DataReconstructionwithSpatialandTemporalCorrelationinWirelessSensorNetworks资源-CSDN文库

研究论文

181 浏览量 2021-02-22 01:28:07 上传评论收藏 365KB PDF 举报

资源推荐

资源详情

资源评论

Data Reconstruction with Spatial and Temporal

Correlation in Wireless Sensor Networks

Yayun Zhang

College of Mathematics and

Computer Sciences

Fuzhou University,China

n140327044@fzu.edu.cn

Hongju Cheng

College of Mathematics and

Computer Sciences

Fuzhou University,China

cscheng@fzu.edu.cn

Dongyin Chen

College of Mathematics and

Computer Sciences

Fuzhou University,China

n140327035@fzu.edu.cn

ABSTRACT

Data loss is a common phenomenon in the practical wireless

sensor networks due to several observations, such as the node or

link failure, the expensive cost of re-transmission, and so on. How

to reconstruct the lost data is one important issue for many

applications which concern heavily on the data gathering issues.

Most of the current data reconstruction solutions are based on the

spatial correlation and they adopt interpolation methods to

approximate the data reconstruction process, which leads to bad

performance especially in case that the number of data loss

increases in the network. In this paper, we consider the spatial and

temporal correlation simultaneously, and propose a novel data

reconstruction algorithm to improve the data error ratio in the

wireless sensor networks. The proposed algorithm can utilize the

spatial correlation by using the curved face reconstruction, and it

is also helpful to improve the accuracy of data reconstruction by

exploring the temporal correlation among the sensory data.

Extensive experiments demonstrate that the proposed data

reconstruction algorithm can significantly reduce the fitting data

error ratio compared with related works especially in case that

data loss is serious in the network.

CCS Concepts

• General and reference➝Estimation • Information systems➝

Sensor networks.

Keywords

Wireless sensor networks; spatial correlation; temporal correlation;

data reconstruction.

1. INTRODUCTION

A wireless sensor network generally consists of a large number of

nodes which are equipped with the sensing devices, micro-

processor, limited memory and wireless communications [1].

They can collect information from the local environment and send

the sensory data to the application center, i.e., the sink. However,

data loss might occur when data is gathered to the sink due to the

following observations. Firstly, the trend for cheaper sensor nodes

might result in increased probability of node failure. In this case,

the sensor node collect cannot obtain all the original data

especially when it works in a periodical manner. Secondly, the

congestion in the wireless communications, such as the exposed

terminal problem and hidden terminal problem, shows that the

data transmission is unreliable, which means that the sink will not

receive all the sensory data sent by nodes in the network. Thirdly,

data re-transmission is a little expensive in the wireless sensor

networks because the nodes are generally budged with un-

rechargeable battery. For example, the data loss ratio is about 23%

in the practical collected data set for Intel Indoor project [2]. How

to reconstruct the lost data is one important issue in the wireless

sensor networks.

Similar to the definition in [3], we synthesize several types of data

loss patterns by analyzing the data set in Intel Indoor project [2],

i.e., element random loss, block random loss and element

sequence loss. The element random loss describes the case that

data is lost randomly during any given collection period. In this

case, the probability of data loss for different nodes is independent.

Figure 1 demonstrates an example, in which the hollow circles

denote nodes whose data is lost (meaning that the sink does not

receive the data due to node/link failure), and the black solid

circles denote nodes whose data is correctly sent to the sink. The

block random loss is used to illustrate the case that a number of

nodes in the same block have lost all their data simultaneously at a

random time. As shown in Figure 2, nodes numbered from 1 to 6

are in the same block, and it is called block random loss if they

lose the data simultaneously (represented by hollow circles to

show the data loss). The element sequence loss shows that the

collected data for one single node is lost during the continuous

time duration. The sequential data loss might mean node failure in

the network when it is detected.

Most of the applications in the wireless sensor networks depend

on data analysis by gathering information from nodes in the

networks to the sink [4, 5]. The presented data loss may influence

the efficiency or feasibility of data analysis in these applications.

Some works aim at providing the approximate query in which

data accuracy is not the key issue and thus the application/sink

only needs to know the approximate data in the network. However,

data loss is not considered and solved in these works, which

means approximate query will encounter with the problems if too

much data is lost.

Most of the current data reconstruction algorithms, such as the

traditional KNN [6], the inverse-distance weighting [7] and the

Delaunay Triangulation [8], depend on the spatial data correlation

in the sensor networks. However, the physical phenomenon

observed in the network is both spatial and temporal correlated

generally. The different data observed by adjacent nodes is similar

and it has the same variation tendency. At the same time, the

value of sequential sensing data for one node is generally

continuous when the collection duration is small enough. Some

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that

copies bear this notice and the full citation on the first page. To copy

otherwise, or republish, to post on servers or to redistribute to lists,

requires prior specific permission and/or a fee. Request permissions

from Permissions@acm.org.

MSCC'16, July 05 2016, Paderborn, Germany

DOI: http://dx.doi.org/10.1145/2940353.2940360

本内容试读结束，登录后可阅读更多

下载后可阅读完整内容，剩余5页未读，立即下载

评论收藏

内容反馈

weixin_38515573

粉丝: 8
资源: 939

Data Reconstruction with Spatial and Temporal Correlation in Wir...

最新资源

Data Reconstruction with Spatial and Temporal Correlation in Wir...

Correlation-Model-Based Data Aggregation in Wireless Sensor Networks

Resource-Efficient Data Gathering in Sensor Networks for Environment Reconstruction

Accurate and Robust Time Reconstruction for Deployed Sensor Networks(摘要论文)

Dictionary Learning and Time Sparsity for Dynamic MR Data Reconstruction

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-Time

Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos

3D Human Sensing, Action and Emotion Recognition in Robot Assisted

处理成arff格式的Localization Data for Posture Reconstruction数据集

reconstruction

Wireless Sensor Networks（无线传感器网络）

Joint 3D Face Reconstruction and Dense.pdf

Surface reconstruction with preserving volume data

advance in wireless sensor and sensor network

Transparent and Specular Object Reconstruction(2000)

Compressive sensing reconstruction with prior information

3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare

Programming Interview Problems and Algorithms in Ruby

Node localization algorithm for wireless sensor networks using compressive sensing theory

Wireless sensor networks a survey

wireless sensor network material

matlab-Data reconstruction and analysis tools for tomography

Remote sensing image analysis including the spatial domain

Document summarization based on data reconstruction

Medical Image Reconstruction A Conceptual Tutorial --pdf

最新资源