Research ArticleFault Detection Modelling and Analysis in a WirelessSensor Network

Shuang Jia ,1 Lin Ma ,1 and Danyang Qin 2

1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China2Department of Communication Engineering, Heilongjiang University, Harbin 150080, China

Correspondence should be addressed to Lin Ma; malin@hit.edu.cn

Received 26 May 2018; Accepted 6 September 2018; Published 17 October 2018

Guest Editor: Ioana Fagarasan

Copyright © 2018 Shuang Jia et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

For the serious impacts of network failure caused by the unbalanced energy consumption of sensor nodes, hardware failure, andattacker intrusion on data transmission, a low-energy-consumption distributed fault detection mechanism in a wireless sensornetwork (LEFD) is proposed in this paper. The time correlation information of nodes is used to detect fault nodes in LEFDfirstly, and then the spatial correlation information is adopted to detect the remaining fault nodes, so as to check the states ofnodes comprehensively and improve the efficiency of data transmission. In addition, the nodes do not need to exchangeinformation with their neighbor nodes in the detection process since LEFD uses the data sensed by the node itself to detectsome types of faults, thus reducing the energy consumption of nodes effectively. Performance analysis and simulation resultsshow that the proposed detection mechanism can improve the transmission performance and reduce the energy consumption ofthe network effectively.

1. Introduction

A wireless sensor network (WSN) consists of a large numberof sensor nodes deployed in a specific area in a self-organizedmanner. There is no central control node in the network, andthe end-to-end information transmission can be achieved bythe intermediate nodes in a multihop forwarding way [1].WSN with the flexible, distributed, and dynamic characteris-tics has a wide range of applications, such as battlefield, disas-ter relief, exploration, environmental threat detection, andother fields [2]. The sensor nodes, however, often suffer fromvarious attacks and other external damage since they are usu-ally deployed in severe environments. In addition, sensornodes have the low manufacturing cost and limited resourcesand radio coverage. All the factors will cause failure to thenodes and thus will reduce the accuracy of monitoring data.Therefore, the network node fault detection is very importantfor ensuring the accuracy of monitoring results.

The fault detection algorithms of sensor nodes in WSNcan be divided into centralized fault detection and distributed

fault detection according to different data processingmethods [3]. The centralized fault detection algorithms usu-ally require all the information being collected by a particularnode and then determine the states of the other nodes. Thesealgorithms can lead to many problems easily, such as single-node failure, information loss, and much energy consump-tion [4]. The distributed fault detection algorithms requireeach node to possess the ability to detect faults and adoptthe data collected by itself or the surrounding nodes to deter-mine their own faults [5]. At present, some problems existingin the fault detection algorithms are as follows [6]:

(1) The network energy consumption is sacrificed forhigher detection accuracy and lower false-positiveratio. In the existing detection algorithms, the sensornode needs to communicate with its neighbor nodesduring fault detection, which will lead to higherenergy consumption

(2) The types of fault nodes are not fully considered.Therefore, the detection performance of these

HindawiJournal of SensorsVolume 2018, Article ID 7935802, 9 pageshttps://doi.org/10.1155/2018/7935802

algorithms will decline rapidly if the types of faultnodes increase

(3) The ability of sensor nodes to collect data is not fullyutilized, and only the spatial correlation of the sensornetwork is used to achieve fault detection so that thecomplexity of algorithms increases significantly

In order to solve the above problems, a low-energy-consumption distributed fault detection mechanism in awireless sensor network is proposed in this paper. LEFDadopts the time correlation features of the data collectedby sensor nodes to detect certain types of fault nodes andthen removes them from the network. LEFD may reducethe time and energy consumption in the communicationbetween neighbor nodes. Then, LEFD adopts the spatial cor-relation properties of WSN to detect the remaining faultnodes that are not detected during the initial detectionphase. If the measured value of a node is the same or similarto that of the neighbor which is in the normal state, the nodecan be considered a normal node. Otherwise, the node isconsidered a faulty node. The algorithm also considers thetransient faults in the sensor readings and corrects the faultdata using the data collected in a short period of time whentransient faults occur, which avoids mistaking the normalnode as a faulty node.

2. Related Work

A distributed Bayesian algorithm for detecting and correct-ing node faults in WSN (BAFD) is proposed [7]. In BAFD,a sensor node exchanges information with its neighbor nodesto obtain the statistical probability of the event, and the fail-ure ratio of the node is used to identify events and faultnodes. A fault detection scheme is proposed in [8], whereeach node detects any suspicious behavior using time corre-lation in its own reading, and the suspected node is requiredto communicate with the confident neighbor node to find thefault node. The algorithm has high detection accuracy andlow communication overhead but does not take into accountthe impact of transient failure. The authors in [9] propose adistributed byzantine fault detection method based onhypothesis testing, the Neyman-Pearson test method is usedto predict the fault states of each sensor node and adjacentsensor nodes, and then the final state of the node is deter-mined by voting in this mechanism. A distributed fault detec-tion method based on metric correlation is proposed in [10].The algorithm detects the fault nodes through the internalmetric correlation of sensor nodes. The computational com-plexity of the algorithm is low, but it does not consider theinfluence of transient faults. A distributed localized fault sen-sor detection algorithm for wireless sensor networks (DLFS)is proposed in [11], and the mutual test results between nodesand neighboring nodes are utilized to determine the states ofnodes. DLFS has high detection accuracy and low computa-tional complexity, but the algorithm requires at least twocommunications between adjacent nodes, thus resulting inmuch energy consumption. The authors in [12] propose afault detection mechanism based on the hidden Markov

random field. The HMRF model is used to characterize thecorrelation between the measured value and the actual valueof the sensor node, and then the parameters of the HMRFmodel are obtained through the variable error estimationmethod to determine the state of the node. The method hashigh detection accuracy and low false-positive ratio, but italso causes high computational complexity and much energyconsumption. In [13], a fully distributed fault detection algo-rithm is proposed. In this algorithm, the nodes first collectthe measurements of their neighborhoods and process themto determine whether they contain an exception value andbroadcast the results. Then, nodes determine their own oper-ational state autonomously. Therefore, the computationalcomplexity of the algorithm is low, while the algorithm needsto communicate with its neighbor nodes several times, thusleading to much energy consumption. The authors in [14]propose a novel method for detecting a sensor that generatesfault data in a distributed manner. The algorithm detects thefault nodes in the cluster locally through the cluster head anduses the trust concept to identify the type of data failure,which may reduce the influence of a fault node on sensorprobability. However, this method leads to the uneven energyconsumption of sensor nodes.

It can be seen that the existing detection algorithmdoes not fully consider the types of the faulty nodes, result-ing in poor detection performance. In addition, the multiplecommunications between adjacent nodes generate muchenergy consumption.

Aimed at improving the detection accuracy and reducingthe energy consumption of the existing detection algorithm,this paper presents a low-energy-consumption distributedfault detection mechanism in a wireless sensor network. Themain contributions of this paper are summarized as follows:

(1) A low-energy-consumption distributed fault detec-tion mechanism is proposed, which uses the timecorrelation information of sensor nodes to detectthe fault nodes in the initial detection stage. Duringthe detection of the remaining nodes, the other nodesdo not need to communicate with the detected faultnodes, thus reducing the communication traffic andnetwork energy consumption

(2) All kinds of fault nodes are fully considered to ensurethe performance of the detection algorithm. In addi-tion, the proposed algorithm also considers the nodesthat may have transient faults in the sensor readings.For transient faults, the fault values will be promptlycorrected by LEFD to avoid mistaking the normalnode as a faulty node, thus improving the utilizationof nodes and reducing false-positive ratio

3. System Model

3.1. Network Model. WSN is a special wireless communica-tion system, which does not depend on any fixed communi-cation facilities; it can be deployed in a complex environmentfor data communication rapidly, and the architecture ofWSN is shown in Figure 1. Each node plays the role of a

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router and endpoint and has access service and wireless back-bone interface [15]. There is no absolute domination of thenodes in WSN, and each node is equal and independent.The data between nodes are transmitted to the destinationnode by intermediate nodes; that is, data transmission is car-ried out by multihop forwarding, which can guarantee theflexibility of network topology.

Assume that the number of nodes randomly deployed ina specific area is N . These sensor nodes have the same com-munication radius Rmax. A node stores at least k segmentsof data that has been collected before executing the faultdetection algorithm. ni represents the i-th node in WSN.The node in the node ni′s communication radius is calledthe neighbor node of ni. N ni denotes all neighbor nodesof ni, and Num N ni represents the number of neighbornodes of ni. d

ti denotes the measurement data of the node

ni at t. It is assumed that the k segments of the data have beencollected in the sensor and stored in the memory before t,that is, dti , dt−1i ,… , dt−k−1i . Node ni and node N ni are inthe same or similar environment, which means that theneighbor nodes of ni are also in the same event area if thenode ni is in the event area and the neighbor nodes of niare also in the same normal area if the node ni is in the nor-mal area. The remaining parameters are shown in Table 1.

3.2. Fault Model. Nodes could still receive, send, collect, andprocess data if the network is partially faulty, but the datacollected by nodes is usually wrong. According to the abnor-mal data collected by nodes, the fault of sensor nodes can bedivided into the following specific types [16]:

(1) Fixed fault: a sensor with this fault collects data withthe same reading, and the data is not affected by theenvironment

(2) Random fault: node readings are random anduncertain

(3) Offset fault: the node readings deviate from normalvalues, and the readings may change if the environ-ment changes

(4) Transient fault: transient fault may occur in a shorttime due to the hardware characteristics and theimpact of the environment on the data collecting

process, resulting in data anomalies occurring oneor more times

In order to improve the utilization of the sensor nodes,this paper considers that the nodes with transient faults arenormal nodes because the readings of these nodes are avail-able at most of the time.

4. Proposed Fault Detection Model

4.1. Detection Principle. The data collected by sensor nodes ina short time is temporally relevant, which means that the col-lected data is the same or similar in a short time, and thechange is not so great [17]. LEFD can detect some types offault nodes based on this feature, such as random faults andtransient faults. The value of the collected data in a short timeis unstable when these faults occur. However, this paper willconsider that the nodes with transient faults are normalnodes to improve the utilization of nodes, so only the col-lected data generated when the fault occurs is corrected; thenormal node will not be mistaken for a fault node. Thematrix Q is established to determine whether there are tran-sient faults or random faults based on the difference of thedata collected by nodes. The faulty data will be replaced bythe collected normal data at other times for transient faults;therefore, the false-positive ratio can be effectively reduced.However, it is not enough to use only the time correlationinformation. For example, the node’s reading still satisfiesthe time correlation feature when fixed faults or offset faultsoccur, and this type of fault nodes cannot be detected justby using the time correlation feature, so the neighbor nodesare necessary. The node fails if the collected data of mostneighbor nodes is not similar to the node’s collected data;that is, the sensor nodes have spatial correlation property,which means that most sensor nodes have the same or similarreadings in smaller areas.

The differences between LEFD and the existing algo-rithms can be summarized from the above analysis. Firstly,LEFD uses time correlation information to detect certain

Cluster heads

Sensor nodes

Base station

Figure 1: Network model.

Table 1: Notations and their definitions.

Notation Definition

ni The i-th node

Num N ni Number of neighbors of niSi Initial states of sensor nodes

vri Each row in matrix QEc Total energy consumption

N ni All neighbor nodes of ni

dti Measurement data of node ni at t

FSi Final states of sensor nodes

k Number of data collected by node niRmax Communication radius

DA Detection accuracy

FPR False-positive ratio

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types of fault nodes and corrects some values as needed, andthen the spatial correlation property of nodes is adopted todetect the remaining fault nodes. However, the existing algo-rithms do not use time correlation information, or only thespatial correlation information is adopted to detect faultnodes, so there always are undetected fault nodes in the net-work. Secondly, the existing algorithms do not consider thetransient faults of nodes so that the normal node is mistakenas a faulty node, thus reducing the utilization ratio of nodes.

4.2. Detection Method. The latest k segments of data can beobtained after the node ni collects the data at time t. Thematrix Q is established according to

Qm×n =0, if dmi − dni ≤ β1 ,1, otherwise, m, n = 0, 1,… k − 1

1

For each row in matrix Q, vri is calculated as

vri =0, if 〠

t

j=t−k+1Qij <

k2 ,

1, otherwise, t − k + 1 ≤ r ≤ t

2

At time t, the value of vri is corrected by vti :

vti =0, if 〠

t

r=t−k+1vri <

k2 ,

1, otherwise3

Any measured value at other times when vri = 0 can beconsidered its value at time t.

Equation (4) is used to determine the initial states of sen-sor nodes:

Si =0, if 〠

t

r=t−k+1vri <

k2 ,

1, otherwise4

For the node ni with state 0, the neighbor reading whoseinitial fault condition is 0 is obtained. Then, the final state ofthe node is determined according to (5) and (6):

vtij =0, if dti − dtj ≤ β2 ,

1, otherwise5

FSi =0, if 〠

nj∈N ni ,Si=0vtij <

Num N ni , S = 02 ,

1, otherwise,6

where Num N ni , T = 0 denotes the number of neighbornodes of ni and the number of nodes whose state may be nor-mal. β represents the node failure thresholds. FSi = 0 repre-sents that the node ni is a normal node. Otherwise, thenode ni is a faulty node.

For example, assuming that k = 5 and Num N ni = 5,the k segments of data collected by the node ni at time tand before t are dti , dt−1i , dt−2i , dt−3i , dt−4i , and their corre-sponding values are 60 12, 30 23, 31 54, 10 68, 30 87 . Theneighbor nodes’ reading of node ni is dt1, dt2, dt3, dt4, dt5at t, whose values are 70 22, 31 35, 65 79, 30 84, 31 10 ,and β = 2. Then, the matrix Q is established according to (1):

Q5×5 =

0 1 0 0 11 0 1 1 10 1 0 0 10 1 0 0 11 1 1 1 0

7

There are vri = 0, 1, 0, 0, 1 and t − 4 ≤ r ≤ t t ≥ 4according to (2). According to (3), the value of vti is correctedto 0; then, there is vri = 0, 1, 0, 0, 0 . The measured value at ris used to update dti when there is vri = 0, namely, dti = 30 23.According to (4), the initial fault state of node ni is consid-ered S = 0, which represents that the node ni is a normalnode. Then, the algorithm obtains the neighbor nodes’ dataof node ni at time t. Equation (6) shows FSi = 0, and the finalstate of ni is normal.

As can be seen from this example, LEFD is a very effectivedetection method for transient faults and random faults. Thedetailed description of LEFD is shown in Algorithm 1.

The algorithm adopts the historical data sensed by nodesto determine the initial state of nodes. The node may be anormal node if the collected data is stable in a short time(almost no change). Otherwise, the node may be a faultynode. In other words, only the sensor node’s own data canbe used to identify some of the fault nodes. After determiningthe initial states of nodes, LEFD further determines that theinitial states of their neighbor nodes are normal for the nodeswith a normal initial state. A node will be determined as anormal node if its measured value is similar to that of mostof its neighbor nodes. In the whole algorithm implementa-tion process, the fault nodes that are identified in the initialdetection process are no longer able to communicate withother normal nodes, and the algorithm adopts the data fromnodes whose initial state is normal. This method not onlyconsumes less energy but also reduces the error detectionratio. In addition, LEFD also considers the transient faultsof nodes. The algorithm will correct the false readings when

4 Journal of Sensors

transient faults occur, which means that the algorithm adoptsthe reading at other times instead of the reading at this timeto further improve the fault tolerance ability of sensor nodesto transient faults.

5. Simulation Experiment andPerformance Analysis

5.1. Performance Indicators. The two indicators are usu-ally adopted to evaluate the effect of fault node identifica-tion, namely, detection accuracy and false-positive ratio.

Detection accuracy (DA) refers to the ratio between thenumber of fault nodes that have been correctly identifiedand the total number of actual fault nodes:

DA = F ∩ AA

, 8

where F represents the set of fault nodes detected by thealgorithm and A represents the set of actual fault nodes.

(1) Begin(2) for each node ni in WSN i = 1, 2,… ,N/* The following method is adopted to establish Q*/(3) for each k times before time t (including time t)(4) if dmi − dni ≤ β1(5) Qmn = 0(6) else(7) Qmn = 1(8) end if(9) end for/* Generate test vri */(10) if ∑t

j=t−k+1Qij < k/2(11) vri = 0(12) else(13) vri = 1(14) end if/*Correct vti */(15) if ∑t

r=t−k+1vri < k/2 and vti = 1

(16) vti = 0, dti = dt−xi // t − x ∈ t − k + 1, t and vt−xi = 0(17) else if ∑t

r=t−k+1vri ≥ k/2 and vti = 0

(18) vti = 1, dti = dt−xi // t − x ∈ t − k + 1, t and vt−xi = 1(19) end if/* Generate a state value Si = 0 based on the value of vri */(20) if ∑t

r=t−k+1vri < k/2

(21) Si = 0(22) else(23) Si = 1(24) end if/* For the nodes with status value Si = 0, test each member of their neighbors to generate test vtij 0, 1 by adopting the followingway*/(25) if dti − dtj ≤ β2(26) vtij = 0(27) else(28) vtij = 1(29) end if/* Make the final decision of the nodes’ state FSi */(29) if ∑nj∈N ni and Si=0v

tij <Num N ni and S = 0 /2

(30) FSi = 0(31) else(32) FSi = 1(33) end if(34) end forProcess end

Algorithm 1: The pseudocode of low-energy-consumption distributed fault detection algorithm.

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The false-positive ratio (FPR) refers to the ratio betweenthe number of normal nodes which are identified as faultnodes and the total number of normal nodes:

FPR = F − AN − A

9

Most of the energy consumption is caused by communi-cation between nodes [18]. Thus, the total number of com-munications between nodes can be adopted to representthe total network. When the communication radius of nodeis Rmax, it is assumed that the average energy consumptionwhen the node ni communicates with its neighbor nodeonce is eleci:

Ec = 〠N

i=1eleci, 10

where Ec denotes the total energy consumption.

5.2. Parameter Settings. The performance of LEFD was ana-lyzed using NS2 in this study [15]. In order to maintain thegenerality, it is assumed that the position of each node isknown and all nodes have the same communication radiusRmax, and the reading of the nodes in the normal region issubject to the distribution of N μ, σ μ = 35, σ = 1 . At least5 segments of data (k = 5) are stored in each sensor node. Thevalue of k should not be chosen too high because the sensornodes have limited storage capacity. The data may take uptoo much storage space if the value of k is too high. The nodefailure threshold β is 5, and the basic idea of the node failurethreshold selection is to determine the node failure thresholdaccording to the allowable deviation of the sensor node. Thekey step is designing an observer. The output of the observerand the output of the sensor node constitute a redundant sig-nal, and then the two signals are compared to obtain the sen-sor residual sequence. The allowable error of the sensor nodeis selected as the node failure threshold [19]. Since the fixedfault is similar to the offset fault, the two types of faults arealso regarded as offset faults. The results were obtained fromthe mean of 100 experiments. All of the simulation parame-ters are shown in Table 2.

5.3. Experimental Results and Performance Analysis.Figures 2(a) and 2(b) show the performance comparisonresults of different algorithms in terms of DA and FPR whenonly offset faults occur. It can be seen that the DA of DLFS ismuch higher when the sensor fault probability is less than30% as shown in Figure 2(a), and the DA of LEFD proposedin this paper is similar to that of BAFD; the DA of DLFS israpidly reduced compared with that of LEFD and BAFDwhen the sensor fault probability is higher than 30%. How-ever, DLFS has low FPR, and the FPR of LEFD is betweenthe FDR of DLFS and BAFD. Based on all the above factors,the performance of the LEFD algorithm is between the per-formances of DLFS and BAFD when only offset faults occur.

Figures 3(a) and 3(b) show the performance comparisonresults of different algorithms in terms of DA and FPR when

only random faults occur. It can be seen that the sensor faultprobability has a little effect on the DA, and the FPR increaseswith the increasing sensor fault probability as shown inFigure 3. However, LEFD also has good performance andalways maintains high DA and low FPR even in the case ofhigh sensor fault probability for random faults, since theLEFD algorithm first checks whether the nodes’ reading isstable in a short time. The node may be faulty if its readingis unstable, because the data of the random fault sensor israndom and unstable. Since the range of random faults isfrom 1 to 100, DLFS and BAFD are effective for this faultbut are less efficient than LEFD. The DA of DLFS and BAFDare also more than 93%, and the DA may increase (such asthat of BAFD) when the sensor fault probability increases.But the FPR of DLFS and BAFD also increase when the sen-sor fault probability increases. However, the FPR of LEFD isalmost zero.

Figure 4 shows the relationship between the sensor faultprobability and the FPR if only transient faults occur whenk = 5. It can be seen that the FPR of all algorithms increasewith the increasing sensor fault probability as shown inFigure 4. The FPR of LEFD is very low because it has a goodability to handle transient faults. For example, the FPR ofLEFD is still less than 5% when the sensor fault probabilityis 50%, since LEFD determines whether the collected data iscorrect according to the k segments of data. LEFD willreplace the current data with the data collected at anothertime to avoid the impact of transient faults if data is wrong.This is why LEFD has a good fault tolerance performancefor transient faults. However, both DLFS and BAFD do notconsider transient faults, so the FPR will continue to increaseas the sensor fault probability increases.

Figures 5(a) and 5(b) show the relationship between thesensor fault probability and the DA/FPR when the offsetfaults, random faults, and transient faults occur randomly,respectively. The DA of DLFS and LEFD is almost the sameas shown in Figure 5(a). The DA of DLFS and LEFD arehigher than that of BAFD when the sensor fault probabilityis less than 35%. However, the DA of DLFS will decline rap-idly when the sensor fault probability is greater than 35%.The FPR of LEFD is the lowest of the three algorithms. Inshort, the performance of the LEFD algorithm achieves ourexpectations in the event of mixed faults.

Table 2: Simulation parameters.

Parameter Value

The type of nodes Normal node, fault node

The number of nodes 1024

Network range 32m × 32mThe reading range of offset fault 61–70

The reading range of random fault 1–100

The reading range of transient fault 1–100

Rmax 2

eleci 10−5 Jk 5

6 Journal of Sensors

Figure 6 shows the relationship between the energy con-sumption (EC) of DLFS, BAFD, and LEFD and the sensorfault probability when the transient fault ratio and the offsetfault ratio are 1 : 1, the communication radius Rmax is 2, andthe nontransient faults occur. It can be seen that DLFS hasmuch higher energy consumption as the sensor fault proba-bility increases under the same conditions as shown inFigure 6, since each node needs to communicate with itsneighbor nodes at least twice (the first communication is toexchange the initial data set, the second communication isto exchange the initial state of each node). However, nodesthat have not yet determined the final state need to makethe third communication. As a result, the energy consump-tion of DLFS is always relatively high. Each node only needsto communicate with its neighbor nodes once for BAFD, soits network energy consumption is moderate and does notchange with the sensor fault probability. LEFD first adoptsthe time correlation information for initial fault detection,and each node does not need to communicate with its neigh-bor nodes in this process. Only nodes that have been detected

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.00.10.20.30.40.50.60.70.80.91.01.11.2

DA

Sensor fault probabilityLEFDBAFDDLFS

(a)

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.000.050.100.150.200.250.300.350.400.450.50

FPR

Sensor fault probabilityLEFDBAFDDLFS

(b)

Figure 2: (a) Detection accuracy under offset faults. (b) False-positive ratio under offset faults.

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.800.820.840.860.880.900.920.940.960.981.001.021.041.06

DA

Sensor fault probabilityLEFDBAFDDLFS

(a)

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.000.050.100.150.200.250.300.350.400.450.50

FPR

Sensor fault probability

LEFDBAFDDLFS

(b)

Figure 3: (a) Detection accuracy under random faults, (b) False positive ratio under random faults.

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.000.050.100.150.200.250.300.350.400.450.500.550.60

FPR

Sensor fault probability

LEFDBAFDDLFS

Figure 4: The relationship between the false-positive ratio and thesensor fault probability under transient faults.

7Journal of Sensors

to have a normal state need to communicate with the neigh-bor nodes and consume additional energy. Therefore, mostof the nodes will be detected as fault nodes by the LEFD algo-rithm in the case of high sensor fault probability, and theenergy consumption of the network also decreases. In sum-mary, the energy consumption of the LEFD algorithm is low.

6. Conclusions

WSN is an important component of modern mobile commu-nication systems. However, network performance is seriouslyaffected due to the breakage of data link and frequent changesof network topology. Therefore, a low-energy-consumptiondistributed fault detection mechanism in WSN is proposedin this paper. LEFD adopts the data sequence collected bythe sensor node itself to detect a particular type of fault andthen further uses the neighbor data to determine the statesof nodes, thus reducing the communication traffic and net-work energy consumption. In addition, LEFD also considersthe nodes that may have transient faults. For transient faults,

the fault values will be promptly corrected by LEFD to avoidmistaking the normal node as a faulty node, thus reducingfalse-positive ratio. The simulation results show that LEFDhas high detection accuracy, low false-positive ratio, and lessenergy consumption for various faults. Future research willstudy a fault tolerance method for WSN, which may providea new way for the effective transmission of data and ubiqui-tous routing.

Data Availability

The data used to support the findings of this study are cur-rently under embargowhile the researchfindings are commer-cialized. Requests for data, 12 months after the publication ofthis article, will be considered by the corresponding author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work is supported by the National Natural ScienceFoundation of China (61571162, 61771186), HeilongjiangProvince Natural Science Foundation (F2016019), Minis-try of Education-China Mobile Research Foundation(MCM20170106), and University Nursing Program forYoung Scholars with Creative Talents in HeilongjiangProvince (UNPYSCT-2017125).

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0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.500.00.10.20.30.40.50.60.70.80.91.01.11.2

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