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doi:10.1093/comjnl/bxu054

Resource-Efﬁcient Data Gathering

in Sensor Networks for Environment

Reconstruction

Linghe Kong

, Xiao-Yang Liu

, Meixia Tao

, Min-You Wu

Yu G u

, Long Cheng

and Jianwei Niu

3,∗

Shanghai Jiao Tong University, Shanghai, China

Singapore University of Technology and Design, Singapore

State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science

and Engineering, Beihang University, Beijing, China

∗

Corresponding author: niujianwei@buaa.edu.cn

Environmentreconstructionistorebuildthephysicalenvironmentinthecyberspaceusingthesensory

data collected by sensor networks, which is a fundamental method for human to understand the

physical world in depth.A lot of basic scientiﬁc work such as nature discovery and organic evolution

heavily relies on the environment reconstruction. However, gathering large amount of environmental

data costs huge energy and storage space. The shortage of energy and storage resources has become

a major problem in sensor networks for environment reconstruction applications. Motivated by

exploiting the inherent feature of environmental data, in this paper, we design a novel data gathering

protocolbased on compressive sensing theory and time series analysis to further improvethe resource

efﬁciency. This protocol adapts the duty cycle and sensing probability of every sensor node according

to the dynamic environment, which cannot only guarantee the reconstruction accuracy, but also

save energy and storage resources. We implement the proposed protocol on a 51-node testbed and

conduct the simulations based on three real datasets from Intel Indoor, GreenOrbs and Ocean

Sense projects. Both the experiment and simulation performances demonstrate that our method

signiﬁcantly outperforms the conventional methods in terms of resource efﬁciency and reconstruction

accuracy.

Keywords: wireless sensor networks; compressive sensing; low-duty-cycle; data gathering; environment

reconstruction; time series; energy-efﬁcient; data deluge

Received 2 January 2014; revised 6 April 2014

Handling editor: Zhangbing Zhou

1. INTRODUCTION

Environment reconstruction [1] is to rebuild the physical

environment in the cyberspace using the sensory data collected

bywirelesssensornetworks(WSNs)[2],whichisafundamental

method for human to understand the physical world in depth.

A lot of basic scientiﬁc work such as nature discovery

and organic evolution heavily relies on the environment

reconstruction. Several realWSN platforms are deployed under

the water [3], in the forest [4] and on the volcano [5, 6] for

environment reconstruction applications.

Motivation: It is desired that a WSN can sense and gather the

environmental data [7] (such as temperature, light or humidity)

with a long-term for accurate environment reconstruction.

Nevertheless, this goal is difﬁcult to achieve due to the shortage

of resources in WSNs. On one hand, a WSN is constrained

by its energy resource. The tiny size of a sensor node and

its limited batteries cannot support a long-term data gathering

because of the high-energy consumption incurred by wireless

communication. For example, a typical TelosB sensor node

with CC2420 wireless module consumes ∼20 mA for 1-s

transmission [8]. But the total power of a TelosB node is no

more than 4000 mA capacity [9], which is usually supplied by

twoAA batteries. On the other hand, a WSN is also constrained

by its storage resource. High reconstruction accuracy demands

Section B: Computer and Communications Networks and Systems

The Computer Journal

, 2014

The Computer Journal Advance Access published June 30, 2014

at Beihang University on December 23, 2014http://comjnl.oxfordjournals.org/Downloaded from

2 L. Kong et al.

ﬁne-grained sensing, which results in large amount of sensory

data and consequently costs huge storage space. To conﬁrm this

empirical result, we did an indoor experiment that 51TelosB

nodes gathered over 1 GB data within just 7 days when the

sensing interval is set to be 5s. Furthermore, Baraniuk [10]

advocates in SCIENCE that the bottleneck of WSN now is data

deluge: the amount of data generated worldwide (1250 billion

GB in 2010), which is dominated by sensory data, is growing

by 58% per year. For these reasons, energy constraint and data

deluge become the key challenges in WSNs, especially for the

environment reconstruction application that requires long-term

and large-amount data gathering.

Existing approaches and limitation: Existing approaches

study either the energy efﬁciency or the data aggregation

problems. (1) Energy constraint is a fundamental problem in

WSNs. Hundreds of works have contributed on optimizing

the energy consumption from the physical layer up to the

application layer [2, 11–14]. In particular, a widely employed

approach is to schedule each sensor node’s duty cycle (also

called wake/sleep cycle) [15–17], which dramatically reduces

the energy cost via turning off the radio. Nevertheless, most

existing approaches pre-determine the schedule of the duty

cycle, which cannot guarantee the reconstruction accuracy for

dynamic environment. (2) Data deluge is currently another

critical problem in WSNs. Diverse data aggregation approaches

have been well investigated for energy balance [18], connected

dominating set [19] and energy-latency tradeoff [20]. However,

thereisno existingworkaddressingthe storage-efﬁcientmethod

with the guarantee of accurate environment reconstruction.

Since both energy and storage efﬁciency could beneﬁt from

reducing the amount of data gathering, they are not incom-

patible, but correlated mutually in environment reconstruction

application. To tackle the joint problem of energy constraint

and the data deluge, in this paper, we i nvestigate the resource-

efﬁcient data gathering (REDG) problem taking energy efﬁ-

ciency and storage efﬁciency into account together, which is

equal to maximize the sleep duration and to minimize the

amount of sensory data gathering, respectively.

Our approach: First, we analyze the inherent features of

environmental data. After analyzing the real datasets from Intel

Indoorexperiment[21], GreenOrbs project [4]andOcean Sense

project [22], we observe that the environmental data exhibit

obvious low-rank feature. This observation implies that the data

existhigh redundancy, so that sensing only a few data are able to

rebuildthe near-optimal environment accordingto the advanced

compressive sensing (CS) theory [23–28]. Furthermore, we

verify that the rank of the environmental data is predictable.

Inspired by the observed features, we propose the novel

REDG protocol. REDG periodically computes the optimal

parameters, including the duty cycle and the amount of sensory

data based on the dynamic environment and then adaptively

tunes the data gathering behavior. To compute the optimal

parameters, one straightforward approach is to gather all the

environmental data and derive them in a posteriori manner.

However, a posteriori manner might not be feasible because

the optimal parameters need to be pre-determined to schedule

the data gathering. To tackle this challenge, our design takes

advantage of the rank prediction. More speciﬁcally, given a

requirement of the reconstruction accuracy, by exploiting the

predicted rank, we derive the maximum sleep duration using

time series analysis [29] based on the predictable rank feature.

Meanwhile, we derive the least amount of sensory data using

CS theory [30] based on the low-rank feature. Therefore, REDG

can effectively adapt to the dynamic environment and achieve

REDG.

Finally, REDG is implemented on a testbed and simulated

on three real datasets. We evaluate the proposed REDG,

the standard collection tree protocol (CTP) [31] and the

classic low-duty-cycle protocol [15] on a 51-node real testbed

for performance comparison. A 30-day experiment shows

that REDG signiﬁcantly outperforms the other protocols on

energy and storage efﬁciencies. To understand the performance

of REDG in large-scale sensor networks, we then conduct

extensive simulations based on real datasets from Intel Indoor,

GreenOrbs and Ocean Sense projects. The evaluation results

also demonstrate that our REDG achieves low resource

consumption with the guarantee of reconstruction accuracy.

Contributions: In summary, the major contributions of this

paper are as follows:

(i) To the best of our knowledge, this is the ﬁrst work to

study both the energy and storage efﬁciencyproblems in

data gathering of WSN for environment reconstruction.

(ii) We mine three real datasets to explore the low-rank and

predictable rank features of dynamic environments.

(iii) A novel REDG protocol is designed based on CS theory

and time series analysis. This protocol can adapt duty

cycle and the amount of data gathering according to the

dynamic environment. And the near-optimal accuracy

of environment reconstruction is guaranteed.

(iv) The proposed protocol is implemented on a real

testbed and simulated based on three real datasets. The

results verify its feasibility and show its signiﬁcant

improvement compared with the existing methods.

Paper organization: The remainder of this paper is organized

as follows. In Section 2, the background is presented. In

Section 3, the problem is formulated. Environment features are

mined in Section 4. In Section 5, the novel REDG protocol

is proposed. In Section 6, implementation and simulation are

performed for evaluating REDG. In Section 7, we conclude this

work.

2. BACKGROUND

In this section, we present the concept of environment

reconstruction, existing resource-efﬁcient methods and the

background of CS theory.

Section B: Computer and Communications Networks and Systems

The Computer Journal

, 2014

at Beihang University on December 23, 2014http://comjnl.oxfordjournals.org/Downloaded from

REDG in Sensor Networks for Environment Reconstruction 3

2.1. Environment reconstruction

The objective of environment reconstruction [1] is to rebuild

the accurate environment in cyberspace based on the gathered

sensory data, which is commonly realized by WSNs. Several

real applications can be found in [3, 4, 6]. However, such

applications are restricted by the energy and storage resources.

The energy constraint is a classic problem in WSNs.

According to [2], battery capacity only doubled in the past 35

years. Moreover, the hazardous sensing environment precludes

manual battery replacement. Energy constraint is unlikely to be

solved in the near future on account of the size limitation of

sensor nodes.

In recent years, the data deluge issue becomes a serious

bottleneck of WSNs. A recent report [10] found that the total

amount of world data storage is growing 31% slower than

the amount of data generated worldwide (dominated by sensor

networks). This expanding gap indicates that storage efﬁciency

will be a critical issue in WSNs.

2.2. Existing resource-efﬁcient methods

This work is related to energy-efﬁcient and storage-efﬁcient

methods. However, we ﬁnd that existing approaches cannot

satisfy the joint problem of energy constraint and data deluge

for environment reconstruction.

In WSNs, the energy-efﬁcient methods are investigated from

physical layer [12], link layer [11, 14], network layer [13]to

application layer [2]. Particularly, scheduling the duty cycle of

every sensor node is the widely employed approach for energy-

saving data gathering [15–17, 32]. Although these solutions

are highly diverse, none of them considers an adaptive energy-

efﬁcient mechanism according to the change of environment.

There are also plenty of data compression and data aggre-

gation approaches, which are studied to reduce the storage

consumption in WSNs, e.g. [18–20]. However, we observe that

these approaches operate at the sink or relay nodes. In another

word,theenvironmentaldataactually have been sensedand then

be processed. In this case, some storage and energy resources

have been used. Hence, in this paper, we study the storage-

efﬁcient problem by reducing the amount of data gathering at

the source nodes.

2.3. Compressive sensing

CS [23–25] is a generic method to recover the whole condition

with only a few sampled data [33–36]. Several effective

CS-based applications have been developed in data recovery

ﬁeld. For instance, network trafﬁc estimation [28], road trafﬁc

interpolation [26] and localization in mobile networks [27].

CS-based methods have the potential to reconstruct the

environment in WSN applications. It has been proved that the

environment can be near-optimally recovered even there are

more than 70% sensory data are missing [30], which motivates

us to exploit CS to reduce the amount of data gathering. Until

now, there is no CS-based method having been studied to

optimize the data gathering for environment reconstruction.

3. PRELIMINARIES

3.1. System model and notation

In environment reconstruction system, sensor nodes are

distributed in the given area to sense and gather data to the

sink during a given period of time. Suppose there are totally n

sensor nodes. The period of monitoring time is evenly divided

into t time slots, any sensor node can periodically (once per

time slot) sense the environmental data.

Deﬁnition 1. Environment condition (EC): EC is the real

environmental data sensed by sensor node i at the time slot

j denoted by x(i,j), where i = 1, 2

...,nand j = 1, 2...,t.

Deﬁnition 2. En

vironment matrix (EM): EM is the matrix of

all real environmental data, which is a mathematical way to

describe the dynamic environment. All ECs x(i,j) form a EM:

X = (x(i, j))

n×t

. (1)

Thereby, this is a matrix constituting of n rows and t columns.

A complete EM presents that all ECs are gathered, which

indicates the 100% accurate environment reconstruction.

Deﬁnition 3. Binary index matrix (BIM): BIM is a n × t

matrix, which indicates whether the sensor node i at time slot

j senses the EC or not. BIM is deﬁned by

B = (b(i, j))

n×t



1ifx(i, j) is gathered,

0 otherwise.

(2)

Deﬁnition4. Gatheredmatrix (GM): GM isthe matrix ofonly

gathered data by a WSN. Since some ECs may not be gathered,

elements of GM are either EC (x(i, j)) gathered by sensor node

or zero (b(i, j) = 0). Thereby, GM is an incomplete EM. GM

is denoted by G and can be presented by

G = X · B. (3)

Deﬁnition 5. Estimated environment matrix (E2M): E2M is

the result of environment reconstruction, which is generated by

interpolating the missing values in GM to approximate EM.

E2M is denoted by

X = ( ˆx(i, j))

n×t

. (4)

3.2. Problem statement

In this paper, we focus on the data gathering problem for saving

the resource consumption and acquiring accurate environment

reconstruction.

Section B: Computer and Communications Networks and Systems

The Computer Journal

, 2014

at Beihang University on December 23, 2014http://comjnl.oxfordjournals.org/Downloaded from

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