A Survey on Gas Leakage Source Detection and Boundary ... · 3. Small autonomous mobile robots, called as electronic nose: Small size autonomous robots with low complexity and high

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A Survey on Gas Leakage Source Detection and BoundaryTracking with Wireless Sensor Networks

Lei Shu, Senior Member, IEEE, Mithun Mukherjee, Member, IEEE, Xiaoling Xu, Member, IEEE,Kun Wang, Member, IEEE, and Xiaoling Wu, Member, IEEE

Abstract—Gas leakage source detection and boundary trackingof continuous objects have received a significant research atten-tion in the academic as well as the industries due to the loss anddamage caused by toxic gas leakage in large-scale petrochemicalplants. With the advance and rapid adoption of wireless sensornetworks (WSNs) in the last decades, source localization andboundary estimation have became the priority of research works.In addition, an accurate boundary estimation is a critical issuedue to the fast movement, changing shape, and invisibility of thegas leakage compared to the other single object detections. Wepresent various gas diffusion models used in the literature thatoffer the effective computational approaches to measure the gasconcentrations in the large-area. In this survey, we compare thecontinuous object localization and boundary detection schemeswith respect to complexity, energy consumption, and estimationaccuracy. Moreover, this survey presents the research directionsfor existing and future gas leakage source localization andboundary estimation schemes with WSNs.

Index Terms—Wireless Sensor Networks (WSNs), DiffusionModel, Source Localization, Boundary Detection and Tracking

I. INTRODUCTION

With the rapid development of petrochemical industriesin recent years, the interest in gas leak detection and lo-calization has increased due the loss of life, injuries, anddamaged equipment caused by the toxic gas leakage [1],[2]. Apart from the manufacturing and production point ofview, real-time information about the distribution area ofhazardous toxic gases in large-scale industry is needed toensure safety precaution for the first-line working staff duringvarious operations in production, storage, transportation, andusage. Thus, gas leakage source estimation continues to bea major part of intelligent industrial sensing systems. Gasleakage source localization and boundary tracking have beenintensively investigated in the existing literature as follows:

1. Fixed cable-based sensing: Traditional monitoring systemconsists of high-resolution sensors with fixed installation. Thesensor data is sent to the control center through long-distancecables, which results very high cost. Deploying a large numberof fixed cable-based sensing devices is not cost-effective in avery large monitoring area.

L. Shu, M. Mukherjee, and Xiaoling Xu are with the Guangdong ProvincialKey Lab. of Petrochemical Equipment Fault Diagnosis, Guangdong Univer-sity of Petrochemical Technology, China. E-mail: [email protected],[email protected], [email protected].

K. Wang is with the School of Internet of Things, Nanjing University ofPosts and Telecommunications, Nanjing, China. E-mail: [email protected].

X. Wu is with the Guangdong University of Technology, Guangzhou,Guangdong, China, the Guangzhou Institute of Advanced Technology, ChineseAcademy of Sciences, Guangzhou, China, and the Shenzhen Institutes ofAdvanced Technology, Chinese Academy of Sciences, Shenzhen, China. E-mail: [email protected].

2. Big mobile robots: Expensive mobile robots are usedto localize underwater gas leakage [3], [4]. Generally, theserobots are designed in large size with good mobility, however,require very high cost for manufacturing as well as mainte-nance. Since the localization in robot-system is difficult dueto its high degree of mobility, the high cost restricts furtherapplicability in the large-scale monitoring.

3. Small autonomous mobile robots, called as electronicnose: Small size autonomous robots with low complexity andhigh mobility are widely used for sensing continuous objects.The smell and biochemical sensors in a single robot measurethe gas density and estimate the direction as well as velocityof the gas diffusion. The collaborative biochemical gas sourcelocalization is based on adaptive swarm intelligence [4]. Thislocalization scheme forms an autonomous group by morerobots that have a wide coverage. However, the mobility ofthese robots is limited by the energy consumed by a longtime in the large area. As the gas leakage source is estimatedaccording to the wind field distribution, the localization accu-racy is depends on the environmental factors, e.g., wind speedand wind direction.

4. WSN-based localization and tracking: Wireless sensornetworks (WSNs) are multi-hop systems with randomly de-ployed sensor nodes in the monitoring area. The gas concentra-tion, diffusion direction, speed, and other physical parametersare measured by various senor nodes. Compared with previouscollaborative localization and tracking systems, WSNs havethe following advantages:

• Due to low-price sensor nodes, the self-organized WSNsperform better target estimation and localization than therobots which have higher cost and lack of cooperativemovement.

• As the number of mobile robots is less, a long-timeobservation is required for the localization of objects. Incontrast, the widely distributed sensor nodes can quicklyestimate the location of the continuous objects,

• The sensor nodes are easily deployed in hard-to-reachareas where the robots have no-access for monitoring.

Along with various available high technologies in sensingdevices, and also due to the advantage of low cost, easeof deployment, energy efficiency, and mobility (as illustratedin Fig. 1) compared to the traditional field bus, WSNs areevolving to become a promising approach for manufacturersas well as plant designers to solve many critical issues likegas leakage source localization and boundary tracking ofcontinuous objects in large-scale industrial area as well as inenvironments.



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Fixed cable-based sensors

Robots with strong mobility

Autonomous robots with

smell and biochemical sensors

Wireless sensors

Hand-held devices

Sensor nodes that harvest

energy from environment by solar pannel

Fig. 1. With the advantages of low cost, ease of deployment, and energy efficiency, various types of sensing elements1 are used to monitor gas leakage inlarge-scale industries as well as in environments with WSNs.

This paper provides a comprehensive study on currentlyavailable gas diffusion models for localization of gas leakagesources. We further categorize the localization algorithms fromthe view of estimation accuracy and energy consumptionissues. Since continuous objects are diffused in a wide regionwith the non-uniform diffusion velocity and the accelerationaccording to the surroundings, tracking of continuous objectsis more difficult than an individual object tracking. Moreover,the study on boundary estimation of continuous objects hasbecome popular in the last decade. We also present a de-tailed survey on boundary detection and tracking algorithmsproposed in the literature. In addition, this survey highlightsthe research issues of localization and boundary tracking ofcontinuous objects in large-scale industries and environments.

The rest of this paper is organized as follows. Section IIbriefly overviews various gas diffusion models used in theliterature. We present the gas source localization algorithmsin Section III. The recent advancements and open challengesof boundary detection algorithms for the continuous objectsare discussed in Sections IV. Finally, conclusions are drawnin Section V.

II. GAS DIFFUSION MODEL

Several research efforts were devoted to characterize basictheories of gas diffusion in the gas leakage area. Gas dif-fusion models are mainly classified into static and dynamicenvironment-based models. According to the spatial boundary,these models are further categorized into open space andlimited space diffusion models. Various physical propertiesof gas-cloud further characterize these above models into

1The use of some images is for nonprofit educational purpose.

Fig. 2. Classification of gas diffusion models.

heavy as well as light gas cloud-based models. Most of thetoxic gases, e.g., liquid ammonia, liquefied petroleum gas,chloroethylene, and benzene belong to heavy gas category.Fig. 2 illustrates the classification of models based on heavyand neutral gases. Generally, Gaussian plume [5] and puffmodel [6], 3-D finite element (FEM-3) model [7], Britter andMcQuaid (BM) model [8], Sutton model [9], and gas turbulentdiffusion model [10] consider static environment characteristicof the gas diffusion. Since the gas diffusion is often affectedby the wind and the barrier in real environment, therefore,present study mainly considers simple gas diffusion model,however, more practical conditions have yet to be addressedin the future.

A. Gaussian Model

The Gaussian model [5], which is one of the oldest compu-tational approaches, calculates the concentration of hazardousgas in an effective manner. This approach assumes a Gaussiandistribution of the hazardous gas as follows. A hazardousgas diffusion has a normal probability distribution with the



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standard deviation that depends on the atmospheric turbulence,the distance from gas source to sensors, and the time durationof gas leakage. This model is more suitable for light gasdiffusion which has a density similar to that of the air. Incontrast, only theoretical analysis is possible in heavy gascategory. However, this model is used for the prediction of thediffusion of non-continuous air pollution plumes. Generally,the Gaussian model is categorized into plume model and puffmodel, which are used to analyze continuous source diffusionand instantaneous source diffusion, respectively.

1) Gaussian plume model: Gaussian plume model [5] isused to simulate the concentration distribution of the neutralcontinuous gas at steady state. Assuming a static wind speedand direction, and atmospheric stability, the gas concentrationcan be expressed as

C(x, y, z) =Q

2πσyσzuexp

(− y2

2σ2y

)[exp

(− (z −H)2

2σ2z

)+ exp

(− (z +H)2

2σ2z

)](1)

where C(x, y, z) denotes the gas concentration in mg m−3

at point (x, y, z), Q is the leakage-rate in mg s−1, H is theeffective gas source height in m, u is the average wind velocityin m s−1, σx, σy , and σz represent the diffusion parameterswith wind in (x, y, z) direction, respectively, and x, y, andz denote the distance in (x, y, z) direction in m, respectively.Using (1), the isoconcentration curve is given by

y = ±{2σ2

y ln

(Q

2πσyσzu C(x, y, z)

[exp

(− (z −H)2

2σ2z

)+ exp

(− (z +H)2

2σ2z

)])}1/2(2)

The general assumptions in Gaussian plume model are asfollows:

1) The gas is steadily and continuously released in ahomogeneous atmospheric turbulence with a constantwind velocity.

2) The chemical conversion process and natural sedimen-tation are ignored.

3) The wind velocity is greater than or equal to 1m s−1.4) The maximum distance from leakage to source is

3000m. However, the distance should be shorter in thehorizontal direction with the homogeneous geographicalconditions.

In recent years, Gaussian plume models [5] are widely usedto estimate local pollution levels. Further, Fan et al. [11]improved this model by adding a terrain factor with geo-graphic information system (GIS). Although, the accuracyof gas diffusion has been increased, the model parametersneed to be frequently updated. Briant et al. [12] proposed anovel solution based on Gaussian plume model. This modelreduces the error in a line source formula when the wind isnot perpendicular to the line source. In addition, this model

is suitable to simulate NOx concentration2 in large-scale.Afterward, Ristic et al. [13] presented a theoretical analysisof best achievable estimation accuracy in estimation of theGaussian plume model parameters. The numerical resultsillustrate that the parameter-estimation accuracy depends onthe sensor measurement accuracy, the density of sensors, andthe quality of prior meteorological advice.

2) Gaussian puff model: This model considers the diffusionof pollutants from an instantaneous point source. Assumingthe axis of abscissae coincides with wind direction and thecoordinate’s initial point is located in the stack’s socle, theconcentration of pollutants to be emitted by a instantaneouspoint source in Gaussian puff model [6] is expressed as

C(x, y, z, t) =Q

(2π)3/2

σxσyσzexp

(− (x− ut)2

2σ2x

)× exp

(− y2

2σ2y

)[exp

(− (z −H)2

2σ2z

)+ exp

(− (z +H)2

2σ2z

)],

(3)

where c(x, y, z, t) denotes the gas concentration at (x, y, z)point at time t in mg m−3.

This model is further improved to study the influence ofuncertainties on the estimation of the parameters in a simple3-D dispersion model. The issue of propagation uncertaintyhas been solved by using polynomial chaos method with acost of additional computation [14]. In addition, the Gaussianpuff model is used for the methane gas diffusion with an ad-justed model parameters in coal-mine network topology [15].This model estimates the safest and fastest escape routes forcoal miners in a disaster. Further, the Gaussian puff modelis extended to the social force model [16] that simulatesthe whole process and finds an efficient evacuation duringa terroristic attack with toxic gas in a sub-way station. Astochastic extension of the Gaussian puff model helps to char-acterize atmospheric pollutant concentration with the multiplecontaminant clouds [17].

B. 3-D Finite Element Model (FEM-3)

The FEM-3 [7], which was first proposed in 1979, simulatesthe liquefied gas diffusion. It handles a variety of heavy gasdiffusion. FEM-3 is composed of the momentum conservationequation, the continuity equation, the heat balance equation,the diffusion mass balance equation, and the ideal gas stateequation [7]. Basically, this model is based on the Galerkinmethod. In addition, this model considers the turbulenceproblem using the gradient transporting theory and the mixing-length theory.

The discharging diffusion process of oxygen is dynamicallysimulated based on the FEM-3 [7] in a sealed space. Anotherdiffusion process is described by the simplified 3-D diffusionmodel according to the characteristics of Chloroethylene [18]at stable as well as unstable atmospheres. Afterward, therelationship between diffusion concentration and diffusion

2NOx is a generic term for the mono-nitrogen oxides NO and NO2 (nitricoxide and nitrogen dioxide)



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TABLE IFEATURE COMPARISON OF GAS DIFFUSION MODELS

Model Application Complexity ComputingPower Accuracy Relevant Parameter Characteristic Disadvantage

GaussianPlume [5]

Large-scaleand long time Easy Few Bad

Density, explosion limits,air temperature, windvelocity and direction,and atmospheric stability

Simulateinstantaneous orcontinuousleakage

Only applicable toneutral gas, lowaccuracy

GaussianPuff [6]

Large-scaleand short time Easy Few Bad

Density, explosion limits,air temperature, windvelocity with direction,and atmospheric stability

Simulateinstantaneouspoint source

Only applicable toneutral gas, lowaccuracy

FEM-3 [7] Un-constrained Difficult Large GoodAir temperature, andwind velocity withdirection

Continuous andlimited time gassource leakage

Huge computationsand difficultcomputer simulations

BM [8] Large-scaleand long time Easy Few Normal

The averageconcentration of gascloud transverse section,

Used continuousleakageexperimental data

Empirical model,poor extensibility

Sutton [9] Large-scaleand long time Easy Few Bad

Diffusion parametersassociated withmeteorological conditions

According to theturbulentdiffusion theory

Simulation error withcombustible gasleakage diffusion

GasTurbulentDiffusion[10]

Large-scaleand long time Easy Few Bad

Density, explosion limits,air temperature, windvelocity and direction,and atmospheric stability

Continuous gasleakage

Only applicable toneutral gas, lowsimulate accuracy

distance is discussed. This model is widely applied in the gasleakage diffusion with continuous or limited time duration. Forinstance, Hu et al. [19] studied the changing nature of the gasconcentration using the FEM-3 in the real conditions with asealed space. High accuracy and low computation complexityare obtained for estimating the gas concentration. In addition,this model is also applicable for the gas leakage diffusion in amore complex terrain, e.g., diffusion near buildings. However,the estimation error increases due to many parameters thatneed to be properly estimated in this model.

C. Britter and McQuaid (BM) ModelIn 1988, Britter and McQuaid estimated the heavy gas diffu-

sion that was continuously and instantaneously released fromarea sources. Basically, Britter and McQuaid (BM) model is anempirical model where the gas leakage diffusion phenomenonis described through a series of simple graphs and relationalexpressions [8]. This method provides a high computationalefficiency with simple and intuitive expressions, however, ithas a very low precision and poor extensibility. It is observedthat this model is mainly suitable for neutral and heavy gasdiffusion in large-scale gas leakage area. Afterward, Hannaet al. [8] performed a non-dimensional analysis. Agreementbetween this analytical, and Britter and McQuaid experimentalcurves is observed.

However, BM model, a benchmark of a screening model,is not suitable for city and industrial area with large surfaceroughness due to its limitation of derived ranges. Since theadvanced simulation model demands accurate and preciseestimation, this model has been gradually replaced by othergas diffusion models as discussed next.

D. Sutton ModelSutton model [9], which was widely used for pheromone

dispersion model, considers the diffusion problem using tur-

bulent diffusion statistical theory. It estimates the gas concen-tration at any point (x, y, z) downwind of a point source as

C(x, y, z, h) =

Q exp

(− y2

C2yx

(2−n)

)πCyCzux(2−n)

[exp

(− (z − h)2

C2zx

(2−n)

)+exp

(− (z + h)2

C2zx

(2−n)

)],

(4)

where Q denotes the release rate, Cy and Cz are the respectivehorizontal and vertical diffusion coefficients, respectively, n isa parameter (0 < n < 1) dependent on the vertical profile ofwind velocity, and h is the height of the source above groundwith an assumption that all sample locations are at the sameheight as the source, i.e., z = h. However, this model is lessapplicable for the combustible gas due to the significant errorin leakage estimation.

E. Gas Turbulent Diffusion Model

Turbulent diffusion model is a static plume model basedon turbulent diffusion theory. It was first applied for staticgas source localization. The gas diffusion is estimated in ahorizontal-flow of the wind according to the distribution of gasconcentration in the 2-D plane. This model is one of the widelyused mathematical models in the static gas source localization.The gas concentration C at coordinate point (x, y) at time tis expressed as [10]

C(x, y, x′, y′, t) =q exp

(V (x−x′)

2K

)π1.5Kd

×∫ ∞(d/2√Kt)

exp

(−ζ2 − V 2d2

16K2ζ2

)dζ

(5)

where q is gas leakage velocity, t is gas leakage time, V isthe wind speed with the positive direction of x-axis, K is gas



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diffusion coefficient, and d is Euclidean distance from arbitrarypoint (x, y, z′) to gas source point (x′, y′, z′) in z axis. LetC ≡ 0 at t ≤ 0, then we rewrite (5) at t→∞ as

C(x, y, x′, y′,∞) =q exp

(− V

2K (d− (x− x′)))

2πKd(6)

It is observed that the gas concentration is higher near thegas source than anywhere else at t → ∞. Moreover, the gasdiffusion has the same direction as wind-flow.

An odor source localization approach [10] was proposedbased on the turbulence model to identify the static odorsource in a stable wind-field. The spatially distributed sensor-array is used to monitor odor concentration. Afterward, theperformances of these algorithms are discussed using staticplume model [20]. Although, the positioning accuracy and thesource localization trends are demonstrated under the staticplume environment, the localization accuracy is low in dynam-ic environment. Recently, Xiao et al. [21] studied gas diffusiontheory and further derived a universal function of gas diffusionmodel with and without wind-field. Table I summarizes thefeature comparison between gas diffusion models. Since thelocalization error increases due to the nonlinearity in the gasdiffusion model, the study of particle filter would be a futureresearch work.

III. GAS SOURCE LOCALIZATION

Continuous target localization and tracking are two majorresearch issues in WSNs applications. Target tracking is ap-plied to various applications, e.g., military, anti-terrorism, anti-riot, industrial and environmental monitoring, and the like.Here, we focus on gas leakage source localization, whichbelongs to the continuous target localization based on WSNs.However, constrained by physical size of sensor nodes, hard-to-reach area, and short distance wireless charging due to highinterference, limited battery-powered sensor nodes bring majorchallenges in localization and tracking operations. A summaryof existing gas source localization algorithms with WSNs isprovided in Fig. 3. We present a detailed overview on gasleakage localization and tracking problem from the view ofprecision, robustness, and energy consumption issues.

The gas leakage source localization algorithms with WSNsare categorized as follows: acoustic signal- and gas diffusionmodel-based localizations. Acoustic signal-based localizationmethod, which uses acoustic signal for localization, providesinstantaneous leakage information. In addition, this methodconsumes a small amount of communication energy due to lowsampling rate [22]. The source location is obtained throughanalyzing and processing the acoustic information of eachnode in the gas leakage area. Ushiku et al. [23] discussedanother type of localization algorithm based on gas diffusionmodel. The gas source location was estimated in a map basedon the specific strength at each observation-point for everycells using a turbulent diffusion model of gas distribution.Such leakage source localization algorithms are realized basedon the detected status of deployed sensor nodes without anyadditional acoustic sensors.

A. Maximum Likelihood Estimation (MLE)

Maximum likelihood estimation (MLE) algorithm uses thedistance between a source and each beacon node to obtain thenonlinear equations according to 2-D space-distance formula.This method approximates the source coordinate using theminimum mean square error (MMSE) estimation method. AsMLE is a centralized method, the detected values from all thenodes must be sent to the fusion center for estimation.

1) Ultrasonic-based localization: At present, many litera-tures implemented some improvements for the localization al-gorithm such as localization accuracy, computational complex-ity, and energy consumption. Masazade et al. [24] proposed anenergy efficient iterative method to improve MLE algorithm.This method reduces the energy consumption in a networkbecause the sensor data is quantized before transmission.Actually, the network uses anchor nodes to obtain the coarselocation estimates, then employs a few additional sensor-datato refine ML estimates through an iterative algorithm withan update in posterior Cramer-Rao lower bound (PCRLB).Since in real applications, MLE is effected by the noise,Liu et al. [25] proposed a model combined with Gaussianand impulse noise model that consider the contamination ofoutliers in these acoustic measurements.

Afterward, a noise-aware MLE [26] was proposed toachieve source localization with the Cramer-Rao bound (CR-B). This model was provided to show how the estimationperformance is improved with a location estimator usingquantized binary data and channel statistics. In addition,the distributed source localization is formulated as a convexfeasibility problem (CFP). Then, consistent and inconsistentCFP for the MLE has been solved by diffusion-based paralleland sequential projection methods [27], respectively. Lowcomplexity and global optimal solution are obtained withoutany fusion center. Furthermore, this non-convex problem wasrelaxed as a convex semi-definite programming (SDP) for abetter estimation with a randomization [28]. Recently, particleswarm optimization (PSO) [29], [30], is extensively usedto solve this above problem. For instance, MLE uses thelight-weight dynamic population in PSO-based grid strategymethod [29] to accurately estimate the source location withreduced energy consumption.

For the multi-source localization, MLE determines thesource-to-sensor distance using acoustic sensors. An acous-tic energy attenuation model was designed to derive CRB.The impact of various deployment strategies was investigatedin [26] for localization accuracy. To overcome the drawbacksof the centralized EM algorithm, Meng et al. [31] proposeddistributed expectation maximization (EM) algorithm. Sinceenergy consumption-based MLE is non-convex, a global so-lution is rarely obtained without a good initial estimates. Atthis point, it is difficult to localize multi-source with lowercomputational complexity. An improved scheme is proposedin [32] that reduces complexity of the algorithm by applyingthe decay factor. Afterward, an efficient sequential dominantsource (SDS) initialization scheme was discussed with anincremental parameterized search scheme for better estima-tion accuracy and lower computation complexity. In addition,



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a) Ultrasonic-based [24-25]b) Gas diffusion-based [34-35]

a) Least squares optimization Ultrasonic-based [40,41] Gas diffusion-based [44,45] Laser-based [42,43]b) TDOA [88-00]

Maximum likelihood estimation (MLE)

Non-linear estimation Trilaterion algorithm Weighted centriod algorithmProjection on convex sets

Others

Gas source localization

a) Direct trilateration algorithm [34]b) Time-of-arrival [50,51]

a) Geometric centriod polygon [38,54]b) Particle filter [54]

a) Semi-definite programming [55, 56]

a) Voronoi diagram [61]b) Bayesian estimation [64]c) Quanrum particle swarm optimization [66]d) Alternating projection algorithm [67]

Fig. 3. Summary of gas source localization algorithms.

Zhang et al. [33] proposed the advanced multiple resolution(MR) alternative method that adopts Tabu heuristic algorithmfor global optimal values in a distributed multiple sourcelocalization.

2) Gas diffusion-based localization: The location estima-tion of a plume source using the MLE algorithm was studiedin [34]. Mitra et al. [35] proposed CH4 source localizationusing MLE, which estimates the related explosion-threat inan indoor environment. It is observed that this localizationmethod estimates more accurate location when a subset nodeis selected. Thus, the error estimation in the above algorithmis satisfactory for various single source points. However,considering the high computational complexity in MLE, areal-time approximated ML estimator (RTAMLE) is discussedin [36] where the diffusion of a gas source is estimated basedon a binary observation. The estimation performance of theRTAMLE is close to MLE with a significant reduction incomputational complexity when the node density tends toinfinity. Levinbook et al. [37] showed an excellent estimationperformance by using MLE and RTAMLE with binary obser-vation made by the active sensor nodes. Thus, this approachis suitable for the real-time processing due to its reducedcomplexity.

Qiuming et al. [38] proposed a novel gas source localizationmethod that combines the weighted centroid algorithm and theparticle filter. The convergence-rate is improved by the properestimation of its initial values. Afterward, Martinez et al. [39]presented a preliminary characterization of the custom windtunnel designed for an experiment on the volatile gas sourcelocalization with a mobile robot. The results conclude that thegas diffusion behavior strongly depends on gas injection rate,position of the mobile robot, and wind flow.

B. Nonlinear Least Squares (LS) Optimization

Nonlinear least squares (LS) optimization estimates thenonlinear static-model parameters based on minimum sum ofsquared error. It uses a sophisticated optimization algorithmto solve the nonlinear location estimation problem.

1) Ultrasonic-based: To estimate the distance between aleakage-source and sensors, Li et al. [40] designed an acousticmicro-sensor array with an energy-decay model. The sourcelocations are realized by the MLE. After that, the energyreadings are compared to solve this nonlinear LS problem.Although, this method is robust against localization errorsand energy-decay factors, is more sensitive to the sensor-gain calibration. A variety of LS optimization using Monte

Carlo simulation is discussed in [41]. The results illustrate thatthe weighted one-step least squares (WOS) algorithm with anoptimal weighting achieves a tradeoff between the esrimationperformance and computational complexity. In addition, theweighted direct LS method with a correction technique canestimate the same location leading to further estimation per-formance gain without any computation of energy-ratios as inconventional nonlinear LS optimization [40].

2) Laser-based: The laser-based algorithms found in theliterature mainly exploit top-down visual attention mechanism(TDVAM) [42] combined with shape analysis to localizegas sources in the large-scale outdoor environments. Mobilerobots capture images at different horizontal angle with anon-board tilted camera. In each image three salient regionsare computed using this TDVAM. One plausible gas source isidentified after the shaping analysis on these salient regions.A laser-range scanner is used to determine the position ofrecognized plausible gas source. After that, a compact surfacerepresentation of gas distribution map [43] was generated withan inspection robot equipped with a 3-D laser-range finder,and gas sensor which returns an integral of the concentrationmeasurements. The state-of-the-art mapping algorithm gives avery accurate estimation of the laser-beam path compared toother previous methods in an open-field environment.

3) Gas diffusion-based: Many researchers used gas diffu-sion model to solve nonlinear LS optimization for the plumesource localization. Such a method is discussed in [44] whichassumes an uniform propagation of the plume in environment.To minimize the least squares error, a two-step approach isdiscussed with a known homogeneous wind field and isotropicdiffusion [45]. The concentration measurement is performedbased on the turbulent diffusion model and the advectionmodel to estimate the source position. Further, Wang et al. [46]performed the nonlinear LS localization experiments withdifferent node distribution and background noise in Gaus-sian plume dispersion model. It is shown by simulation thathigh accuracy is observed at larger node density. Thus, thisapproach could be widely used in environment monitoring.Recently, semi-definite programming (SDP) relaxation is usedto solve the approximate weighted least squares (WLS) usingthe energy-decay model [47]. The accuracy can be furtherimproved with a randomization, however, this approach suffersfrom local minima at higher noise level.



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C. Trilateration AlgorithmTrilateration algorithm, which is an another type of local-

ization algorithms, calculates the location according to thecoordinates of three beacon-nodes and the distance betweenthe beacon-node and the target.

1) Direct trilateration (DT)-based: Kuang et al. [34] s-tudied the direct trilateration (DT)-based source localizationalgorithm that provides better accuracy compared to the M-LE in a plume source localization at low noise level. Tohandle a stronger background noise than conventional DTalgorithm [34], a robust plume source localization algorithmis designed based on an weighted combination in trilaterationalgorithm [48]. Afterward, an effective source localizationalgorithm called equilateral triangular distribution trilaterationalgorithm (ETDT) is proposed in [49] where the beacon nodesare deployed in the equilateral triangles. The ETDT algorithmthat combines trilateration measurement with weighted cen-troid method considers an angular-weighted function to furtherreduce the localization error.

2) Time-of-arrival (TOA): A time-of-arrival (TOA) methodcalculates the distance between nodes according to the signalpropagation time. The trilateration method is used to estimatethe source position with a known signal propagation speed. Asa centralized scheme, this method has low energy efficiencyand low scalability due to the excessive radio transmissions. Inaddition, as the sink node is overloaded with the data traffic inthe centralized scheme, the network lifetime becomes a criticalissue. To prolong the network lifetime, a distributed process-ing is proposed in [50] where many intermediate estimates(IEs) are used in some of the active nodes. Another flexibledistributed method is proposed in [51]. It emphasizes on thedata fusion strategies that are introduced to process the rawand intermediate data, which result in an improved estimationin TOA-based localization.

D. Time difference-of-arrival (TDOA)Time difference-of-arrival (TDOA) method, which locates

the emission signal source by measuring the time lag betweenthe radio signals transmitted to different monitoring centers, isa nonlinear localization method. As relative time is used forlocalization, the time synchronization issue can be relaxed.Wang et al. [52] suggested a localization method based onthe TDOA scheme that considers the non-convex optimizationproblem. Monte-Carlo sampling method is used to achievean approximate global solution of the ML estimation in aline-of-sight (LOS) environment. It is noted that this methodoutperforms several existing methods with the CRB accuracy.A robust source localization with TDOA method in presenceof errors was proposed by Yang et al. [53]. In particular,this method analyzes both second-order cone programming(SOCP) relaxation and alternative semi-definite program (S-DP) relaxation. These computationally efficient convex relax-ation methods provide optimal performance predicted by theCRLB.

E. Weighted Centriod AlgorithmThe geometric centroid of polygon is treated as a source

localization by the weighted centriod algorithm. The polygon

is the overlapping area of the beacons where the unknownnodes are within the scope of communications. This algorithmis designed to be simple, however, is not widely used due tolow estimation accuracy. In addition, this algorithm cannotaccurately estimate the source location with the nonlineargas diffusion model in a windy condition. Qiuming et al. [54]proposed a localization method that combines the advantageof particle filter with the weighted centroid algorithm. Animproved position estimation is observed by the rigoroussimulation results.

F. Projection on Convex Sets (POCS)

A distributed source localization method is proposed basedon convex sets. Assume that the communication link betweenthe nodes as a geometric constraint of the node location, wholenetwork is modeled as a convex set. Thus, the localizationproblem is converted into a convex-constrained optimizationproblem which can be solved with a projection on convexsets (POCS) method. The algorithm uses linear programmingand SDP method to obtain a global optimized solution for thesource location. The anchor nodes continuously update theposition based on circles and lines according to the energy-ratio.

The POCS method is widely used for consistent as well asinconsistent source localization. Then, a diffusion-based pro-jection method is applied to the distributed source localizationas a convex feasibility problem. Since full data-set from eachnode is not required for processing, the algorithm has fewcomputation and communication cost compared to weightedleast squares method [55]. When this algorithm solves a localoptima and saddle points in the convex feasibility problem,estimation performance is better than MLE [56]. Thus, anunique solution to true source location is obtained based onintersection of convex sets, however, assuming infinite sampleswithout any measurement noise.

G. Others

Apart from the above-mentioned algorithms, there are a fewother source localization methods as follows. For instance, Hoet al. [57] discussed an accurate algebraic closed-form solutionfor source localization using the acoustic-energy model. Itreaches the CRLB accuracy under Gaussian noise. To handlenon-Gaussian noise, a sequential method based on acoustic-energy attenuation model with particle filter was discussedin [58]. It tracks the unknown number of the targets. Inaddition, this method dramatically reduces the computationalcomplexity with an improved localization accuracy. It is truethat the beamforming algorithm with an acoustic wave pro-vides better accuracy in relatively sparse network, however,needs high energy requirement [59]. Kim et al. [60] designeda functional quantizer to minimize the localization error bytransmitting the quantized acoustic sensor data to a fusioncenter. To reduce the complexity as well as to improve theaccuracy, You et al. [61] proposed a distributed algorithm witha Voronoi diagram that reduces the size of estimation area witha Voting-grid to achieve the essential event region (EER) withless complexity.



8

TABLE IICOMPARISON BETWEEN SOURCE LOCALIZATION ALGORITHMS

Algorithms Applicationscope

Localizationaccuracy

Computationalcomplexity Advantages Disadvantages

Maximum likelihoodestimation (MLE)[24]–[26]

Small-scale Low General Multiple source localization androbust in noisy environment

Poor scalability, largecommunication requirement, andsensitive to parameter perturbation

Nonlinear leastsquares optimization[40], [41]

All High General Better precision than MLE at lownoise power

Limited to single source estimationand low localization accuracy athigher noise levels

Direct trilateration(DT) [34], [49] Large-scale Low Low Very low hardware requirements Large localization error

Timedifference-of-arrival(TDOA) [52], [53]

Small-scale High Low Time synchronization is notrequired

Multipath effect and limited byultrasonic propagation distance

Time-of-arrival (TOA)[50], [51] Small-scale High Low High estimation accuracy with

few sensor nodesTime synchronization is requiredand multipath effect

Weighted centriod[54] All Low Low Low communication overhead Localization accuracy depends on

node density and distributionsProjection on convexsets (POCS) [55], [56] All High General Low hardware requirement Local optimum, localization failure,

and high energy consumption

An data compression and sensor selection are performed toselect the most informative sensors before the communicationin an iterative Monte-Carlo source localization [62]. Thismethod significantly reduces the communication overheaddue to message-exchange between nodes. Yong et al. [63]proposed the distributed sequential minimum mean squarederror (MMSE) estimation with node scheduling and nodecooperation for a random gas diffusive source localization.This method provides an advantage in terms of energy con-sumption and communication latency issues. This method isfurther improved by the Bayesian estimation in the schemeproposed in [64]. First, the physical and statistical measure-ment models of the substance dispersion are derived by solvingthe diffusion equations. Then, this model is integrated intodistributed processing techniques. At last, the optimal nodesare selected to meet the low communication overhead andaccurate estimation. Meanwhile, two parametric belief repre-sentation methods, which are suitable for various source typesin different environments, are proposed for the distributedprocessing.

Taylor expansion is used in [65] to design a linear model.Then, the best linear unbiased estimator (BLUE) is employedto estimate gas leakage position. This method obtained moreaccurate localization through many iterations based on theGaussian model and the BLUE. Based on the attenuationmodel of a plume source, Liao et al. [66] applied a quantumparticle swarm optimization (QPSO) to solve the plume sourcelocalization problem in windy condition. According to thefact that the plume source only can be detected when thesensor measured concentration is larger than a threshold,the sensors exert the virtual forces to affect the updatedposition of each particle in QPSO based on the force-directedheuristics. Further, this method makes the particles to roammore purposive, which directs the updating of particles toimprove the convergence speed.

For multiple gas source detection and localization, an al-ternating projection (AP) algorithm was considered when the

advection and diffusion change with time [67]. The complex-ity, localization performance, and cost function are analyzedin a distributed sensor networks. However, the performance isonly similar to that of ML estimator in some cases. A lumped-parameter state-space model for the advection diffusion ofatmospheric gas concentration is proposed in [68]. The two-tiered detection strategy, dynamic sensor selection, and athreshold-based approach are used to extend the networklifetime with an increase in active sources. The mean andcovariance data are provided to Kalman filters to accuratelyestimate the source location. To simultaneously detect andlocalize multiple sources, Kalman filter [69] is extensivelyused. To decrease localization error, a greedy approach issuggested to iteratively detect the potential sources based onthe measurement of sensors.

A qualitative comparison of the above-mentioned sourcelocalization algorithms is provided in Table II. In general,these algorithms are categorized into centralized and dis-tributed source localization algorithms. Centralized algorithmprovides better estimation, however, energy consumption ismore compared to the distributed algorithms. Based on theabove discussion, we have reached the following conclusions:

1) Since autonomous mobile robot can move towards thetarget as close as possible to obtain more accurate mea-surements, localization estimation is better than staticnode-based WSNs. In addition, measurement precisionincreases in higher node density, however, increasing thenumber of sensor nodes results in higher deploymentcost.

2) Most of localization algorithms are gas diffusion model-specific, thus the localization error varies with diffusionmodel.

Although the aforementioned studies provide a usefuloverview on source localization methods, these algorithms arestill in their infancy and should be carefully redesigned withhigh accuracy as well as low energy consumption.



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Fig. 4. (a) The boundary of the continuous objects is estimated by theboundary nodes. The area covered by the boundary nodes is reported to thesink. (b) However, the continuous objects are diffused in a wide region withnon-uniform diffusion velocity and acceleration according to the surroundings.In addition, these objects also dynamically change their shape and size, whichmake it more difficult than an individual object tracking.

IV. BOUNDARY DETECTION AND TRACKING IN WSNS

Continuous objects such as wild fire, radio-active contami-nation, toxic gas leakage, and hazardous biochemical materialare diffused in a wide region with non-uniform diffusionvelocity and acceleration according to the surroundings. Theseobjects also dynamically change their shape and size. Thustracking of continuous objects is more difficult than an individ-ual object tracking. To overcome this problem, many methodsare designed with reduced energy consumption and higheraccuracy in estimation as discussed next.

A. Dynamic Cluster-based Algorithms

A dynamic cluster structure for object detection and tracking(DCSODT) [70] is one of the widely used approaches. Thereare two important phases in DCSODT as follows: collab-orative data management and object localization reporting.A node that contains one of more adjacent normal nodes iscalled as a reporter. According to the location of the object,all reporters are organized into cluster in a distributed manner.Cluster-head (CH) aggregates the collected data in a cluster,and broadcasts the information to the sink using a geographicrouting method. Then, the target boundary is estimated bythe sink. The CH selection process is simple since a node,which is close to cluster center, is always selected as a CH.

However, this type of CH selection process yields trafficoverhead when the number of reporters increases. In addition,this method does not consider the power consumption issue ofthe reporter. Han et al. [71] proposed a mobile anchor-assistedlocalization algorithm based on regular hexagon (MAALRH),which covers the whole monitoring area with a boundarycompensation method. The unknown nodes calculate theirpositions by the trilateration method. It is shown by simulationthat the MAALRH achieves high estimation accuracy when thecommunication range is within the trajectory resolution.

A distributed, light-weight, and dynamic clustering algorith-m is proposed by Chen et al. [72]. A node, which is close to thetarget, is selected as CH using the Voronoi diagram. When theacoustic signal strength exceeds a pre-determined threshold, anactive CH broadcasts the information about solicitation packetaccording to the two-phase broadcast mechanism. After that,the CK requests nodes to join its cluster for the boundaryestimation. However, this method is limited by the hardwareresource the node. To relax the hardware constraint, Yanget al. [73] proposed a multi-sink architecture which consistsof a static sink and a mobile sink guided by geographicinformation system (GIS). The mobile sink calculates theoptimized position and collects the information from boundarynodes with less energy consumption. This sink also movesto a adaptive position relative to the static sink. Then, thestatic sink reduces data transmission as well as energy con-sumption based on the centroid algorithm. Wang et al. [74]proposed a task allocation algorithm based on score incentivemechanism (TASIM) with an reward or punish policy. CHsare responsible for task allocation and score calculation. Theuncompleted tasks on failed nodes can be timely migrated toother cluster members for further execution. In addition, theuncompleted tasks on death nodes are reallocated by CHs. Theperformance of the TASIM is better than conventional taskallocation algorithms in terms of both network-load balanceand energy consumption. It is observed by simulation thatthe energy consumption is reduced upto 30% compared toprevious methods.

Continuous object detection and tracking algorithm (CO-DA) [75] allows each sensor node within the scope of sensingto detect and track moving continuous object. In each cluster,any sensor that is near to the detected object directly sendthe status to the CH. After receiving the information, theCH performs local boundary estimation to determine theboundary of the continuous object within the scope of cluster.Then, a dynamic cluster sends the continuous target boundaryinformation to the sink. Since the number of boundary nodesis reduced, communication cost is minimized. However, thisprocess leads to cluster construction and maintenance overheadbecause the clusters are proactively formed to wait for acontinuous object. Furthermore, a convex-hull algorithm isused to detect boundary of a continuous object to overcome thelimitation of CODA on the detection of convex forms of con-tinuous objects [76]. The broadcasting strategy is similar to thesingle target tracking (SAT) algorithm. Most importantly, thisalgorithm prolongs the network lifetime and achieves a goodbalance between tracking accuracy and energy consumption.However, this algorithm assumes uniformly distributed nodes



10

in homogeneous WSNs which sometime is far away from prac-tical application. Another scheme is proposed termed as staticcluster and dynamic cluster head mechanism (SCDCH) [77]to forwards the data collected from active node to the CHwithout changing cluster border within the network lifetime.

B. The Topology Control and Routing Protocol

Topology control protocol is found to be an interestingextension to dynamic cluster-based algorithms. For example,Tu et al. [78] proposed scalable continuous object detectionand tracking based on a cluster-based approach (DCSODT),which has two phases as collaborative data processing andobject location reporting. These phases determine whether anevent node is helpful to estimate the position of the object ina collaborative data processing stage. If the local informationof an event node is not useful for locating the object, theopportunity for this node to be selected as a reporter isvery less. Chintalapudi et al. [79] proposed three qualitativelydifferent approaches to select boundary nodes for localizedboundary. A boundary node is defined as the sensor node thatdetects the object in its sensing area, however, has one ormore one-hop neighbors that do not detect the same object.The statistical approach is used to gather data from the nodesin probing neighborhood and to determine the boundary node.Image processing techniques are used for an edge detection.Also, a classifier-based approach is used to partition the similardata gathered from its neighbor in a classifier, thereafter,solves the false detection problem. A tradeoff between energyconsumption and estimation accuracy is observed. However,this method is not capable to handle a random distributionthat also changes with time in the networks

For a moving phenomenon over the area, real-time edgetracking is difficult. Liu et al. [80] discussed a distribut-ed position-based adaptive quantization scheme to choose athresholds of the quantizer. Each sensor node dynamicallyadjusts its quantization threshold, then sends its one-bit quan-tized version of the original observation to a fusion center.The signal intensity received at local sensors is modeled as anisotropic signal intensity attenuation model. Numerical resultsshow that the position-based MLE is more accurate thanthe classical fixed-quantization MLE and the position-basedCRLB is less than its fixed-quantization-based CRLB.

Zhang et al. [81], proposed two novel algorithms for bound-ary detection where only one-hop information is available.Two novel computational geometric techniques as localizedVoronoi polygon (LVP) and neighbor embracing polygons(NEP) are developed. The LVP algorithm detects all theboundary nodes, and gives perfect estimation on coverageboundaries with low energy consumption. Most importantly,these algorithms are topology independent.

C. Continuous Target Detection and Tracking Mechanism

An energy-efficient algorithm is proposed for continuoustarget detection [82].The proper selection of monitoring nodessignificantly reduces the size of reporting messages. However,the method ignores the energy consumption due to the com-munication between representative nodes and boundary nodes.

Hong et al. [83] focused on the nodes-state scheduling andused the minimum set of active sensing node to obtain a pre-dictive continuous object tracking scheme before an acceptabletracking accuracy. The scheme predicts the next boundary bymeasuring the diffusion speed and direction of the currentboundary line. This process suggests a wake-up mechanismto decide which sleeping nodes need to be activated forfuture tracking. Adjusting the sensing range of active nodesusing boundary node identification mechanism (BNIM) [84]is another way to save communication energy. An accurateboundary estimation is possible by controlling the sensingrange of the active nodes. Another wake-up mechanism forfuture boundary tracking is proposed in [85]. This methodminimizes the number of boundary nodes, however, preciseboundary estimation is less possible due to low computingability of these boundary nodes. A grid-based asynchronous s-elective wake-up protocol is discussed in [86]. This simple andasynchronous protocol is suitable for WSNs, however, resultsinaccurate estimation. Based on the cluster-based structure,a selective wake-up scheme is presented in [87]. Basically,this method forecasts the next location by the selective wake-up scheme with grid-based clusters. Such a technique doesnot require any complex computations on massive data. Mostimportantly, this asynchronous algorithm is suitable for real-time WSNs.

To reduce the energy consumption through decreasing re-dundant information and communications, Park et al. [88]considered a two-tier grid structure that relies on locationinformation of beacon node and a grid cell size value whenthe nodes are aware of their locations to achieve flexibleand reliable detection as well as tracking. A coarse-grainedgrid structure for flexibility is constructed proactively, and thefine-grained grid structures of minute grid cells provide thedetailed boundary shape of the continuous object for reliabilityaccording to the continuous objects movement or alteration. Itreduces the amount of data from boundary nodes to the sinks toget better performance. Peng et al. [97] developed a distributedmultiple-sensor cooperative turbo coding (DMSCTC) schemefor a large-scale WSN with sensor grouped in a cooperativecluster. Along with a simple forward error correction (FEC)coding in each sensor nodes, the CH performs a simple multi-sensor joint coding. The complicated joint iterative decodingis implemented only at the data collector. The WSNs achievethe target error performance with less power consumption,thus prolong its lifetime. The analytical and simulation resultsshow that the DMSCTC can substantially improve the energyefficiency of the clustered WSN. Hong et al. [89] presented asmall set of candidates using neighbors descriptor table (NDT).The candidates provide a well-balanced representative nodesselected from the small set that is independent of node density.Another method is discussed in [90] where the sensing fieldis divided into cells like the TV pixels. The sampling andreporting time are estimated based on the pixel density. Theboundary traverse algorithm (BTA) in space domain is used toobtain the boundary information. The redundant informationis reduced with the help of a sampling method based on thevirtual-grid in the static cluster-based WSNs.

Chen et al. proposed an energy-efficient boundary estima-



11

TABLE IIICOMPARISON BETWEEN BOUNDARY DETECTION AND TRACKING ALGORITHMS IN WSNS

Algorithms Key Issues Solution Advantages Disadvantages

Dyn

amic

clus

ter-

base

dal

gori

thm

Ji et al. [70] Dynamic infrastructure A dynamic cluster-basedstructure

The reduction ofcommunication cost

Not adjust adaptively the sizeof clusters. Low accuracyand the lack of scalability

Chen et al. [72] Only one CH consideringcommunication collision Voronoi diagram Better quality data collected

and less collision incurred

No routing protocol, energyconsumption of CH is notconsidered

Yang et al. [73] Reduced energy consumption Multi-sink architecture Less energy consumption Not precise enough withmovement of the mobile sink

Chang etal. [75]

Precise boundary tracking andreduced energy consumption

A hybrid static-and-dynamicclustering mechanism Less energy consumption Does not provide boundary

detection of convex forms

Xu et al. [76] Heavy data traffic and timeconsumption

Scheduling with adaptive nodeselection

Tradeoff between accuracyand energy consumption

Consider only uniform nodedistribution

Rou

ting

and

topo

logy Tu et al. [78] The number of reporters A scalable, topology

control-based protocol Reduces number of reporters Sleep scheduling

Chintalapudi[79] Edge node detection Three edge detection approaches Generates the phenomenon

edgesNot consider the objects thatrandomly change their shape

Zhang et al.[81] Boundary node detection A deterministic method based

on localized Voronoi polygons

Coverage boundary wasidentified correctly, applied toarbitrary topology

Low precision due toone-hop information

Con

tinuo

usTa

rget

Det

ectio

nan

dTr

acki

ngM

echa

nism

Kim et al. [82] Selection of boundary node set Energy-efficient algorithm formoving phenomena boundaries

Reduces the data trafficbetween nodes

Ignores the communicationbetween representative nodesand boundary nodes

Hong et al. [83] Sensor state scheduling Using minimum set of activenodes and wake-up mechanism

Dramatically reduces theenergy consumption

Not precise predictiveboundary line

Jin et al. [84] Selection of boundary node set Adjusting sensing range,representative node selection

Size of the communicationand report message is reduced

Not precise boundaryestimation

Hong et al. [85] Scheduling the sensor state The future boundary line andthe wake-up mechanism

Reduces the energyconsumption

Boundary estimation is notaccurate

Park et al. [86] An asynchronous predictionprotocol

A grid-based asynchronousselective wakeup protocol Simple asynchronous protocol Inaccurate boundary

estimation

Lee et al. [87] An asynchronous method An asynchronous selectivewakeup scheme

Simple computation andaccuracy tracking

Prediction accuracy becomeslower with faster target speed

Park et al. [88] Flexible and reliable detectionand tracking

The coarse-and fine-grained gridstructures

Reduces data from boundarynodes to the sinks

Ignores the long-rangetransmission

Hong et al. [89] Selection of a small set ofcandidates

Selection of representativeboundary node

Reduces a large amount ofdata

Small set of representativenodes sometime results inreduced accuracy.

Kim et al. [90] Reducing the redundancy,adaptive sampling scheme

Diffusion area is divided intoseveral cells with virtual grid

Reduces the number ofboundary nodes as well asredundant boundaryinformation

The diffusion velocitydirectly affect the boundarytracking accuracy

Chen et al. [91] Monitoring unsmoothedcontinuous object

Selecting a minimum set ofrepresentative nodes

Simple and efficient selectionprocess Low boundary accuracy

Ding et al. [92] Faulty sensor identification Fault-tolerant event boundarydetection

High accuracy and a low falsealarm rate

Depends on sensor faultprobability.

Liao et al. [93] The high noise environment Data fusion and decision fusionfor edge node detection

Low computationalcomplexity andcommunication cost

Short tolerance range

Sun et al. [94] Tracking with uncovered holes Directional antenna Uncovered holes is consideredwith less energy consumption Low estimation accuracy

Hong et al. [95] Void area problem Selective wakeup scheme More accurate trackingPays more attention toestimation accuracy thanenergy efficiency

Lee et al. [96] Multiple objects tracking withchanging shape

Dynamic rectangular zone-basedmechanism

Minimizes the energyconsumption in datacollection

Low accuracy due todiffusion speed

tion for unsmoothed continuous object called as EUCOW [91]that considers the Voronoi-based network to simplify nodeselection in the WSNs. This algorithm monitors both interiorand exterior boundary of unsmoothed object by selecting onlythe nodes that are within transmission range and far from eventboundary, respectively. A minimum set of representative nodes

near event boundary is reported to the sink. To decrease thefalse alarm-rate, faulty node identification is most essential. Aboundary detection algorithm with fault-event disambiguationis proposed to detect several faulty nodes [92]. However,this algorithm is highly sensitive to the threshold settings.Two distributed approaches with composite hypothesis test



12

in WSNs are developed in [93]. One-level decision methodis used to collect and process the data from its neighborswith a low false-alarm probability, whereas the two-leveldecision method determines the edge nodes with reduced thefalse alarm-rate of local decision. The algorithm has a lowcomputational complexity as well as low power consumptionsdue to message exchange among nodes. It is important tonote that the detailed information about the whole continuousobject is required for rescue operations to estimate the natureof the damage. However, none of these above methods discussin-depth research on this issue, hence, more study into thisdirection is needed in the future.

Since most of the above methods do not able to estimatenear void area, which is a serious issue in uniformly deployedWSNs, the messages sometimes are not able to reach to thesink. To overcome this problem, the directional antenna isused to tracking continuous objects in void area [94]. Honget al. [95] proposed a selective wake-up scheme for smart-cluster continuous object tracking protocol (SCOP) with avoid area around the boundary of the objects. Since thedata collection with a void-area is more important in such asituation, SCOP pays attention to the estimation accuracy thanenergy efficiency when compared with the other schemes.

For multiple continuous objects tracking, dynamic rectanglezone-based collaboration mechanism is introduced accordingto the dynamic change of objects [96]. Basically, this methodcontains three phases as: 1) selection and collection of sens-ing data, and construction of a dynamic rectangle zone, 2)reselection of the node and reconfiguration of the dynamicrectangular zone based on the diffusion of continuous object,and 3) construction of an updated dynamic rectangular zonewhen continuous objects are merged together. In addition,the split behavior of the continuous object is discussed in[98]. The corresponding center nodes in the rectangular zoneare updated when the continuous objects split into severalcontinuous areas. Moreover, the total energy consumptionissue is discussed in [99]. Table III provides a qualitativecomparison of the existing boundary detection and trackingalgorithms for continuous objects in WSNs.

Current research efforts have provided a good understandingof the boundary tracking of continuous object. However,studies on time synchronization, void-area problem, 3-D spacetracking, and robust interference-aware routing protocol arelimited in the complex environments. Hence, more in-depthresearch in these issues is expected in the future.

V. CONCLUSION

This survey provides a comprehensive overview of theexisting and emerging work on gas leakage source detectionand tracking of continuous objects with WSNs. We havehighlighted the inherent features of the various well-known gasdiffusion models used in localization and tracking algorithms.With the advancement in sensing technologies, the gas sourcelocalization techniques are discussed from the view of preci-sion, robustness, and energy consumption issues. In addition,we also categorized the state-of-the-art algorithms to estimatethe boundary of the continuous objects that change their shapeand size with time.

Although a significant amount of research has been carriedout on source localization and boundary tracking of continuousobjects, there are still many issues to be addressed as follows:• As the detection characteristic of nodes directly affect the

localization and tracking estimation, the choice of sensoris much more crucial than the choice of the optimizationalgorithms in real environment.

• Multiple gas source localization is less discussed in theexisting literature. The current approaches may be furtherexplored for robust multi-source identification under thenoise due to hardware impairments, interference, andchannel estimation errors.

• Since turbulence effects, obstacles, and wind speed great-ly influence the localization accuracy, these are the mainissues to be properly addressed in future. For moreaccurate localization, gas diffusion models need to becombined with gas temperature that strongly depends onlarge-scale temperature fluctuations.

• In the context of energy-efficient localization and bound-ary detection algorithms, redundant nodes generate amassive amount of data transmission, which also reducesthe network efficiency. An algorithm should be designedbased on data fusion technology to solve this problem.

• Considering the fact that the research on continuousobject detection and tracking in a 3-D space is in anearly stage under strong assumptions, further studies areneeded to handle these issues in the 3-D space.

As localization of continuous object and boundary trackingbecome serious issue in large-scale industrial area as wellas environment, in-depth research on the development oflocalization and tracking technology is expected to becomefundamental task with several new problems to solve andchallenges to overcome in factory automation, fault diagnosis,surveillance, and gas consumption monitoring systems.

VI. ACKNOWLEDGEMENT

This work was supported by the NSFC under grant61572262, the NSF of Jiangsu Province under grantBK20141427, the 2014 Pearl River S&T Nova Programof Guangzhou under grant 2014J2200023, the 2014 Natu-ral Science Foundation of Guangdong Province under grant2014A030313685, the 2014 Shenzhen City Knowledge Inno-vation Program Project under grant JCYJ20140417113430604,and the Open Project Foundation of Information TechnologyResearch Base of Civil Aviation Administration of Chinaunder grant CAAC-ITRB-201406. Mithun Mukherjee is thecorresponding author.

REFERENCES

[1] F. Chraim, Y. Erol, and K. Pister, “Wireless gas leak detection andlocalization,” IEEE Trans. Ind. Inf., pp. 1–13, 2015.

[2] G. T. Nofsinger, “Tracking based plume detection,” Ph.D. dissertation,Dartmouth College, 2006.

[3] J. A. Farrell, S. Pang, and W. Li, “Plume mapping via hidden markovmethods,” IEEE Trans. Syst., Man, Cybern. B: Cybern., vol. 33, no. 6,pp. 850–863, Dec. 2003.

[4] D. Zarzhitsky, D. F. Spears, and W. M. Spears, “Distributed roboticsapproach to chemical plume tracing,” in Proc. IEEE/RSJ Conf. Intell.Robots and Syst. (IROS), Canada, Aug. 2005, pp. 4034–4039.



13

[5] N. Kh. Arystanbekova, “Application of gaussian plume models forair pollution simulation at instantaneous emissions,” Mathematics andComputers in Simulation, vol. 67, no. 4–5, pp. 451–458, Dec. 2004.

[6] Y. Dongfen, Y. Qiaolong, and L. Weichen, “A calculation approachand implementation of hazard chemical substance based on Gaussiandiffusion model,” Computers and Applied Chemistry, vol. 29, no. 2, pp.195–199, 2012.

[7] C. W. Xianjin Chen, Huan Qi, “Simulation of release and dispersion ofgas in sealed interspace,” Computer simulation, vol. 2, pp. 24–27, 2003.

[8] S. R. Hanna, J. C. Chang, and D. G. Strimaitis, “Hazardous gas modelevaluation with field observations,” Atmospheric Environment. Part A.General Topics, vol. 27, no. 15, pp. 2265–2285, Oct. 1993.

[9] J. S. Elkinton, R. T. Carde, and C. J. Mason, “Evaluation of time-average dispersion models for estimating pheromone concentration ina deciduous forest,” J. Chem. Ecol., vol. 10, no. 7, pp. 1081–1108, Jul.1984.

[10] L. W. Chen, J. H. Yang, L. Sun, and M. M. Shi, “Odor sourcelocalization algorithm based on spatially distributed sensors array,” J.University of Electronic Science and Technology of China, vol. 43, no. 2,pp. 212–215, 2014.

[11] L. Fan, S. Yi, Z. Li, and B. Yang, “Research on toxic gas diffusionsimulation in urban areas based on GIS,” in IEEE Int. Conf. New Trendsin Inform. and Service Science (NISS), Beijing, China, June 2009, pp.166–169.

[12] R. Briant, C. Seigneur, M. Gadrat, and C. Bugajny, “Evaluation of road-way gaussian plume models with large-scale measurement campaigns,”Geoscientific Model Development, vol. 6, no. 2, pp. 445–456, Apr. 2013.

[13] B. Ristic, A. Gunatilaka, and R. Gailis, “Achievable accuracy in param-eter estimation of a gaussian plume dispersion model,” in Proc. IEEEWorkshop on Statistical Signal Process. (SSP), Australia, June/July 2014,pp. 209–212.

[14] U. Konda, T. Singh, P. Singla, and P. Scott, “Uncertainty propagation inpuff-based dispersion models using polynomial chaos,” EnvironmentalModelling & amp; Software, vol. 25, no. 12, pp. 1608–1618, Dec. 2010.

[15] Y. Shanliang, Z. Xinye, Y. Mei, and Z. Yun, “Interactive methanemonitoring system modeling and simulation for coal mines,” in Proc.IEEE Int. Conf. Meas., Inform. and Control (ICMIC), Harbin, Aug.2013, pp. 772–777.

[16] J. Wan, J. Sui, and H. Yu, “Research on evacuation in the subwaystation in China based on the combined social force model,” PhysicaA Statistical Mechanics and its Applications, vol. 394, pp. 33–46, Jan.2014.

[17] F. Septier, A. Carmi, and S. Godsill, “Tracking of multiple contaminantclouds,” in Proc. IEEE Int. Conf. Inform. Fusion (FUSION), Seattle,WA, July 2009, pp. 1280–1287.

[18] H. E. Jia, A. G. Xuan, W. U. Yuan-Xin, Z. G. Yan, and Y. Pan, “Modelof drain diffusion of chloroethylene,” J. Wuhan Institute of Technology,vol. 31, pp. 1–4, Dec. 2009.

[19] Y. Hu, J. Jiang, and D. Li, “3D simulation of leakage and diffusionof gas in complex and sealed space,” in Proc. IEEE Int. Conf. IntellComput. Technol. and Autom. (ICICTA), Shenzhen, Guangdong, Mar.2011, pp. 743–746.

[20] L. S. Liwei Chen, Jianhua Yang and M. Shi, “Odor source localizationmethods based on wireless sensor network,” Ph.D. dissertation, TianjinUniversity, 2009.

[21] X. Xiao, “Research on gas source localization based on wireless sensornetworks,” Ph.D. dissertation, Dongbei University, 2013.

[22] X. Sheng and Y.-H. Hu, “Maximum likelihood multiple-source localiza-tion using acoustic energy measurements with wireless sensor networks,”IEEE Trans. Signal Process., vol. 53, no. 1, pp. 44–53, Jan. 2005.

[23] T. Ushiku, N. Satoh, H. Ishida, and S. Toyama, “Estimation of gas-source location using gas sensors and ultrasonic anemometer,” in Proc.IEEE Conf. on Sensors, Korea, Oct. 2006, pp. 420–423.

[24] E. Masazade, R. Niu, P. K. Varshney, and M. Keskinoz, “An energyefficient iterative method for source localization in wireless sensornetworks,” in Proc. Annual Conference on Information Sciences andSystems (CISS), Baltimore, MD, Mar. 2009, pp. 623–628.

[25] Y. Liu, Y. H. Hu, and Q. Pan, “Robust maximum likelihood acousticsource localization in wireless sensor networks,” in Proc. IEEE GLOBE-COM, Honolulu, Nov. 2009, pp. 1–6.

[26] F. Xu, Z. Li, and W. Li, “Acoustic source localization in noisy wirelesssensor networks,” in Proc. IET Int. Conf. Wireless, Mobile and Multi-media Netw. (ICWMMN), Beijing, China, Oct. 2008, pp. 41–44.

[27] W. Meng, W. Xiao, L. Xie, and A. Pandharipande, “Diffusion basedprojection method for distributed source localization in wireless sensornetworks,” in Proc. IEEE Conf. Computer Communications Workshops(INFOCOM Workshop), Shanghai, China, Apr. 2011, pp. 537–542.

[28] G. Wang and K. Yang, “Efficient semidefinite relaxation for energy-based source localization in sensor networks,” in Proc. IEEE Int. Conf.Acoustics, Speech and Signal Process. (ICASSP), Taipei, Taiwan, Apr.2009, pp. 2257–2260.

[29] Y. Liu, W.-T. Zhou, Y. Liang, Q. Pan, and Y.-M. Cheng, “A novel PSObased acoustic source localization algorithm in wireless sensor network,”in Proc. World Congress on Intell. Control and Autom. (WCICA), Jinan,China, July 2010, pp. 820–825.

[30] Y. Huang, C. Wu, Y. Zhang, and J. Zhang, “Source localization basedon particle swarm optimization for wireless sensor network,” in Proc.IEEE Int. Conf. Progress in Inform. and Computing (PIC), Shanghai,China, Dec. 2010, pp. 495–498.

[31] W. Meng, W. Xiao, and C. Wu, “Distributed energy-based multi-sourcelocalization in wireless sensor network,” in Proc. IEEE Int. Conf.Systems, Man and Cybernetics (SMC), Singapore, Oct. 2008, pp. 2950–2954.

[32] W. Meng, W. Xiao, and L. Xie, “An efficient EM algorithm for energy-based multisource localization in wireless sensor networks,” IEEE Trans.Instrum. Meas., vol. 60, no. 3, pp. 1017–1027, Mar. 2011.

[33] Y. Zhang, D. Xue, C. Wu, and W. Meng, “A new algorithm for multiple-source localization based on acoustic energy in wieless sensor networks,”in Proc. Int. Conf. Ind. Mechatronics and Autom. (ICIMA), Chengdu,China, May 2009, pp. 360–363.

[34] X. Kuang and H. Shao, “Study of the two plume source localizationalgorithms based on WSN,” Chinese J. Scientific Instrument, vol. 28,no. 2, pp. 298–303, 2007.

[35] S. Mitra, S. P. Duttagupta, K. Tuckley, and S. Ekram, “Wireless sensornetwork based localization and threat estimation of hazardous landfillgas source,” in Proc. IEEE Int. Conf. Ind. Technol. (ICIT), Athens, Mar.2012, pp. 349–355.

[36] S. Vijayakumaran, Y. Levinbook, and T. F. Wong, “Maximum likelihoodlocalization of a diffusive point source using binary observations,” IEEETrans. Signal Process., vol. 55, no. 2, pp. 665–676, Feb. 2007.

[37] Y. Levinbook and T. F. Wong, “Maximum likelihood diffusive sourcelocalization based on binary observations,” in Proc. Asilomar Conf.Signals, Sys. and Computers,, California, Nov. 2004, pp. 1008–1012.

[38] L. Qiuming, L. Zhigang, W. Jinkuan, and X. Xianda, “A gas sourcelocalization algorithm based on wireless sensor network,” in IntelligentControl and Automation (WCICA), 2014 11th World Congress on,Shenyang, Jun. 2014, pp. 2514–2518.

[39] D. Martinez, E. Clotet, M. Tresanchez, J. Moreno, J. M. Jimenez-Soto,R. Magrans, S. Marco, and J. Palacin, “First characterization resultsobtained in a wind tunnel designed for indoor gas source detection,” inProc. IEEE Int. Conf. Advanced Robotics (ICAR), Istanbul, pp. 629–634.

[40] D. Li and Y. H. Hu, “Energy-based collaborative source localizationusing acoustic microsensor array,” EURASIP J. Appl. Signal Process.,vol. 4, no. 4, pp. 321–337, 2003.

[41] C. Meesookho, U. Mitra, and S. Narayanan, “On energy-based acousticsource localization for sensor networks,” IEEE Trans. Signal Process.,vol. 56, no. 1, pp. 365–377, Jan. 2008.

[42] P. Jiang, Q.-H. Meng, and M. Zeng, “Mobile robot gas source localiza-tion via top-down visual attention mechanism and shape analysis,” inProc. 8th World Congress on Intelligent Control and Autom. (WCICA),Jinan, China, July 2010, pp. 1818–1823.

[43] V. Hernandez Bennetts, E. Schaffernicht, T. Stoyanov, A. J. Lilienthal,and M. Trincavelli, “Robot assisted gas tomography–localizing methaneleaks in outdoor environments,” in Proc. IEEE Int. Conf. Robotics andAutom. (ICRA), Hong Kong, May/June 2014, pp. 6362–6367.

[44] M. P. Michaelides and C. G. Panayiotou, “Plume source positionestimation using sensor networks,” in Proc. IEEE Int. Symp. MediterreanConf. Control and Autom. Intelligent Control, Limassol, June 2005, pp.731–736.

[45] J. Matthes, L. Groll, and H. B. Keller, “Source localization by spatiallydistributed electronic noses for advection and diffusion,” IEEE Trans.Signal Process., vol. 53, no. 5, pp. 1711–1719, May 2005.

[46] H. Wang, Y. Zhou, X. Yang, and L. Wang, “Plume source localizing indifferent distributions and noise types based on WSN,” in Proc. IEEEInt. Conf. Commun. and Mobile Comput. (CMC), Shenzhen, China, Apr.2010, pp. 63–66.

[47] G. Wang, “A semidefinite relaxation method for energy-based sourcelocalization in sensor networks,” IEEE Trans. Veh. Technol., vol. 60,no. 5, pp. 2293–2301, Jun. 2011.

[48] Y. Zhu, “Study of the plume source localization weighted combinationtrilateration based on WSN,” Electronic Meas. Technol., vol. 8, pp. 1–3,2009.

[49] Z. Lina, “Study on gas source localization based on Trilateration inWSN,” Ph.D. dissertation, Heilongjiang University, 2010.



14

[50] W. Qiu and E. Skafidas, “Distributed source localization based on toameasurements in wireless sensor networks,” Res. Let. Signal Proc., vol.2009, pp. 1687–6911, Jan. 2009.

[51] W. Qiu, P. Hao, and E. Skafidas, “Distributed source localization inwireless sensor networks,” in Proc. IEEE Int. Conf. Commun., Circuitsand Systems (ICCCAS), Chengdu, China, July 2010, pp. 90–94.

[52] G. Wang and H. Chen, “An importance sampling method for TDOA-based source localization,” IEEE Trans. Wireless Commun., vol. 10,no. 5, pp. 1560–1568, May 2011.

[53] K. Yang, G. Wang, and Z.-Q. Luo, “Efficient convex relaxation methodsfor robust target localization by a sensor network using time differencesof arrivals,” IEEE Trans. Signal Process., vol. 57, no. 7, pp. 2775–2784,July 2009.

[54] L. Qiuming, L. Zhigang, W. Jinkuan, and X. Xianda, “A gas sourcelocalization algorithm based on wireless sensor network,” in Proc. WorldCongr. Intelligent Control and Autom. (WCICA), Shenyang, China, June2014, pp. 2514–2518.

[55] W. Meng, W. Xiao, L. Xie, and A. Pandharipande, “Diffusion basedprojection method for distributed source localization in wireless sensornetworks,” in Proc. IEEE Int. Conf. Computer Commun. Workshops(INFOCOM Workshop), Shanghai, China, Apr. 2011, pp. 537–542.

[56] D. Blatt and A. O. Hero, “Energy-based sensor network source local-ization via projection onto convex sets,” IEEE Trans. Signal Process.,vol. 54, no. 9, pp. 3614–3619, Sept. 2006.

[57] K. C. Ho and M. Sun, “An accurate algebraic closed-form solutionfor energy-based source localization,” IEEE Trans. Audio Speech Lang.Process., vol. 15, no. 8, pp. 2542–2550, Nov. 2007.

[58] X. Sheng and Y. H. Hu, “Sequential acoustic energy based sourcelocalization using particle filter in a distributed sensor network,” in Proc.IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP),Canada, May 2004, pp. 972–5.

[59] S. Phoha, N. Jacobson, D. Friedlander, and R. Brooks, “Sensor networkbased localization and target tracking through hybridization in the oper-ational domains of beamforming and dynamic space-time clustering,” inProc. IEEE GLOBECOM, San Francisco, USA, Dec. 2003, pp. 2952–2956.

[60] Y. H. Kim, “Functional quantizer design for source localization in sensornetworks,” EURASIP J. Advances in Signal Process., vol. 2013, no. 1,pp. 1–10, Dec. 2013.

[61] Y. You, J. Yoo, and H. Cha, “Event region for effective distributedacoustic source localization in wireless sensor networks,” in Proc. IEEEWCNC, Kowloon, HongKong, Mar. 2007, pp. 2762–2767.

[62] E. Masazade, R. Niu, P. K. Varshney, and M. Keskinoz, “A Monte Carlo-based energy efficient source localization method for wireless sensornetworks,” in Proc. IEEE Int. Workshop Computational Advances inMulti-Sensor Adaptive Process. (CAMSAP), Aruba, Dec. 2009, pp. 364–367.

[63] Z. Yong, M. Qing-Hao, W. Yuxiu, and Z. Ming, “Distributed gas-source localization based on MMSE estimation with cooperative sensornetworks,” in Proc. 31st Chinese Control Conf. (CCC), Hefei, China,July 2012, pp. 6611–6616.

[64] T. Zhao and A. Nehorai, “Distributed sequential bayesian estimationof a diffusive source in wireless sensor networks,” IEEE Trans. SignalProcess., vol. 55, no. 4, pp. 1511–1524, Apr. 2007.

[65] Q. Xiaoxin, “Research of key technology about harmful gas leakagemonitoring based on WSN,” Beijing University of Posts and Telecom-munications, 2012.

[66] H. Liao, Z. Zhuang, S. Huang, and F. Li, “Study of plume sourcelocalization based on force-directed QPSO algorithm,” J. Test and Meas.Technol., vol. 2, p. 010, 2011.

[67] D. Ampeliotis and K. Berberidis, “Low complexity multiple acousticsource localization in sensor networks based on energy measurements,”Signal Process., vol. 90, no. 4, pp. 1300–1312, Apr. 2010.

[68] J. Weimer, B. Sinopoli, and B. Krogh, “Multiple source detectionand localization in advection-diffusion processes using wireless sensornetworks,” in Proc. IEEE Real-Time Systems Symp., Washington, USA,Dec. 2009, pp. 333–342.

[69] B. I. Ying-Wei and G. U. Yuan-Tao, “Study on gas leak source detectionand localization based on greedy approach,” Computer Simulation,vol. 1, no. 1, pp. 286–289, 2014.

[70] X. Ji, H. Zha, J. J. Metzner, and G. Kesidis, “Dynamic cluster structurefor object detection and tracking in wireless Ad-Hoc sensor networks,”in Proc. IEEE ICC, vol. 7, Paris, June 2004, pp. 3807–3811.

[71] G. Han, C. Zhang, J. Lloret, L. Shu, and J. J. Rodrigues, “A mobileanchor assisted localization algorithm based on regular hexagon inwireless sensor networks,” The Scientific World J., vol. 2014, pp. 1–13, July 2014.

[72] W.-P. Chen, J. C. Hou, and L. Sha, “Dynamic clustering for acoustic tar-get tracking in wireless sensor networks,” IEEE Trans. Mobile Comput.,vol. 3, no. 3, pp. 258–271, July/Aug. 2004.

[73] C. Yang, Q. Li, and J. Liu, “A multisink-based continuous object trackingin wireless sensor networks by GIS,” in Proc. IEEE Int. Conf. onAdvanced Commun. Technol. (ICACT), Pyeongchang, South Korea, Feb.2012, pp. 7–11.

[74] F. Wang, G. Han, J. Jiang, W. Li, and L. Shu, “A task allocationalgorithm based on score incentive mechanism for wireless sensornetworks,” Int. J. Distrib. Sensor Netw., pp. 1–12, 2015.

[75] W.-R. Chang, H.-T. Lin, and Z.-Z. Cheng, “CODA: A continuous objectdetection and tracking algorithm for wireless Ad-Hoc sensor networks,”in Proc. IEEE Consumer Communications and Networking Conference(CCNC), Las Vegas, Jan. 2008, pp. 168–174.

[76] Y. Xu, W. Bao, and H. Xu, “An algorithm for continuous object trackingin WSNs,” in Proc. IEEE Int. Conf. Research Challenges in ComputerScience (ICRCCS), Shanghai, China, Dec. 2009, pp. 242–246.

[77] M. A. Wahdan, M. F. Al-Mistarihi, and M. Shurman, “Static clusterand dynamic cluster head (SCDCH) adaptive prediction-based algorithmfor target tracking in wireless sensor networks,” in Proc. IEEE Int.Conv. Inform. and Commun. Technol. Electronics and Microelectronics(MIPRO), Opatija, Croatia, May 2015, pp. 596–600.

[78] S.-C. Tu, G.-Y. Chang, J.-P. Sheu, W. Li, and K.-Y. Hsieh, “Scalablecontinuous object detection and tracking in sensor networks,” J. ParallelDistrib. Comput., vol. 70, no. 3, pp. 212–224, Mar. 2010.

[79] K. K. Chintalapudi and R. Govindan, “Localized edge detection insensor fields,” Ad Hoc Netw., vol. 1, no. 2, pp. 273–291, 2003.

[80] G. Liu, H. Liu, H. Chen, C. Zhou, and L. Shu, “Position-based adaptivequantization for target location estimation in wireless sensor networksusing one-bit data,” Wirel. Commun. Mob. Comput., Feb. 2015.

[81] C. Zhang, Y. Zhang, and Y. Fang, “Localized algorithms for coverageboundary detection in wireless sensor networks,” Wireless Netw., vol. 15,no. 1, pp. 3–20, Jan. 2009.

[82] J.-H. Kim, K.-B. Kim, S. H. Chauhdary, W. Yang, and M.-S. Park,“DEMOCO: Energy-efficient detection and monitoring for continuousobjects in wireless sensor networks,” IEICE Trans. Commun.

[83] S.-W. Hong, S.-K. Noh, H. Ryu, E. Lee, and S.-H. Kim, “A novel con-tinuous object tracking scheme for energy-constrained wireless sensornetworks,” in Proc. IEEE VTC-Fall, Ottawa, Sept. 2010, pp. 1–5.

[84] D. X. Jin, S. H. Chauhdary, X. Ji, Y. Y. Zhang, D. H. Lee, and M. S.Park, “Energy-efficiency continuous object tracking via automaticallyadjusting sensing range in wireless sensor network,” in Proc. IEEE Int.Conf. Comput. Sciences and Convergence Inf. Technol. (ICCIT), Seoul,Korea, Nov. 2009, pp. 122–127.

[85] S.-W. Hong, S.-K. Noh, E. Lee, S. Park, and S.-H. Kim, “Energy-efficient predictive tracking for continuous objects in wireless sensornetworks,” in Proc. IEEE PIMRC, Instanbul, Turkey, Sept. 2010, pp.1725–1730.

[86] H. Park, S. Oh, E. Lee, S. Park, S. H. Kim, and W. Lee, “Selectivewakeup discipline for continuous object tracking in grid-based wirelesssensor networks,” in Proc. IEEE WCNC, Shanghai, China, Apr. 2012,pp. 2179–2184.

[87] W. Lee, Y. Yim, S. Park, J. Lee, H. Park, and S.-H. Kim, “A cluster-based continuous object tracking scheme in wireless sensor networks,”in Proc. IEEE VTC Fall, San Francisco, Sept. 2011, pp. 1–5.

[88] B. Park, S. Park, E. Lee, and S.-H. Kim, “Detection and tracking ofcontinuous objects for flexibility and reliability in sensor networks,” inProc. IEEE ICC, Cape Town, May 2010, pp. 1–6.

[89] S.-W. Hong, E. Lee, H.-Y. Ryu, and S.-H. Kim, “Energy-efficientboundary monitoring for large-scale continuous objects,” IEICE Trans.Commun., vol. E95.B, no. 7, pp. 2451–2454, July 2012.

[90] W. Kim, H. Park, J. Lee, and S. H. Kim, “Efficient continuous objecttracking with virtual grid in wireless sensor networks,” in Proc. IEEEVTC, Yokohama, May 2012, pp. 1–5.

[91] J. Chen and M. Matsumoto, “EUCOW: Energy-efficient boundary mon-itoring for unsmoothed continuous objects in wireless sensor network,”in Proc. IEEE Int. Conf. Mobile Ad-Hoc and Sensor Systems (MASS),Macau, Oct. 2009, pp. 906–911.

[92] M. Ding, D. Chen, K. Xing, and X. Cheng, “Localized fault-tolerantevent boundary detection in sensor networks,” in Proc. IEEE INFOCOM,Miami, USA, Mar. 2005, pp. 902–913.

[93] P.-K. Liao, M.-K. Chang, and C. C. J. Kuo, “Distributed edge detectionwith composite hypothesis test in wireless sensor networks,” in Proc.IEEE GLOBECOM, Texus, USA, Nov. 2004, pp. 129–131.

[94] Z. Sun and H. Li, “Using directional antenna for continuous movingobject tracking in WSN with uncovered holes,” in Proc. IEEE Int.



15

Conf. Distrib. Comput. Systems Workshops (ICDCSW), Philadelphia,July 2013, pp. 274–279.

[95] H. Hong, S. Oh, J. Lee, and S.-H. Kim, “A continuous object trackingprotocol suitable for practical wireless sensor networks,” in Proc. IEEEWCNC, Shanghai, China, Apr. 2013, pp. 2351–2356.

[96] E. Lee, S. Park, F. Yu, M.-S. Jin, H. Park, and S.-H. Kim, “Dynamicrectangle zone-based collaboration mechanism for tracking continuousobjects in wireless sensor networks,” in Proc. IEEE Int. Conf. WirelessCommun., Netw. and Mobile Comput. (WiCOM), Dalian, Oct. 2008, pp.1–5.

[97] Y. Peng, Y. Li, L. Shu, and W. Wang, “An energy-efficient clustereddistributed coding for large-scale wireless sensor networks,” J. Super-comput., vol. 66, no. 2, pp. 649–669, Nov. 2013.

[98] M.-S. Jin, F. Yu, S. Park, E. Lee, and S.-H. Kim, “Localized mechanismfor continuous objects tracking and monitoring in wireless sensornetworks,” in Proc. Int. Symp. on Autonomous Decentralized Systems(ISADS), Athens, Mar. 2009, pp. 1–8.

[99] A. Milan, S. Roth, and K. Schindler, “Continuous energy minimizationfor multitarget tracking,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 36, no. 1, pp. 58–72, Jan. 2014.

Lei Shu eceived the Ph.D. degree from the NationalUniversity of Ireland, Galway, Ireland, in 2010.Until March 2012, he was a Specially AssignedResearcher with the Department of Multimedia En-gineering, Graduate School of Information Scienceand Technology, Osaka University, Japan. Since Oc-tober 2012, he has been with Guangdong Universityof Petrochemical Technology, Maoming, China asa Full Professor. Since 2013, he has been a Ph.D.Supervisor with Dalian University of Technology,Dalian, China, and a Master Supervisor with Beijing

University of Posts and Telecommunications, Beijing, China. He has alsobeen the Vice Director of the Guangdong Provincial Key Laboratory of Petro-chemical Equipment Fault Diagnosis, Guangdong University of PetrochemicalTechnology. He is the Founder of the Industrial Security and Wireless SensorNetworks Laboratory. He is the author of over 300 papers published inrelated conference proceedings, journals, and books. His current H-indexis 25. His research interests include wireless sensor networks, multimediacommunication, middleware, security, and fault diagnosis. Dr. Shu servedas a Cochair for more than 50 various international conferences/workshops,e.g., the IEEE International Wireless Communications and Mobile ComputingConference (IWCMC), the IEEE International Conference on Communi-cations (ICC), the IEEE Symposium on Computers and Communications(ISCC), the IEEE International Conference on Computing, Networking andCommunication, and the International Conference on Communications andNetworking in China (Chinacom). He also served/will serve as a SymposiumCochair for IWCMC 2012 and ICC 2012, as a General Chair for Chinacom2014 and the 2015 International Conference on Heterogeneous Networkingfor Quality, Reliability, Security, and Robustness (Qshine), as a Steering Chairfor the 2015 International Conference on Industrial Networks and IntelligentSystems, and as Technical Program Committee members of more than 150conferences, including the IEEE International Conference on DistributedComputing in Sensor Systems, the IEEE International Conference on MobileAd hoc and Sensor Systems, ICC, Globecom, IEEE International Conferenceon Computer Communications and Networks, IEEE Wireless Communicationsand Networking Conference, and ISCC. He currently serves as the Editor-in-Chief for the European Alliance for Innovation Endorsed Transactions onIndustrial Networks and Intelligent Systems, and the Associate Editor fora number of renowned international journals. He received the 2010 IEEEGlobal Communications Conference and 2013 IEEE International Conferenceon Communications Best Paper Awards. He is a member of the IEEE Commu-nication Society, the European Alliance for Innovation, and the Associationfor Computing Machinery.

Mithun Mukherjee [S’10–M’16] received the B.E.degree in electronics and communication engineer-ing from University Institute of Technology, Burd-wan University, India in 2007, the M.E. degree ininformation and communication engineering fromIndian Institute of Science and Technology, Shibpur,India in 2009, and the Ph.D. degree in electricalengineering from Indian Institute of TechnologyPatna, India in 2015. Currently, he is a SpeciallyAssigned Researcher in the Guangdong ProvincialKey Lab of Petrochemical Equipment Fault Diag-

nosis, Guangdong University of Petrochemical Technology, China. He wasa faculty in the department of electronics and communication engineering,National Institute of Technology Hamirpur, India (2014-2015). His researchinterests include wireless sensor network, wireless communications, sensornetwork middleware, dynamic spectrum sharing, and cloud computing.

Xiaoling Xu received the M.S. and B.S. degree inthe department of Automation, School of Electri-cal Engineering and Automation, Henan Polytech-nic University, and B.S degree from Departmentof Electric Power engineering, School of ElectricalEngineering and Automation, Henan PolytechnicUniversity, China in 2008 and 2006, respectively.Shehas served for 4 years as a teacher of measurementtechnology and instrument specialty in GuangdongUniversity of Petrochemical Technology, China. Hercurrent research interests are wireless sensor net-

works, intelligent control.

Kun Wang [M’13] received the B. Eng. and Ph.D.degree in the School of Computer, Nanjing Uni-versity of Posts and Telecommunications, Nanjing,China, in 2004 and 2009. From 2013 to 2015,he was a Postdoc Fellow in Electrical EngineeringDepartment, University of California, Los Angeles(UCLA), CA, USA. In 2016, he was a ResearchFellow in the School of Computer Science and En-gineering, the University of Aizu, Aizu-WakamatsuCity, Fukushima, Japan. He is currently an Asso-ciate Professor in the School of Internet of Things,

Nanjing University of Posts and Telecommunications, Nanjing, China. He haspublished over 50 papers in referred international conferences and journals,including IEEE Transactions on Industrial Informatics, ACM Transactionson Multimedia Computing, Communications and Applications, ACM Trans-actions on Embedded Computing Systems, IEEE Wireless CommunicationsMagazine, IEEE Communications Magazine, IEEE Network, IEEE Access,and IEEE Sensors Journal. His current research interests are mainly in thearea of big data, wireless communications and networking, smart grid, energyInternet, and information security technologies. He is a member of IEEE andACM.



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Xiaoling Wu received her Ph.D. degree from De-partment of Computer Engineering, Kyung Hee U-niversity, Korea, in 2008 and her MS. and BS. fromHarbin Institute of Technology, Harbin, China in2003 and 2001 respectively. From 2008 to 2012,she was a senior engineer in R&D center of AT LabInc., Korea. Since June 2012, she joined GuangzhouInstitute of Advanced Technology, Chinese Acade-my of Sciences, as an associate professor. She isnow an associate professor in Guangdong Universityof Technology and an evaluation expert both in

Guangdong and Guangzhou bureau of Science and Technology. Her researchinterests include wireless sensor networks, touch sensing and IoT. She haspublished over 10 SCI indexed journal papers and over 30 EI indexed papers,granted 7 Korea and Taiwan patents and filed more than 15 China inventionpatents. She has been serving as Area Editor for EAI Endorsed Transactionson Industrial Networks and Intelligent Systems and Lead Guest Editor forIJDSN special issue. Besides, she has been a reviewer of multiple SCI/EIinternational journals and TPC member/session chair for many internationalconferences.

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