Energy efficient Gas Emission monitoring systemsusing Wireless Sensor Networks

M. A. RazzaqueFaculty of Computing

University Technology MalaysiaJB, Malaysia

Email:marazzaque@utm.my

Md. Akhtaruzzaman AdnanFaculty of Computing

University Technology MalaysiaJB, Malaysia

Email:adnan.iut05@gmail.com

Abdul Hanan AbdullahFaculty of Computing

University Technology MalaysiaJB, Malaysia

Email:hananfsksm@gmail.com

Abstract—Use of Wireless Sensor Networks(WSNs) in environ-mental gas emission monitoring system can provide inexpensive,portable, and easily accessible system. In most cases naive use ofWSNs in gas emission monitoring will fail to provide long-termand cost-effective system due to very high power requirementof most gas sensors. In this work, we support this claim bya quantitative analysis of the main operational energy costsof some popular gas sensors, radios, and sensor motes use inWSN-based gas monitoring systems. In light of the importanceof sensing level energy costs for power hungry gas sensors, weconsider compressed sensing as a potential approach to minimizesensing energy costs in Wireless Sensor Networks based gasemission monitoring system. Numerical experiments investigatingthe effectiveness of compressed sensing using real datasets showits potential for minimizing sensing and overall energy costs inWireless Sensor Networks based gas emission monitoring system.Results shown that, for gas monitoring, compressed sensing canprovide greater energy efficiency than transform coding, andmodel based adaptive sensing in Wireless Sensor Networks.

I. INTRODUCTION

The environmental impact of different gas emitted by hu-man activity is mostly invisible to us. Most people in the worldare living in areas that have worse conditions than the recom-mended by World Health Organization Air Quality Guideline(WHO AQG) value [1]. It is really important to monitor theenvironment to aware the people and build a greener andhealthy environment. Most existing gas pollutant monitoringsystems are made of few expensive, bulky, stationary sensorsplaced in inaccessible locations for people. It is very hard touse them for long-term measurement of emissions due to thehigh cost and power requirement. Wireless Sensor Networks(WSNs) is one of the solutions to this monitoring problem,as they can provide cheaper, lighter (weight), mobile, easilyaccessible (even wearable [2]), and long-term environmentmonitoring system [3]. Thus, researchers are investigating theuse of WSNs in environmental monitoring [4], [5].

Most existing works in this area mainly concentrate onways to use WSNs to gather environmental data and visualizethem [3], [6], [7], [8], [9], [10], [11]. In most cases, naiveuse of WSNs in gas emission monitoring will fail to providelong-term and cost-effective system, due to very high powerrequirement of the gas sensors [12], [13], [14]. They consumemuch higher energy than their related communication andcomputation energy costs in WSNs. For example, a CO2

monitoring system using GE/Telaire 6004 (GE) sensor [12]and TelosB mote can monitor the environment only for 13.9hrs

taking samples once in every 5 minutes. So to use WSN-based monitoring system in environmental gas emission, en-ergy management and conservation approaches [5], [4], [14]or less power hungry sensors [10] are required. This paperconsiders energy management approach to make WSN basedgas monitoring systems energy efficient and work for longerperiod.

Majority of the existing data-driven energy managementand conservation approaches [5] for WSNs [4] target reductionin communications energy at the cost of increased compu-tational energy. In principle, most compression techniqueswork on reducing the number of bits needed to represent thesensed data, not on the reducing the amount of sensed data,hence they are unable to minimize sensing energy costs inWSNs. Importantly, in most cases these approaches assumethat sensing operations consume significantly less energy thanradio communications [14], [4]. In fact, the energy cost ofsensing in gas sensors [12], [13], [14] is always significantlyhigher than communication and computation energy costs. So,in WSN-based gas monitoring systems, approaches which canminimize sensing as well others energy costs [4] are needed.

Compressive sensing (CS) provides an alternative to Shan-non/Nyquist sampling when the signal under consideration isknown to be sparse or compressible [15], [16], [17], [18].Unlike other compression algorithms they remove redundancyin the signal during the sampling process, leading to alower effective sampling rate and reduced sensing energycost. Provided certain conditions are satisfied, the signal canstill be accurately recovered even when sampling at a sub-Nyquist rate [15], [16], [17], [18]. The main objectives ofthis work are twofold: (i) to do a quantitative analysis ofthe main operational energy costs for a selection of off theshelf gas sensors, radios, and sensor motes used in WSN-basedmonitoring systems and (ii) to exploit CS for energy efficientgas sensing and monitoring using WSNs. We will also includea comparative study between CS and its counterparts adaptivesensing approach [19], and transform coding [20].

Section II provides a brief overview of related work. Sec-tion III presents a quantitative analysis of the main operationalenergy costs of popular gas sensors, radios, and sensor motesuse in WSN-based gas monitoring systems. An overview ofCS in WSNs is presented in section IV. The evaluation insection V presents the results of numerical experiments ofCS on some real gas sensor datasets and shows the energyefficiency of it. It also includes a comparative study between

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CS and its counterparts. Finally section VI concludes the workwith some future directions.

II. RELATED WORK

Use of WSNs in environmental monitoring is gainingpopularity recently. There are some existing works in thisarea. Most existing works in this area mainly concentrate ondifferent approaches to exploit WSNs to gather environmentaldata and visualize them [3], [6], [7], [8], [9], [10], [11]. Theseworks do not explicitly consider the energy efficiency issue ofgas emission monitoring using WSNs. But most cases naiveuse of WSNs in gas emission monitoring will fail to providelong-term and cost-effective system [12], [13], [14]. Inclusionof energy management and conservation in this systems isimportant.

Existing data-driven energy management and conservationapproaches [5] for WSNs [4] especially compression tech-niques work on reducing the number of bits needed to representthe sensed data, not on the reducing the amount of senseddata, hence they are unable to minimize sensing energy costsin WSNs. Importantly, in most cases these approaches assumethat sensing operations consume significantly less energy thanradio communications [14], [4], which is not always true in gassensors [12], [13], [14]. Adaptive sampling algorithms [21],[19] dynamically estimate sampling rate can be useful inminimizing sensing energy cost in WSNs. This approach canbe integrated into a model based data gathering approach [22]to obtain compression in all operations level. Transform codingis a necessary element in CS, but suffers as a standalonecompression in WSNs due to its inherent inefficiencies [4].These motivate the use of CS like alternative compressiontechniques in WSNs.

There are number of research works on CS related toWSNs. Most existing works [23], [24], [25], [26] exploit CS atgathering level, assuming that all sensors sample the physicalphenomenon at each sampling instance. Thus they are missingthe acquisition or sensor level compression, which is one of thekey benefits of CS. Moreover, existing works do not explicitlyanalyze and quantify sensing level energy savings, and theresulting overall energy cost savings due to CS in gas sensingand monitoring like applications.

III. OPERATIONAL ENERGY COSTS OF WSN-BASEDMONITORING SYSTEMS

In any WSN application, the energy used by a node mainlyconsists of the energy consumed by computing, communi-cating, and sensing data, and sleeping. In communication itincludes sending, receiving, and listening. Switching of state,especially in the radio, can also cause significant energyconsumption. Here we focus on energy consumption duringa single sampling period. In calculation of these operationalenergy costs in a sensor node, we consider the MAC protocolas it has a significant impact on energy consumption. Weconsider the popular BMAC [27]. For simplicity, we considera common sampling period of 360 seconds for all sensors.In calculating operational energy costs, we used equationsfrom [4] and information from [13], [28], [29], [12], [30],[31], [32], [33]. Table I presents the sensing energy costsof few popular gas sensors and Table II presents comparison

between sensing, computational, and communication energycost normalized by the communication cost. It is very clearfrom the Table II that in gas sensors sensing energy costs aresignificantly higher than the communication and computationenergy costs. So, inclusion of sensing energy cost in energymanagement or compression technique is important to makeWSN-based gas monitoring systems energy efficient.

TABLE I: Sensing energy costs for gas sensors

Types Sensors Esm(J)VOC/CO/CH4 MiCS-5521[13] 4.8

NO2 MiCS-4514[28] 5.2O3 MiCS-OZ-47[29] 105

CO2 GE/Telaire 6004[12] 225

TABLE II: Comparison between Ecomm, Esm, and Ecomp

Sensors TelosB Mica2Esm Ecomp Esm Ecomp

MiCS-5521 26.98 1.84 17.242 5.2MiCS-4514 29.3 1.99 18.7 5.63

MiCS-OZ-47 582.9 4.34 372.75 12GE /Telaire 6004 1249.25 9.03 798.2 25.64

IV. COMPRESSED SENSING

The CS (also known as compressive sampling) field has ex-isted for at least four decades, but recently researchers’ interestin the field has exploded especially in areas of applied math-ematics, computer science, and electrical engineering [34],[15], [35]. CS is a novel sensing/sampling paradigm that goesagainst the traditional understanding of data acquisition andcan surpass the traditional limits of sampling theory.

A. Overview of compressed sensing

In earlier part of this section we briefly summarize the keyelements of CS and in later parts we discuss CS in WSNs.For more advanced and detailed information on CS theory,readers are referred to [15], [16], [17], [18] and referencestherein. From [15], [16], [17], [18], we summarize the keyelements of CS in the following:

Signal representation: One of the preconditions for anysignal to be compressible by CS is that the signal is sparseor compressible. Mathematically, if x is the signal (discrete)of interest, expressed by the vector x of size N and {ψi}Ni=1is a given basis, we can represent every signal x ∈ RN interms of coefficients {αi}Ni=1 as x = ΣN

i=1αiψi and x can becompactly presented as x = Ψα. A signal x is K-sparse if||x||0≤ K, which means only K � N entries are nonzero.Many natural and manmade signals are not strictly sparse, butcan be approximated as such. These are known as compressiblesignals [17], [18].

Compressive measurement: CS integrates the signal sam-pling and compression steps into a single process [15], [16],[17], [18]. In CS we do not acquire x but rather acquireyM×1 = Φx linear measurements or samples using an M ×Nmeasurement matrix, where M < N . This linear measurementalso is known as a projection of x onto M compressive samples

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y according to a projection matrix Φ [15]. Finally, we can re-cover the signal by exploiting its sparsity or compressibility. Inorder to recover a good estimate of x from the M compressivemeasurements, the measurement matrix Φ should satisfy therestricted isometry property (RIP) [15], [16], [17], [18].

Reconstruction algorithm: The reconstruction problem ofthe original signal x expressed by x = Ψα is to determine αfor a given measurement y = ΦΨα and known matrices Φand Ψ . This is an under-determined linear system, as thenumber of equations M is much smaller than the numberof variables N (i.e., number of entries of α). Hence thereare infinitely many signal coefficient vectors x′ that producethe same set of compressive measurements y = Φx andto recover the “right” signal we need to exploit a prioriknowledge of its sparsity or compressibility. In practice, stablerecovery algorithms rely on the RIP, hence require at leastM = K log(N/M) measurements. These recovery algorithmscan be grouped into three types: (i) l1 minimization, (ii) GreedyApproach and (iii) Combinatorial Approach [18].

B. Compressed sensing in WSNs

Considering the availability of sparsity or compressibilityin WSNs signals due to spatio-temporal correlations within thesensor readings, CS and Distributed CS can work as potentialcompression approaches for WSNs [36], [37], [24], [38], [39].The asymmetric computational nature of CS and DCS makesthem even more attractive for compression in WSNs. In CSmost computation takes place at the decoder (sink), ratherthan at the encoder (sensor nodes), thus sensor nodes withminimal computational performance can efficiently encodedata. In addition, CS has two further advantages: gracefuldegradation in the event of abnormal sensor readings and lowsensitivity to packet loss. Hence CS and DCS are promisingapproaches for removing redundancy during sensing operationsof WSN-based gas monitoring systems and minimize energyconsumption. CS for WSNs exploits only temporal (intra-signal) structures within multiple sensor readings at a singlesensor and DCS exploits spatial (inter-signal) correlationsamongst nearby sensors [36], [37], [24], [38], [39]. Due tospace limitations and gas monitoring systems can be portableand single node-based, this paper works on CS.

For the implementation of CS in WSNs, we need to knowthe measurement matrix Φ and the representation basis Ψ. Φdirectly corresponds to the measurement or sampling schedul-ing of a WSN application and Ψ use in signal sparsifying andreconstruction algorithm to determine α and then recover theoriginal signal x.

1) Measurement or Projection matrix Φ: The measurementor projection matrix mainly depends on the signal of interest,whose detail may be unknown to an user. There are twopossible solutions to this problem: (i) Machine learning and(ii) Random projection. Learning-based approach in WSNscan be expensive in terms of computation and communicationcost. Random projections can guarantee recovery of a near-optimal approximation of compressible data, with a very littledegradation of performance [15]. Precisely, K log(N) randomprojections of the data can produce an approximation witherror comparable to the best approximation error using theM -largest transform coefficients [40].

In WSNs, sensors can obtain a Φ from the sink or they cangenerate it using the same pseudo-random number generator atall nodes including the sink [40]. Once sensor nodes in WSNsknow Φ, they can calculate the compressive measurements byprojections of the data x onto the measurement vectors, yi =<Φi, x >, Φi is a ith row of Φ.

2) Representation basis Ψ: Representation basis in CSdepends of the nature of the signal of interest. There aretwo main criteria in selecting a good representation basis (Ψ):(i) its corresponding inverse has to sufficiently sparsify thesignal x, and (ii) it has to be sufficiently incoherent with thecorresponding measurement matrix Φ. Finding such a basisis not a trivial job but certain known features of the signalcan help in simplifying it [26], [41]. Based on the nature ofWSNs application signals (temporal and spatial), we can usethe Fourier Transform(FT), Discrete Cosine Transform (DCT),Wavelet Transform (Haar, Daubechies) [20] basis for sparserepresentation of the signal. Typically, the DCT is suitablefor smooth signals whereas wavelet-based transforms are moresuitable for piecewise constant data [41].

V. EVALUATION

This section evaluates the effectiveness of CS, transformcoding (TC), and adaptive sampling based predictive coding(PC) in minimizing sensing and overall energy costs in WSN-based gas monitoring system.

For the evaluation, we used two real life sensor datasets.Dataset one is from the BeACON project [42] and the secondone is from the OpenSense project [43], [44]. The first datasetis for CO2, and second one for O3 (ozone) emission. Thefirst dataset is taken from the BeACON project’s SkylineHigh School site for the month of August, 2012 [42], whichcollected CO2 readings once in every 5 minutes. The BeACONproject hardware was more powerful than typical WSN nodesand the nodes were connected to mains power. Hence, foranalysis in a WSN environment, we assumed the hardwaresimilar to the CitySee project [3], that is, TelosB [33] nodesand GE/Telaire 6004 [12] CO2 sensors. In dataset two, data istaken for O3 [43], [44], where sensors collects O3 samplesonce in every 20 seconds. Both of these projects provide real-life data on these emissions as well as others.

For the evaluation, we used Matlab and the SparseLab [45]. For CS reconstruction, we use a standard reconstruc-tion algorithm (Basis Pursuit [46]). Random projection matrixwas used for the measurement and Haar wavelet transform wasused for sparsification.

Results are presented in three parts. The first one presentsthe sparsity performance for the datasets to show that theyare compressible. The second part of the results (Figures 3,4,and 5) includes the performance of CS, transform coding(TC), and adaptive sampling based predictive coding (PC)in minimizing energy costs in monitoring. For this part,we have generated separate results for CS and comparativestudy between CS, TC, and PC. Due to space limitations,we only present plots for comparative study, which includesthe performance of CS as well. The third and final partpresents the result for both datasets in tabular form. We usedsensing energy cost saving, overall energy cost saving, absolutemean reconstruction error (Rmean), and root mean-squared

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error (RMSE) as performance analysis parameters. TypicalWSNs applications fall into one of two categories: periodicmonitoring and event detection. Hence, in the experiments,we did the analysis for both. For the results calculation, weran each experiment 100 times and calculated the average.

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Fig. 1: Sparsity analysis of temporally correlated CO2 emissionreadings [42] using DWT.

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Fig. 2: Sparsity analysis of temporally correlated O3 emissionreadings [43] using DWT.

Figures 1 and 2 present the results sparsification or com-pressibility test for CO2 and O3 datasets. As shown in Fig-ures 1 and 2, considered temporally correlated CO2 and O3

signals are compressible as their Discrete Wavelet Transform(DWT) analysis show that number of significant waveletcoefficients are very limited. For instance, the approximatenumber of significant coefficients (using balanced sparsity-norm thresholding) for temporally correlated CO2 and O3

are 32 (out of 1024, in figure only 256 are shown) and37 out of 512 respectively. These are the values of K forthe respective signals. Most importantly these datasets arehighly compressible as their sorted (descending order) waveletcoefficients have good fit with the power law (shown inFigures 1 and 2), hence strongly satisfy the compressibilitycondition [17], [18].

For the comparative study, we present three plots. The firstone provides results for temporally correlated CO2 and secondone for O3 signals, where we compare CS, transform coding(TC), predictive coding with uniform sampling (PC-US) andadaptive sampling (PC-AS). Final one presents the comparativestudy in terms of event detection. These results are mainly interms of reconstruction performance and energy saving.

For transform coding (TC), as in CS sparsity analysis,we use the Haar wavelet transform. In particular, we exploitthreshold-based transform coding, where transform coefficientsunder certain threshold values are discarded and others are sentto the sink, reducing communication cost. Balanced sparsity-norm thresholding based 2-level Haar wavelet transform is

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Fig. 3: CS in temporally correlated CO2 readings [42].

used. For the temporally correlated sensor readings, eachsensor collects readings over ns sampling periods and thenapplies transform [47], [4] coding to determine the coefficientsof each measurement, and after thresholding, the node sendsthe significant coefficients to the sink. For simplicity, we donot consider any encoding of the transform coefficients [48].

In general, compressive sensing (CS) integrates the signalacquisition and compression steps into a single process [15],[16], [17], [18]. Herein, we combine adaptive sampling [19]and an autoregressive based prediction model [22] for tempo-rally correlated readings or signals. Instead of CUSUM test,we use prediction error to detect non-stationarity changes insensor readings. For the detail of these schemes, please see thereferences [19], [22].

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Fig. 4: CS in temporally correlated Ozone readings [43].

Figures 3 and 4 present the results for the comparativestudy between CS,TC, PC-US, and PC-AS. We have usedfixed N = 512 and two values of M (mentioned in figures).

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Performance is summarized in terms of sampling energyminimized Esmmin , overall energy saving Esaving , Rmean,and RMSE in Tables III. CS using M1 (CS1) and PC-ASperform less well than TC and PC-US in terms of Rmean,RMSE but they provide better SR, and hence better sensingand overall energy savings. CS with M1 suffers as M1 (wehave chosen M = 4K) is close to 4K, where 4K is thestandard required samples for satisfactory reconstruction.

Figures 3 and 4 show the results for regular monitoringapplications. Figure 5 presents the result of event detectionusing CS,TC, PC-US, and PC-AS for a temporally correlated(O3) signal. It is clear from the figure that in TC, CS, andPC-US event detection is always possible with good accuracybut PC-AS is unreliable (Figure 5) since down-sampling mightcause the event to be missed, as in the considered scenario.

As Table III shows, in terms of Rmean and RMSE, allschemes performing well above the sensor tolerances [12],[29]. In case of CO2 and O3 signals, in terms of sensing andoverall energy savings CS outperforms all its counterparts. Itis clear from the Table that TC and PC-US perform poorly forCO2 and O3 signals as the sensing cost of these sensors’ areextremely high compared to others.

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(a) Signal Reconsrtuctions

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Fig. 5: Comparison between CS, PC-US, PC-AS, and TC in termsof event detection for temporally correlated O3 signal

TABLE III: Numeric Experiment: Comparative study with tempo-rally correlated signals

Approach SR EsmminEsaving Rmean RMSE

CS1CO2 4 75% 74.9% 1.29 .0028CS2CO2 2 50% 49.4% .5 .0032TCCO2 1 0% .06% .37 .0012

PC − USCO2 1 0% .036% .64 .0017PC − ASCO2 1.21 17.41% 37.39% .67 .0036

CS1O3 4 75% 74.64% .68 .0053CS2O3 2 50% 49.48% .27 .027TCO3 1 0% .76% .25 .0145

PC − USO3 1 0% .74% .37 .017PC − ASO3 1.13 10.75% 11.6% .411 .036

It is quite evident from the above table and the discussionthat, CS has the potential to minimize sensing and overall

energy costs in WSN-based gas monitoring systems, hencemaking them energy efficient. Even it can outperform mostof their counterparts. However, delay can be an issue inlarge WSNs and lack of sparsity can be a problem in smallWSNs. TC and PC-US perform less well than CS, and PC-AS as they do not support sensing level compression. Forthis reason, in power hungry gas sensors e.g. CO2 sensors,communication and computational energy cost savings arealmost nullified by high sensing costs. Due to the cost of modelupdate and re-training, PC-US, and PC-AS might performspoorly in dynamic networks and environments where frequentupdates are necessary. Hence, PC-AS may fail to detect events(Figure 5).

VI. CONCLUSION

Most existing works on energy management of WSNsdisregard sensing energy cost assuming that it is significantlyless than that of sensor data communication. In this work, wehave quantified the main operational energy costs in WSNs forsome popular sensors, radios, and sensor motes. The resultspresented in Table III clearly show that in a number ofpractical applications, the energy consumption of the sensingoperation is comparable to, or even greater than, that of theradio. Cognizant of the importance of sensing energy costs, wehave evaluated CS as potential approaches in reducing sensingand overall energy costs in WSNs. To show the potential ofCS in minimizing sensing and overall energy costs, we havepresented three sets of results. The first set clearly shows thattemperature and CO2 signals are sparsely representable andso compressible, allowing CS to be effectively applied. Theresults also give the reconstruction accuracy of CS. The secondset of results quantifies the potential of CS in minimizingsensing and overall energy costs. Finally, a comparative studybetween CS with their counterparts was undertaken. Thisstudy clearly showed that CS are better schemes in terms ofminimizing sensing and overall energy costs than TC, PC-US,PC-AS, and ASAP. These results show that CS can minimizesensing and overall energy costs and can be used for energyefficient data sensing and gathering in WSNs, especially inWSNs with energy hungry sensors.

The computational complexity of CS encoding is notsignificant but decoding complexity (O(n3)) [49] can be. Dueto decoding complexity, CS might not be suitable for in real-time applications employing large WSNs. Investigation of de-coding complexity reduction for CS is a recommended futureresearch direction. In experiment, we considered clusteredWSNs, which might be unavailable in some WSN applications.Investigations for other WSNs structures would be of merit.

ACKNOWLEDGMENT

This work is fully supported by UTM, Malaysia undergrant number PY/2012/00306 (Vote No: 4D062).

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