located in an Euclidean space. In real-life situations, however, MCs normally move along a road sensor network, and the Euclidean distance may not properly reflect real road sensor network distance [8]. Thus, the existing spatial query processing methods proposed for Euclidean space are inapplicable to process spatial queries in road sensor networks. Recently, researchers have begun to zero in on the problem. Li et al. [9] addressed the issue of processing CNN queries in road networks on the air and proposed an network Voronoi diagrams (NVDs)-based distributed air index to support query processing. One key limitation of the proposed method is that it processes CkNN queries with k D 1. Sun et al. [10] presented an air index called network partition index (NPI) to support spatial query processing in road networks on the air and proposed efficient algorithms to process different spatial queries such as kNN query, range query, and CNN query. NPI is, however, not a fully distributed structure, and thus, each query client will have to wait for the arrival of the index part followed by the whole index before starting its query processing, which could lead to long access latency and tuning time. In this paper, we aim to overcome these limitations and propose a fully distributed air ...

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