2054 ieee transactions on industrial informatics, …

2054 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 16, NO. 3, MARCH 2020

MTES: An Intelligent Trust Evaluation Scheme inSensor-Cloud-Enabled Industrial

Internet of ThingsTian Wang , Hao Luo , Weijia Jia, Anfeng Liu , and Mande Xie

Abstract—As an enabler for smart industrial Internet ofThings (IoT), sensor cloud facilitates data collection, pro-cessing, analysis, storage, and sharing on demand. How-ever, compromised or malicious sensor nodes may causethe collected data to be invalid or even endanger the normaloperation of an entire IoT system. Therefore, designingan effective mechanism to ensure the trustworthiness ofsensor nodes is a critical issue. However, existing cloudcomputing models cannot provide direct and effective man-agement for the sensor nodes. Meanwhile, the insufficientcomputation and storage ability of sensor nodes makesthem incapable of performing complex intelligent algo-rithms. To this end, mobile edge nodes with relatively strongcomputation and storage ability are exploited to provideintelligent trust evaluation and management for sensornodes. In this article, a mobile edge computing-based in-telligent trust evaluation scheme is proposed to compre-hensively evaluate the trustworthiness of sensor nodesusing probabilistic graphical model. The proposed mech-anism evaluates the trustworthiness of sensor nodes fromdata collection and communication behavior. Moreover, themoving path for the edge nodes is scheduled to improvethe probability of direct trust evaluation and decrease the

Manuscript received April 25, 2019; revised June 4, 2019 and June26, 2019; accepted July 7, 2019. Date of publication July 23, 2019;date of current version January 16, 2020. This work was supportedin part by the General Projects of Social Sciences in Fujian Provinceunder Grant FJ2018B038, in part by the National Natural ScienceFoundation of China (NSFC) under Grant 61872154, Grant 61772148,and Grant 61672441, in part by the Natural Science Foundation of FujianProvince of China under Grant 2018J01092, in part by the the FujianProvincial Outstanding Youth Scientific Research Personnel TrainingProgram and the Chinese National Research Fund (NSFC) Key Project61532013 and Project 61872239, in part by the Science and TechnologyDevelopment Fund, Macao (FDCT), China, under Grant 0007/2018/A1,Grant 0060/2019/A1, and DCT-MoST Joint-Project 025/2015/AMJ, andin part by the University of Macau under Grant MYRG2018-00237-RTO,Grant CPG2019-00004-FST, and Grant SRG2018-00111-FST. Paper no.TII-19-1542. (Corresponding author: Weijia Jia.)

T. Wang and H. Luo are with the College of Computer Scienceand Technology, Huaqiao University, Xiamen 361021, China (e-mail:,[email protected]; [email protected]).

W. Jia is with the State Key Laboratory of Internet of Things forSmart City, University of Macau, Macau 519000, China (e-mail:,[email protected]).

A. Liu is with the School of Information Science and Engineering,Central South University, Changsha 410006, China (e-mail:, [email protected]).

M. Xie is with the School of Computer Science and InformationEngineering, Zhejiang Gongshang University, Hangzhou 310018, China(e-mail:,[email protected]).

Color versions of one or more of the figures in this article are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TII.2019.2930286

moving distance. An approximation algorithm with provableperformance is designed. Extensive experiments validatethat our method can effectively ensure the trustworthinessof sensor nodes and decrease the energy consumption.

Index Terms—Artificial intelligence (AI), edge computing,sensor-cloud, smart industrial Internet of Things (IoT), trustevaluation.

I. INTRODUCTION

SMART industrial Internet of Things (IoT), including in-dustrial equipment monitoring, industrial property man-

agement, smart manufacturing, and smart factory, is attractingincreasing attention from both academia and industry [1],[2]. Through sensing the industrial environment, underlyingdevices generate huge amounts of data which is hardly properlyprocessed due to their limited computation and storage ability[3]. Therefore, researchers propose sensor-cloud system (SCS)which combines cloud computing and sensor nodes to harnessthe processing power of the cloud and process the generated datain industrial IoT [4]. For example, smart surveillance systemsbased on SCS can help users to process and analyze industrialdata on demand [5].

However, the combination of cloud computing and underlyingnetwork brings many new issues [6], [7]. On one hand, thelimited computation and storage ability of sensor nodes andvulnerability to environment influences make it difficult for themto guarantee the data quality, and thus, leading to invalidity ofdata [8]. On the other hand, increased network connectivitymagnifies the harm of malicious nodes, which can result indisruption, damage, or even loss of life [9], [10].

To address the problems mentioned above, trust managementsystem is considered in SCS-enabled industrial IoT. The conceptof trust first originated in sociology and was considered as theassumption of knowledge, ability, and goodwill of others [11].Trust management system enables SCS to detect compromisedand malicious nodes and assure its normal operations. Currenttrust management systems can be classified into two categories,namely, centralized and decentralized.

1) In centralized trust management systems, a computingcenter evaluates and stores the trust value of all sensornodes [12], [13]. Compared with decentralized trust man-agement systems, centralized trust management systemshave relatively high evaluation accuracy with simple

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WANG et al.: MTES: AN INTELLIGENT TRUST EVALUATION SCHEME IN SENSOR-CLOUD-ENABLED INDUSTRIAL IOT 2055

implementation, while the disadvantage of this methodis that the nodes and the computing center have to usemultihop transmissions to communicate, which increasesthe energy consumption of the network.

2) In decentralized trust management systems, the trustvalue of a node is calculated and stored by its neigh-bors. The trust evidence is derived from behavior, i.e.,communication success rate, energy consumption rate[14]. Decentralized trust management systems have theadvantage of low energy consumption and fast responseover the centralized trust management systems. However,many complex trust evaluation algorithms based on ar-tificial intelligence (AI) cannot be implemented on thesensor nodes due to their limited computing and storagecapabilities [15]. Moreover, with the increase of networkscale, multihop trust transfer results in distortion of thetrust value and increases in energy consumption.

Therefore, both centralized and decentralized trust manage-ment systems face the problem of increased energy consumptionand distorted trust value when the network size increases.Meanwhile, the limited computation and storage ability ofsensor nodes makes them hardly able to conduct complexintelligent trust evaluation mechanisms. To the best of ourknowledge, there is currently no research to address theseproblems simultaneously.

To this end, we adopt edge computing and introduce mobileelements named mobile edge nodes (MENs) with relativelystrong computation and storage ability to conduct trust eval-uation. MENs can be used as bridges for connecting lowernetworks and upper remote cloud center [16], [17]. The mobilityof MENs enables them to evaluate sensor nodes in short range,which improves the accuracy of trust evaluation and minimizesthe energy consumption [18]. Moreover, a mobile-edge-basedtrust evaluation scheme (MTES) adopting the probabilisticgraphic model to represent the relationship between nodes andcalculate their trust value in MENs is proposed.

The contributions of this article can be summarized as fol-lows.

1) We innovatively introduce mobile elements in SCS-enabled industrial IoT to conduct trust evaluation. Com-pared with traditional architecture, the new architecturecan better connect underlying network and cloud, andprovide more find-grained management for underlyingsensor nodes.

2) We propose an MTES for SCS conducted on MENsusing probabilistic graph model. Compared with tradi-tional trust evaluation mechanisms, the proposed schemeowns advantages of lower energy consumption and moreaccurate trust evaluation results.

3) We design a moving strategy for MENs to improve theprobability of direct trust evaluation to improve the ac-curacy of trust evaluation. The proposed moving strategycan effectively decrease the moving distance and energyconsumption of MENs. A formal proof of effectivenessof the moving strategy is provided.

The remainder of this article is organized as follows. InSection II we present the related works. The proposed trust

evaluation mechanism is introduced in Section III. Section IVis about the detailed introduction of the moving strategy designof the MENs. Performance evaluation is given in Section V andSection VI concludes this article.

II. RELATED WORK

Some solutions have been proposed to deal with the trustmanagement issues in industrial IoT recently. Wang et al.proposed an energy-efficient and trustworthy protocol basedon mobile fog computing. By establishing a trust model on fogelements to evaluate the sensor nodes, the mobile data collectionpath with the largest utility value is generated, which can avoidvisiting unnecessary sensors and collecting untrustworthy data[8]. In this article, we focus on the process of using MENs toevaluate sensor nodes.

Jiang et al. proposed a distributed trust evaluation mecha-nism where sensor nodes consider communication behavior,residential energy, and data content as trust evidence and usesubjective logic to compute trust value [19]. By collecting theinformation of the neighbors, sensor nodes can get the directtrust of the surrounding nodes. When observers do not haveenough communication with the object node, they can gainrecommendation trust from the neighbors of the target. Trustevaluation of remote nodes are described by indirect trust usingthe trust propagation. In this article, we introduce MENs insteadof sensor nodes to conduct overall trust evaluation, which canachieve better performance.

Liu et al. proposed a sparse trust mining method to dealwith cold start and sparse evaluation problems in large-scalerecommendation systems [20]. The authors defined the rep-resentation of sparse trust and proposed roundtable gossipalgorithm to discover the hidden trust relationship. The authorsalso proposed an antisparsification method to overcome therandomness of trust propagation. In this article, we introduceMENs to overcome the randomness of trust propagation.

Karati et al. proposed a provably secure and lightweightsignature scheme to authenticate data [21]. The authors designeda new certificateless signature scheme to deal with both Type-Iand Type-II adversaries under the hardness of extended bilinearstrong Diffie–Hellman and bilinear strong Diffie–Hellman as-sumptions, respectively. In this article, we focus on the behaviorsof the data provider.

Furthermore, Wang et al. considered the trust issue in thecombination of IoT and cloud computing [18]. The authorsproposed a crowdsourcing method to recruit mobile elements toevaluate the trustworthiness of underlying nodes and used thefuzzy logical method to calculate the trust value of each node.Different from this work, our scheme focus on the integration ofunderlying nodes and MENs to further improve the performanceof SCS-enabled industrial IoT.

Zhang et al. considered hierarchical trust evaluation of sensornodes and cluster heads [22]. In cluster-based industrial IoT, thecluster head collects data from surrounding sensor nodes anduploads it to base station. Thus, the cluster head can monitorthe communication behavior and data quality of neighboringsensor nodes. This article introduces MENs rather than clusterto conduct trust evaluation.



Besides the traditional method, some authors adopted artifi-cial intelligence to approach the trust issue. Ling et al. proposeda reinforcement learning-based trust model to adjust the clustersize in distributed cognitive radio networks [23]. Jayasingheet al. designed a machine learning-based trust computationalmodel for IoT services [24]. This article adopts the probabilis-tic model to represent the trust relationship in an industrialenvironment.

Apart from trust evaluation, some people consider trust issuesfrom different perspectives. Ren et al. considered the security oftrust value storage and proposed a trust storage method in unat-tended industrial IoT to prevent the trust value in sensor nodesfrom being tampered with [25]. The authors mentioned threedifferent scenarios and verified their effectiveness accordingly.Meanwhile, based on various trust similarity methods, they alsoproposed several methods to detect trust pollution attack.

The work mentioned above considered and applied the trustevaluation of industrial IoT from different perspectives. Mostof them focused on novel trust evaluation mechanisms andtrust evaluation methods. However, the problems of energyconsumption and distorted trust value still remain unsolved.Thus, we introduce MENs to cope with these two problems.

III. MOBILE-EDGE-BASED TRUST EVALUATION SCHEME

In this section, we explain the design of the MTES in detail.In the real-world smart surveillance systems, sensor nodesare randomly deployed to monitor the industrial environment.The collected data of sensor nodes have temporal and spatialcorrelation [26]. Here, we represent the relationship of sensornodes using probabilistic graph model.

The probabilistic graph model is an AI method consistingof three parts—representation, reasoning, and learning [27].In SCS-enabled industrial IoT, the representation of the rela-tionship between entities is dependent on the structure of thenetwork. Reasoning can be defined as using the correlationbetween nodes and collected data to infer the trust value of nodesin trust evaluation. Learning in our method refers to parametriclearning. As the accumulation of history data, MENs update theparameters of the node itself and the relationship between thenodes.

The goal of MTES is to detect the malicious nodes in thenetwork. We consider the trust value of a node from datacollection behavior and communication behavior. For data col-lection behavior, MENs compare the data collected by the sensornodes to their neighbors’ data. For communication behavior, thetrustworthiness can be measured from the current behavior ofsensor nodes and their behavior history.

A. Probabilistic Graph Model

The probabilistic graphical model is adopted to represent therelationship between nodes [28]. As shown in Fig. 1, node j’sability to obtain real-world information can be represented as

P (xj = θ) = pj (1)

where xj is the observed result of node j and θ is the groundtruth.

Fig. 1. Probabilistic graphic model.

The influence of node i to node j can be described as Pij andQij according to the history record. Pij is the positive influenceof node i to node j which can be defined as follows:

P (xj = θ|xi = θ) = Pij . (2)

The definition of Qij is as follows:

P (xj = θ|xi �= θ) = Qij (3)

which describes the the negative influence of node i to node j.

B. Evaluation of Data Collection

In this section, we introduce the evaluation of data collectionbehavior of underlying nodes. Suppose that the data collectionability of a sensor node at time t is consistent with the distribu-tion with expectation

E(Dt

j

)= dt

j (4)

and variance

V(Dt

j

)= τ t

j (5)

When MENs visit a sensor node, it can obtain the influencebetween the node and its neighbor, supposing the expecta-tion of the positive reliance and negative reliance at timet + 1 are E

(P t+1

ij

)= pt+1

ij and E(Qt+1

ij

)= qt+1

ij . Variances

are V(P t+1

ij

)= τ t+1

ij,p and V(Qt+1

ij

)= τ t+1

ij,q . Therefore, theexpectation of the updated data trust of node j at time t + 1can be represented as follows:

E(T t+1

j

)=

T tj

τ tj

+∑

i∈Ni

pt + 1i j

τ t + 1i j , p

+∑

i∈Ni

q t + 1i j

τ t + 1i j , q

1τ t

j+

∑i∈Ni

1τ t + 1

i j , p

+∑

i∈Ni

1τ t + 1

i j , q

. (6)

where Ni is the set neighbor node of node j and i is the neighborof node j.

C. Evaluation of Communication

In this section, we introduce the evaluation of communicationbehavior. By accessing underlying nodes and their neighbors,MENs can obtain the history information of each node storedin their neighbors. By comparing their current behavior withtheir history information, MENs can evaluate whether the node



is malicious or not. For example, a node conducting floodingattack or black hole attack will send an abnormal number ofcommunicate packets within a period of time.

Suppose that the normal communication trust of node j attime t is

E(Ct

j

)= ct

j (7)

and the variance is

V(Ct

j

)= δt

j . (8)

The fluctuation of communication within a certain period oftime can be illustrated as

cΔtj = ω × exp

[

−num(pΔt

j

) − num (pj )2σ2

]

. (9)

where num(pΔt

j

)is the number of packet sent by node j within

Δt and num (pj ) is the average number of packets sent by nodej within every Δt, which can be obtained from the neighbor ofnode j. σ2 is the variance of noise.

Therefore, the expectation of updated trust of node j’s com-munication behavior can be expressed as follows:

E(Ct+Δt

j

)=

ctj

δ tj

+cΔ t

j

σ 2

1δ t

j+ 1

σ 2

. (10)

IV. MOVING STRATEGY DESIGN OF MOBILE EDGE NODE

In this section, a moving strategy design of the MEN isintroduced to decrease the moving distance of the MEN. Dueto the demand of security in industrial environments, the MENhas to get trust evidence directly from every single sensor node.The aim of the moving strategy of the MEN is to evaluate everysensor node while decreasing the travel distance of the MEN.

A. Problem Description

To simulate a real-world surveillance system, we randomlydeployed m fixed sensors in a two-dimensional L × L plane areato monitor the industrial environment. Due to the demand forsecurity of industrial environments, the MEN needs to evaluatethe trust value of every sensor. Meanwhile, trust evaluation onlyconsiders the single-hop information. This requires the MEN tocome within the radio range of every sensor during the trajectory.It is also required to decrease the trajectory length for reducingthe travel time. Therefore, the goal is to schedule the MEN todirectly evaluate every sensor and decrease the travel distanceas much as possible.

B. Algorithm of Mobile Edge Nodes

This section proposes basic steps of the moving edge algo-rithm (MEA). We assume that the MEN is a resource-rich devicewith sensor’s location information. To accomplish the goal, themoving mechanism can be divided into three steps.

Step 1: According to the network topological graph, MENneeds to determine which sensors should be visited. We namethem intermediary nodes (IN). Fig. 2(a) shows a sensor network

Fig. 2. Two simple examples to illustrate the problem. (a) No schedulingscheme. (b) Scheduling scheme.

TABLE INOTATIONS USED IN ALGORITHM 1

where nodes p1, p2, p3 are selected. By visiting these nodes, theMEN is able to access the trust evaluation of the whole network.The selection of IN can be formulated as a minimal dominatingset problem.

Step 2: After selecting the IN, the problem transforms intotraveling salesman problem with complete graph of IN. TheEuclidean distance between two nodes represents edge weight.

Step 3: Based on the observation, this algorithm can be furtheroptimized. Fig. 2(b) shows a modified example where MENvisit p1, p2, p4, and p3 instead of the original p0, p1, and p2. Itcan be proved that the route length p1p2p4p3 is shorter than thelength of p0p1p2. We name p0, p1, p2 outside points (OP), whilep4, p3 inside points (IP). The pseudocode of these three steps areshown in Algorithm 1 and the notations used in the algorithmare shown in Table 1.

C. Algorithm Analysis

We prove the effectiveness of the mobile edge algorithm asfollows.

Theorem 1: The perimeter of a convex shell inside a triangleis shorter than the perimeter of the triangle.

Proof: Suppose the edge number of convex shell equals n.�S(n) : Convex shell with n edges inside a triangle has shorter

perimeter than the triangle.Base case: Showing that S(n) holds for n = 3 is trivial, since

the convex shell inside the triangle is also a triangle. Obviously,a convex hull with three edges inside a triangle has a shorterperimeter.



Algorithm 1: Moving Edge Algorithm.Input: position of each sensor SOutput: ordered set of sensors T

1: while S is not empty do2: Select s with the most neighbors;3: DS = DS ∪ s;4: S = S\ {s, neighbors of s};5: end while // Calculate minimal

dominating set6: for each s in DS do7: Select the sensor with shortest distance to Fpos;8: Add s to T ;9: Fpos = spos;

10: end for // Calculate initial trajectory11: for each s in T do12: if si+3 is on the same side of −−−−−→si+1si+2 as si+2

toward −−−→sisi+1 then13: Cj = Cj ∪ si+3;14: else15: j = j + 1;Cj={si+1, si+2};16: end if17: end for18: for each C do19: CIP = {every sensors within Cov(C)};20: if dnum(Cov(smin, smax, CIP )) ≥ dnum(Cov(C))

then21: C = {smin, smax} ∪ CIP ;22: end if23: Add sensors in C to T ;24: end for // Find optimization in each convex

hull

Fig. 3. Illustration of Theorem 1.

Step case: Given that S(n) holds for some value of n ≥ 3,connecting any two adjacent edges inside the convex shell canget the situation of n + 1. As shown in Fig. 3, the given condi-tion shows that AB + BC + CA > ab + bc + cd + de + ea.To prove AB + BC + CA > ab + bc + cf + fg + ge + ea,it is required to prove that ab + bc + cd + de + ea > ab +bc + cf + fg + ge + ea, equivalent to cd + de > cf + fg +ge. Based on trilateral relation law in triangle, we can getfd + dg > fg. Thus, it holds.

Since both the base case and the inductive step case have beenproved, the statement holds for all natural numbers n ≥ 3.

Fig. 4. Illustration of Lemma 1. (a) n = 1. (b) n to n + 1.

Theorem 2: The perimeter of a convex shell inside a convexpolygon is shorter than the perimeter of the polygon.

Proof: This theorem can be split into two Lemmas. �Lemma 1: The perimeter of a convex shell with all its

endpoints on the convex polygon is shorter than the perimeterof the convex polygon.

We first prove Lemma 1 by mathematical induction. Supposethat the edge number of the convex shell is equal to k. Sincetwo points determine a straight line, a convex shell with allthe endpoints on a convex polygon can have at most 2k edges.Hence, to prove the theorem, we only need to prove that convexshells of 3, 4, ..., 2k edges with all endpoints on a convexpolygon have shorter perimeter than the convex polygon.

Let n = 2k + 1 − edge number of convex shell.S(n) : Convex shell of 2k + 1 − n edges inside a convex

polygon with endpoints on it has shorter perimeter than theconvex polygon.

Base case: When n = 1, as shown in Fig. 4(a), the edgenumber of convex hull is 2k.

Based on trilateral relation law in triangle and observation, wecan get Aa + Ab > ab, F l + Fk > lk,Ej + Ei > ji,Dh +Dg > gh,Ce + Cf > ef,Bd + Bc > dc and AB + BC +CD + DE + EF + FA = Aa + Ab + Fl + Fk + Ej + Ei+ Dh + Dg + Ce + Cf + Bd + Bc + al + kj + ih + gf +ed + cb. Therefore, AB + BC + CD + DE + EF + FA >ab + lk + ji + gh + ef + dc + al + kj + ih + gf + ed + cb,the convex shell has shorter perimeter than the convex polygon.

Step case: Given that S(n) holds for some value of n,connecting any two adjacent edges inside the convex shell canget the situation of n + 1. As shown in Fig. 4(b), ag < ah + hg.The given condition shows that AB + BC + CD + DE +EF + FA > ab + bc + cd + de + ef + fg + gh + ha, thenAB + BC + CD + DE + EF + FA > ab + bc + cd +de + ef + fg + ag. Thus, S(n + 1) holds.

Since both the base case and the inductive step case have beenproved, the statement S(n) holds for n = 3, 4, ..., 2k.

Lemma 2: The perimeter of a convex shell with some end-points inside the convex polygon is shorter than the perimeterof the convex polygon.

Here, we convert the situation of Lemma 2 into the onein Lemma 1 by constructing an auxiliary line passing anendpoint inside the convex polygon, intersecting the con-vex polygon at two points, as shown in Fig. 5. Accordingto Lemma 1, AB + BC + CD + DE + EF + FA > Ab +



Fig. 5. Illustration of Lemma 2.

BC + CD + Dh + hg + gA. Do the same for all endpointsin turn, we get AB + BC + CD + DE + EF + FA > · · · >ab + bc + cd + de + ef + fa. Hence, the perimeter of a convexshell with some endpoints inside the convex polygon is shorterthan the perimeter of the convex polygon.

Since both Lemma 1 and Lemma 2 hold, Theorem 2 holds.Theorem 3: Time complexity of MEA is O(n2), where n is

the number of sensor nodes.Proof: The algorithm includes the solution of the minimal

dominating set problem, solution of traveling salesman problem,and further optimization. The time complexity of minimaldominating set problem and traveling salesman problem usinggreedy method is O(n2) and O(m2), respectively, where m isthe number of IN. �

The algorithm optimization consists of building convex poly-gons, searching sensor nodes within the convex polygons, andfinding convex hulls set inside the convex polygons with thesame dominating set. The main operation of building convexpolygons is to determine the positional relationship between thepoints and lines with O(m) time complexity. The second stepdetermines the positional relationship between all sensor nodesand convex polygons. Time complexity depends on the numberof convex polygons consisting of IN. In the worst case, timecomplexity is O( 3

2nm − 32m) where each three points form a

triangle. During the process of finding convex hulls set insidethe convex polygons with the same dominating set, MEN onlyconsiders the convex shell closest to the convex hull with O(mn)time complexity.

Considering n � m, the time complexity of MEA is O(n2).

V. PERFORMANCE EVALUATION

This section is divided into four parts. The first part introducesthe experimental setting. Then, we evaluate the reliability ofMTES. The comparison between energy consumption of MTESand existing trust evaluation mechanism is provided in the thirdpart. Finally, we study the effectiveness of the moving strategyof MEN.

A. Experimental Setting

To evaluate our MTES and moving strategy of MEN, weconduct experiments in MATLAB 2017 and NS3. Consider-ing a real-world surveillance system, we simulate a networkdeployment scenario with relatively dense nodes and a short

TABLE IIEXPERIMENTAL PARAMETERS

communication radius. One hundred sensors with 15 m com-munication radius are randomly deployed in a 100 × 100 m2

area. The malicious nodes conduct black hole attack and datapoison attack. Each experiment is conducted 100 times and theresults are based on the average of trials. The experimentalparameters are presented in Table II.

B. Calculation of Trust Value

This section mainly evaluates the performance of the pro-posed trust evaluation mechanism. We first consider the effec-tiveness on data collection behavior and compare the trust valueover normal nodes and malicious nodes. Then, we compare thetrust value of nodes with normal communication behavior andmalicious communication behavior.

Fig. 6(a) shows the difference between trust value of normalnodes and malicious nodes on data collection. The original trustvalue of each node on data collection follows beta distributionwith mean value 0.9 from the results of 1000 times of collectingdata. Every sensor node collects 20 times of data each iteration.From iteration 0 to 25, all the data collected by maliciousnodes are wrong. Then, the attack is stopped and the datacollection behavior turns normal after iteration 25. From thefigure, it is clear that the trust value of malicious nodes beginsto drop at iteration 0 and rebound at iteration 25, which followsthe malicious behavior of the nodes. The results validate theeffectiveness of the trust evaluation on data collection.

In Fig. 6(b), the difference between trust value of normalnodes and malicious nodes on communication is illustrated. Ateach iteration, the normal sensor nodes send 15 packages tomaintain routing. Random event sources are set to simulate thereal environment. From iteration 15 to iteration 25, the maliciousnodes conduct black hole attack dropping all packets. Afteriteration 25, the attack is stopped and the malicious nodes returnto normal behavior. As show in Fig. 6(b), the trust value ofmalicious nodes drops suddenly after they begin to conductattack at iteration 15. The result validates the effectiveness ofthe trust evaluation on communication.

C. Analysis of Energy Consumption

In this section, we compare the energy consumption betweenour MTES and efficient distributed trust model (EDTM) [19].We consider the impact of the number of hops between subjectnode and object node on energy consumption. In EDTM, thesubject node needs to obtain the trust evaluation of the remoteobject node through multihop, whereas the subject node canacquire the trust value of the object node by communicatingwith MENs in MTES. We take the number of hops between thesubject node and the object node as input parameter. Meanwhile,



Fig. 6. Performance of the proposed MTES. (a) Trust evaluation on data collection behavior. (b) Trust evaluation on communication behavior.

Fig. 7. Influence of MEN on energy consumption and the influence of different parameters on moving distance. (a) Influence of MEN. (b) Influenceof communication radius. (c) Influence of number of nodes.

we conduct ten experiments and take the average in order toensure the validity of the results.

Fig. 7(a) shows that as the number of hops between the subjectnode and object node increases, the energy consumption ofnetworks with EDTM increases, while the energy consumptionof MTES remains stable. The reason is that in MTES, sensornodes can acquire the trust value of the object by communicatingdirectly with MENs rather than asking the neighbor of the objectnode.

D. Algorithm of Mobile Edge Nodes

In this section, we compare the proposed MEA and thecentralized minimum dominating set overlay method (CMD-SOM) [29]. CMDSOM is the combination of node selectionalgorithm and ant colony optimization algorithm which canobtain the optimal result of the traveling salesman problem.Thus, we use the CMDSOM to provide a performance up-per bound for the MEA. First, we compare the moving dis-tance of MENs using unscheduled scheme (US), MEA, andCMDSOM. Then, the running time of MEA and CMDSOMare studied. To better illustrate the performance, we use thecommunication radius and number of nodes as input variables,respectively.

As shown in Fig. 7(b) and (c), the moving distance of MENsdecrease as the communication radius increases, and increasesas the number of nodes increases. The moving distance of MENsusing MEA is 6.16% and 7.6% less on average than using USin Fig. 7(b) and (c), respectively. The performance of MEAis 7.22% and 7.9% worse than CMDSOM on average. Theexperimental results indicate that although MEA can effectivelyreduce the moving distance of MEN than US, it still has a certaindistance to the upper bound.

Fig. 8(a) and (b) compares the running time of MEA andCMDSOM in different parameters. As the number of nodes andcommunication radius increases, the running time of both MEAand CMDSOM rises and drops. The CMDSOM runs 5.17 and24.4 times as long as MEA in Fig. 8(a) and (b) apiece. Theexperimental results show that MEA has a big advantage onrunning time over CMDSOM.

To verify whether the proposed theorem can effectivelydecrease the moving distance of MEN, Fig. 8(c) uses theintermediary nodes selected by MEA as the input of ant colonyoptimization algorithm. Compared with CMDSOM, the com-bination of MEA and ant colony optimization algorithm caneffectively decrease the moving trajectory of MEN.

Considering the result presented above, MEA can effectivelydecrease the running time without significantly enlarging the



Fig. 8. Influence of different parameters on running time and moving distance. (a) Influence of number of nodes on running time. (b) Influence ofcommunication radius on running time. (c) Influence of number of nodes on moving distance.

moving distance. Meanwhile, the combination of MEA canachieve better performance than that of CMDSOM.

VI. CONCLUSION

SCS-enabled industrial IoT enables users to obtain the abilityof collecting, processing, analyzing, storing, and sharing dataon demand. However, the susceptible property of sensor nodesmay cause many security problems. Therefore, designing aneffective mechanism to ensure the trustworthiness of sensornodes becomes a critical issue. In this article, we introducedMENs to evaluate the trustworthiness condition of sensor nodesand design a corresponding trust evaluation mechanism withthe AI approach. The experimental results indicated that theproposed intelligent trust evaluation mechanism can effectivelydistinguish compromised and malicious nodes and decrease theenergy consumption of the entire network compared with thestate of the art. Meanwhile, we proposed a moving algorithm forMENs. Compared with traditional moving scheme, the proposedmoving algorithm effectively reduced the moving distance ofMENs, and thus, further decreased the energy consumption.

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Tian Wang received the B.Sc. and M.Sc. de-grees in computer science from Central SouthUniversity, Changsha, China, in 2004 and 2007,respectively, and the Ph.D. degree in computerscience from the City University of Hong Kong,Hong Kong, in 2011.

He is currently a Professor with the Collegeof Computer Science and Technology, HuaqiaoUniversity, Xiamen, China. His research inter-ests include wireless sensor networks, fog com-puting, and mobile computing.

Hao Luo received the B.Sc. degree in computerscience and technology from the Civil AviationUniversity of China, Tianjin, China, in 2016. Heis currently working toward the master’s degreewith the College of Computer Science and Tech-nology, Huaqiao University, Xiamen, China.

His research interests include wireless sen-sor networks, mobile computing, and edgecomputing.

Weijia Jia received B.Sc. and M.Sc. degreesfrom Central South University, Changsha, China,in 1982 and 1984, respectively, and M.A.Sc.and Ph.D. degrees from the Polytechnic Facultyof Mons, Hainaut, Belgium, in 1992 and 1993,respectively, all in computer science.

He is currently a Chair Professor, DeputyDirector of State Key Laboratory of Internet ofThings for Smart City, Head of the Center ofData Science, University of Macau, China. Hisresearch interests include smart city, next gen-

eration Internet of Things (IoT), multicast and anycast Quality of Servicerouting protocols, wireless sensor networks, and distributed systems.

Anfeng Liu received the M.Sc. and Ph.D. de-grees in computer science from Central SouthUniversity, Changsha, China, in 2002 and 2005,respectively.

He is a currently Professor with the Schoolof Computer Science and Engineering, CentralSouth University, Changsha, China. His majorresearch interests are cyber-physical systems,service network, and wireless sensor network.

Prof. Liu is a Member of China ComputerFederation.

Mande Xie was born in 1977. He receivedthe Ph.D. degree in circuits and systems fromZhejiang University, Hangzhou, China, in 2006.

He is currently a Professor with the Schoolof Computer Science and Information Engineer-ing, Zhejiang Gongsahng University, Hangzhou,China. His research interests include wirelesssensor networks, social network, and privacypreservation.


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