ProcessingWindow Queries in Wireless Sensor Networks

Xu, Yingqi; Lee, Wang-Chien; Xu, Jianliang; Mitchell, G.

doi:10.1109/icde.2006.119

Cited by 33 publications

(8 citation statements)

References 23 publications

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“…Xu et al [32] investigated a framework of spatial window query in dynamic sensor networks. To further simplify the query process, Xu et al [33] proposed an infrastructure-free window query technique in sensor networks. Ku et al [34] presented an approach to reduce the access latency of the spatial queries, e.g., window queries, by utilizing the neighbor information in wireless broadcast environments.…”

Section: Related Workmentioning

confidence: 99%

Above-Threshold Queries of Environmental Conditions Based on Bilinear Interpolation in Wireless Sensor Networks

Liu

Huangfu

Zhang

et al. 2018

Sensors

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Wireless sensor networks can be regarded as sensor database systems, which permit users to query sensor data of interest. Among various spatial database queries, we focus the area-wise aggregate queries in the region where the sensor values are above a predefined threshold, which are summarized as above-threshold queries. In this paper, we propose a novel Bilinear Interpolation-Based (BIB) algorithm, which utilizes the bilinear interpolation to estimate the environmental variables inside a grid with the known sensor values at the vertexes, to support the above-threshold queries for regularly-deployed sensor networks and provide the closed-form solution of the above-threshold ratio. We designate experiments with both the artificially-constructed environment data and the real temperature data. Experiment results manifest that the proposed BIB algorithm shows a good performance in estimating the above-threshold ratios to support the above-threshold queries in an accurate and efficient manner.

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Section: Related Workmentioning

confidence: 99%

Above-Threshold Queries of Environmental Conditions Based on Bilinear Interpolation in Wireless Sensor Networks

Liu

Huangfu

Zhang

et al. 2018

Sensors

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“…higher SNR represents better channel quality and lesser energy consumption while higher distance represents more hops and hence more energy consumption. Hence we propose a metric namely the KNN-METRIC to compute a single valued output from distance and SNR inputs given by (4).…”

Section: ) 3d-k Nearest Neighbor: K Neighbor Selection Phasementioning

confidence: 99%

“…These approaches have been proven energy inefficient [2]. To overcome the inefficiency, in-network sensor data aggregation and processing [3,4] approaches have been proposed. These approaches still use the centralized sink but perform in-network aggregation and processing before delivering the data.…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient Data Collection Approach Using K-Nearest Neighbors in a 3D Sensor Network

Jayaraman

Zaslavsky

Delsing

2010

2010 Eleventh International Conference on Mobile Data Management

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Sensor networks represent an important component of distributed infrastructure supplying raw data to various applications from military to healthcare. A key challenge is costefficient collection of distributed data streaming from those sensor networks. In this paper we propose the use of mobile data collectors that employ K-NN queries as a cost-efficient approach to collect data within the sensor network. We investigate a 3D sensor network and propose a cost-efficient 3D-KNN algorithm that uses minimal energy and communication overheads to compute k-nearest neighbors. The 3D-KNN algorithm uses a 3 dimensional plane rotation algorithm that maps sensor nodes on a 3D plane to a reference plane identified by the mobile data collector. We propose a cost-efficient KNN boundary estimation algorithm that computes KNN boundary based on network density. We also propose a neighbor prediction algorithm that uses distance, signal to noise ratio and mobile data collector's trajectory information to identify sensor nodes along the mobile data collector's path. We simulate the proposed 3D-KNN algorithm using GlomoSim and validate its cost efficiency by evaluating its energy efficiency and query latency. Lessons and results of extensive simulation conclude the paper.

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“…After the route has been calculated, the data would then be processed using the aggregation function by the mid-node which returns the results to the query nodes in order to reduce energy consumption. According to the infrastructure size, these kinds of methods can be further divided into the network spanning infrastructure (NSI) [1] and window spanning infrastructure (WSI) [5]. The NSI approach is to construct and maintain a network system (as shown in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Perkins etc. [5] developed a method that operates under the condition of movable sensors. This method allows the window query to operate without any information of the network topology as well as query and perform the aggregation operation within the network.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Approaches for Processing Window Queries in Dynamic Wireless Sensor Networks

Liu

Hsu

Huang

2011

AMM

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Due to the proliferation of low cost wireless sensors, there is growing research interest in their applications, for example, in home healthcare and location tracking. However, due to sensors’ energy resource constraint, some possible applications of sensors have been restricted. In particular, in applications concerning deployment of mobile sensors in dynamic environments, high amounts of energy are consumed by sensors to maintain routing tables. Although existing methods have been proposed to query data from sensors without the use of any routing tables, these methods typically require redundant data to be sent back to the sink and not all of the aggregation functions could be executed precisely. In this paper, we modify an existing method to provide more accurate query answers and extend the lifetime of a wireless sensor network (WSN). According to our simulation, this method outperforms the existing method our approach modifies.

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ProcessingWindow Queries in Wireless Sensor Networks

Abstract: Abstract

Cited by 33 publications

References 23 publications

Above-Threshold Queries of Environmental Conditions Based on Bilinear Interpolation in Wireless Sensor Networks

Above-Threshold Queries of Environmental Conditions Based on Bilinear Interpolation in Wireless Sensor Networks

Cost-Efficient Data Collection Approach Using K-Nearest Neighbors in a 3D Sensor Network

Enhanced Approaches for Processing Window Queries in Dynamic Wireless Sensor Networks

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