distributed clustering strategies in industrial wireless...

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2016.2628409, IEEETransactions on Industrial Informatics

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Distributed Clustering Strategies in IndustrialWireless Sensor Networks

Angelo Cenedese, Member, IEEE, Michele Luvisotto, Student Member, IEEE, and Giulia Michieletto

Abstract—Wireless sensor networks (WSNs) can provide nu-merous benefits in industrial automation. By removing the cableinfrastructure, the wireless architecture enables the possibility fornodes in a network to dynamically and autonomously group intoclusters according to the communication features and the datathey collect. This capability allows to leverage the flexibility androbustness of industrial WSNs in supervisory intelligent systemsfor high level tasks such as, for example, environmental sensing,condition monitoring and process automation. In this paper, aclustering strategy is studied that partitions a sensor networkinto a non–fixed number of non–overlapping clusters accordingto communication network topology and measurements distribu-tion: to this aim, both a centralized and a distributed algorithmare designed that do not require a cluster–head structure or othernetwork assumptions. As a validation, these strategies are testedon a real dataset coming from a structured environment and theeffectiveness of the clustering procedure is also investigated toperform anomalies detection in an industrial production process.

Index Terms—Industrial Wireless Sensor Networks (IWSNs),network clustering, distributed algorithms, anomaly detection,environment monitoring.

I. INTRODUCTION

In the competitive industrial marketplace, Industrial Wire-less Sensor Networks (IWSNs) have emerged as a key tech-nology to improve the efficiency of production processeswhile limiting implementation costs. Recent developmentshave led to the realization of tiny and low–cost sensor nodesequipped with data processing and communication capabilitiesand IWSNs incorporate networks of up to thousands of theseautonomous devices to facilitate the realization of highlyreliable and self–healing intelligent systems for heterogeneousapplications in the industrial context [1], [2], [3].

The collaborative nature, rapid deployment and flexibilityof IWSNs bring several advantages over traditional wiredindustrial systems. Specifically, the lack of cables, the supportfor mobility and the low power maintenance make thesesolutions suitable for harsh environmental conditions [4].The existing and potential applications of IWSNs can beclassified according to the taxonomy in [5] into three maincategories, namely environmental sensing, condition monitor-ing, and process automation, and they regard a wide andheterogeneous range of specific scenarios that include building

This work was supported in part by the Italian MIUR Project SEAL -Smart&safe Energy-aware Assisted Living (SCN-00398).

The authors are with the Department of Information Engineering, Universityof Padova, Via Gradenigo 6/B, 35131, Padova, Italy, e-mail: {angelo.cenedese,giulia.michieletto}@unipd.it, [email protected].

Copyright (c) 2016 IEEE. Personal use of this material is permitted.However, permission to use this material for any other purposes must beobtained from the IEEE by sending a request to [email protected].

automation [6], process control [7], utility automation [8],precision agriculture [9], to cite a few.

In all these contexts, there is typically an intelligent unit,be it centralized or distributed, that realizes the principal taskthrough monitoring and control actions, thus requiring real–time information delivery over very large–scale systems. Inthis sense, locality in the communication can, on the onehand, ensure a rapid spreading of the information and, on theother, reduce the data interpretation complexity by filteringout the unnecessary information that regards non neighboringnodes. Hence, grouping nodes into local clusters arises asa fundamental tool to enhance the network self-organizationcapabilities and improve the system autonomicity towards thefulfillment of collective goals.

Besides real–time performance, IWSNs differ from otherwireless sensor networks because of an increased attentiontowards maintainability, reliability, and safety [1], [4]. Inthe industrial environment the networks should be able toguarantee robust operations in often critical scenarios and toensure the safety of personnel, machinery and propriety aswell as fast detection and recovery from malicious externalattacks. Moreover, IWSNs should operate autonomously forsuch process and service monitoring. To this aim, fault de-tection algorithms must be developed that are able to identifysensors and actuators whose operating conditions are differentfrom those expected [10]. In this venue, network partitioningstrategies able to group nodes that exhibit similar behaviorscan serve as a useful tool.

The development of effective clustering strategies specifi-cally tailored for the industrial environment is hence a keyresearch area towards the realization of self–organizing, real–time, robust and secure wireless sensor networks to be de-ployed in industrial applications [11], [12], [13], [14].

A. Related works

The literature on clustering in sensor networks is quite vastand heterogeneous: it involves different concepts of clusteringand covers several disciplines. It is therefore difficult to drawan even fairly complete state of the art: in this respect andwith no aim of being exhaustive, a brief overview of clusteringresults is presented in the following.

As a general distinction, clustering problem in a wirelesssensor network can be tackled by considering the topology(network decomposition) or the data gathered from the en-vironment (data clustering). The first approach is generallyconsidered in the design of communication algorithms andprotocols, where the formation of clusters can lead to higher



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energy efficiency and reduced communication delays [15].On the other hand, data–based network partitioning allowsto reduce the computational load by taking into accountsimilarities among nodes measurements in applications thatdeal with large amounts of information [16].

Several partitioning algorithms have been proposed to ac-complish the clustering task. In particular, the network decom-position problem can be tackled via well known procedures,which can be divided into heuristic, weighted, hierarchical andgrid schemes. A comprehensive overview can be found in [17]and the references therein. Differently, in data clustering thefocus is moved from the topology of the information sourcesto the information flow itself. Most techniques are based onthe spatio–temporal correlation properties of the data gatheredby the sensors [18]. In all these works, one solution appearsas particularly popular, in which nodes are partitioned intoclusters and some of them, called cluster-heads (CHs), takethe crucial role of aggregating the data coming from the otherelements in the cluster and forwarding them to the base station.

The interest around clustering strategies for general purposewireless sensor networks is well established; conversely, onlyrecently the application of these techniques to IWSNs hasbeen considered. The work in [11], for example, presents twoclustering approaches to realize tracking of mobile nodes inIWSNs, a static one where clusters have a predefined numberof elements and a dynamic one, which achieves the task moreefficiently. The application of clustering strategies to solveenvironmental sensing and conditional monitoring tasks is thesubject of [12], where a real–time clustering algorithm isapplied to an industrial monitoring network to achieve riskassessment in an energy–efficient way. The detection of faultand anomalies in industrial sensing or process applications isanother problem that can be tackled via clustering strategies.The work in [13] first adopts a well known network decompo-sition algorithm, Low Energy Adaptive Clustering Hierarchy(LEACH) [17], then implements a fault detection algorithmthat exploits the presence of clusters. The authors of [14],instead, develop an original clustering algorithm (DistributedMatching–based Grouping Algorithm, DMGA) to partitionthe network into strongly correlated groups of at least apredefined number of nodes; on this basis, then, a GeneralAnomaly Detection (GAD) distributed procedure is developedthat exploits data correlation for real–time recognition ofanomaly conditions. Again, all these approaches rely on thepresence of CHs to facilitate the clustering task.

B. Contribution of the Paper

This paper deals with clustering strategies in IWSNs. Dif-ferently from other approaches proposed in the literature,network decomposition and data clustering are here consideredtogether, since both these aspects are crucial in ensuring theperformance standards of industrial communications. More-over, the proposed approach is fully distributed and does notrely at all on the presence of CHs, yielding considerablebenefits in terms of scalability and robustness.

The first contribution of this paper is to provide a formaldefinition of the clustering task over a given IWSN, stated as

the determination of the unique partition that simultaneouslyabides by three different criteria. Firstly, for each pair of nodesin a cluster there must exist at least one (communication) pathconnecting them composed exclusively by elements includedin the same cluster (connectivity criterion). Secondly, for eachnode in a cluster there must exist at least another one in thesame cluster such that the two nodes measurements are similaraccording to a defined metric (measurements similarity crite-rion). Finally, the network must be partitioned into the minimalnumber of non–overlapping clusters (maximality criterion).

The most important and original contribution of this workis to provide algorithms which solve the aforementionedclustering task. Specifically, the partitioning problem is solvedin the first instance through a centralized policy and then thedistributed paradigm is considered to provide an algorithmcapable of partitioning the network only by local explorationof measurements. In both cases, no CHs assignment is con-sidered, and no assumptions are made on the structure of theemerging clusters (other than those implied by the partitioningcriteria), which are uniquely determined by the proposedalgorithms. The convergence of the distributed solution tothe centralized one is showed through a numeric exampleand the efficiency of the proposed approach is confirmed bycomparing it with other clustering techniques, i.e., a classicalk-means strategy [19] and the most recent DMGA algo-rithm [14]. Finally, the applicability of the proposed procedureto real industrial use cases is demonstrated by experimentaland simulated scenarios. As a first example, the distributedclustering algorithm is employed to perform anomaly detectionin a simulated industrial production process. Subsequently,the performance of a fault detection algorithm based on thisprocedure are evaluated on an environmental sensing datasetand compared with those offered by the state–of–the–art GADalgorithm, that serves the same purpose.

The remainder of the paper is organized as follows. SectionII formally describes the task to achieve. Section III introducesboth the centralized and the distributed algorithms to solve theclustering problem, with a first validation against k-means andDMGA. Then, Section IV validates the proposed distributedclustering strategy in real–world industrial use cases. Finally,Section V presents the main conclusions of this work and somefuture directions of research.

II. CLUSTERING IN AN IWSN:DEFINITION AND NOTATION

The clustering procedure proposed in this paper is definedover an IWSN composed by N nodes, whose topology isrepresented by an undirected graph G = (V, E), where Vis the set of nodes (vertexes) and E is the set of commu-nication links (edges) among them. In this framework, eachnode vi is associated with a measurement mi, gathered fromthe environment and stacked into the measurement vectorm =

[m1 . . . mN

]>.

Given G and m, the clustering task consists in identifyingthe node clusters {C`} that constitute the unique partition of V ,defined as C = {C1, . . . , Ck} satisfying the following criteria:C1. Connectivity: ∀ C` ∈ C, ∀ vi, vj ∈ C` ∃ path p =

{v1, . . . , vh, . . . , vn} such that v1 = vi and vn = vj ,(vh, vh+1) ∈ E and vh ∈ C` ∀ h ∈ [1, n− 1].



3

1

2

3

4

56

Ca1

Ca2

Ca3

(a) Partition Ca with 3 clusters (C1-C2)

1

2

3

4

56Cb1

Cb2

Cb3

(b) Partition Cb with 3 clusters (C1-C2)

1

2

3

4

56

Cc1

Cc2

(c) Partition Cc with 2 clusters (C1-C2-C3)

Fig. 1. Example 1: clustering on a 6–nodes network. The vector measurement for nodes {v1, . . . , v6} is m = [10 12 13 20 22 11]>.

C2. Measurements similarity: ∀ C` ∈ C, ∀ vi ∈ C` ∃ vj ∈C` such that ‖mi −mj‖ ≤ b, according to some norm;b ∈ R is named as clustering bound.

C3. Maximality: let C(i) = {C(i)1 , . . . , C(i)ki}, i ∈ N be a

generic partition of the network satisfying criteria C1–C2, the maximal partition is C? = {C1, . . . , Ck?}, where

k? = arg mini∈N

[ki]

The elements of the obtained partition C1, . . . , Ck? are denotedas optimal clusters and the function F : V → C? is introducedthat maps each node to the optimal cluster it belongs to in theoptimal partition.

Remark 1. (Optimal) clusters are non–overlapping and coverthe entire network, i.e., any node in V belongs to one and onlyone cluster following C1-C2-C3. This results in the functionF to be bijective.

Looking at the three criteria, criterion C1 requires that eachcluster forms a connected subgraph: in practice, in industrialapplications, this means that the measurements and more ingeneral the data obtained from nodes within the same clusterare shared. Criterion C2, instead, states that a sort of similarityexists among the measurements of nodes in a cluster: fromthe application point of view, the clustering bound b is a setupparameter related to the expected variance in the measurementsrange. Finally, since there can be several network partitionswhich fulfill C1 and C2, condition C3 is introduced to selectthe partition composed by the minimum number of clusters,i.e. the one wherein the cardinality of each cluster is maximal.This ensures that the defined partition is unique, apart frompathological cases.

Example 1. In order to provide an example of the presentedclustering task and to show the uniqueness of the definedpartition, a network composed by 6 nodes is considered inFig. 1. The following (scalar) measurement vector is assumed,m =

[10 12 13 20 22 11

]>, with a clustering bound

chosen as b = 2, which corresponds to the admitted standarddeviation for a correct measurement. Figs. 1a–1b show twopartitions of the network, Ca and Cb, both composed bythree clusters, that satisfy criteria C1 and C2. Nevertheless, afurther partition Cc with only two clusters can be identified,as reported in Fig. 1c, which also abides by C1 and C2, andmaximizes the cardinality of the clusters (C3).

Interestingly, with respect to C1 and C2 only, all these

partitions show a cluster that includes exclusively nodes v4and v5 due to the similarity between their measurements andthe dissimilarity with the other nodes values; conversely, theremaining nodes show a higher level of measurement simi-larity among them and can be grouped according to differentconnectivity graphs that are identified in the network. From abuilding intelligence perspective, this suggests the possibilityof detecting and isolating faulty nodes (in this case v4 and v5)or anomalous events through the clustering procedure [20].

III. CLUSTERING ALGORITHMS

In this section two clustering algorithms are presented: theformer is designed through a centralized approach, while thelatter is achieved according to the distributed paradigm. Bothstrategies converge to the same solution providing an optimalnetwork partition with respect to the established criteria.

A. Centralized Clustering Algorithm (CCA)

In the framework introduced in Sec. II, it is possible to solvethe clustering task through the centralized procedure reportedin Alg. 1. The inputs of the algorithm are the measurementvector m, the clustering bound b and the (symmetric) adja-cency matrix E ∈ RN×N of the network, which providesinformation about the communication links derived from E ,i.e., (vi, vj) ∈ E ⇔ E[i, j] = 1. The outcome of the algorithmis the set {C(vi)}Ni=1 which is related to the optimal partitionC? defined in Sec. II, through the neighborhood of each nodevi, Ni = {vj ∈ V | E[i, j] = 1}, according to the relationC(vi) = Ni ∩ F(vi).

The proposed solution relies on the dynamic update of theterms B`

i and Bui that indicate a lower and upper bound,

respectively, associated to each node vi. The initial part ofthe algorithm is devoted to the setup phase (rows 2-5): foreach node vi, C(vi) = {vi}, while the two bounds B`

i and Bui

are defined basing on the initial node measurement mi andthe imposed clustering bound b. The remaining part of Alg. 1consists of two steps that are repeated iteratively.

1) Inclusion of nodes in clusters (rows 8-16): for each nodevi, all the neighbors not already included in C(vi), i.e.,vj /∈ C(vi) such that E [i, j] = 1, are explored. Neighborvj is added to cluster C(vi) only if the measurementssimilarity criterion (C2) is fulfilled.

2) Update of bounds (rows 17-32): in the second step, foreach cluster C(vi) whose cardinality is larger than 1,



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Algorithm 1 CCA1: procedure CCA(m,b,E)2: term← false3: C(vi)← {vi} ∀ i4: B`

i ← mi − b ∀ i5: Bu

i ← mi + b ∀ i6: while not term do7: update← false8: for i← 1 to N do9: for j ← 1 to N do

10: if E [i, j] = 1 and vj /∈ C(vi) then11: if ∃ vh ∈ C(vi) : B`

h ≤ mj ≤ Buh then

12: C(vi)← vj13: end if14: end if15: end for16: end for17: for i← 1 to N do18: if |C(vi)| > 1 then19: B`

min ← minvk∈C(vi)

B`k

20: Bumax ← max

vk∈C(vi)Bu

k

21: for each vj ∈ C(vi) do22: if B`

j > B`min then

23: B`j ← B`

min

24: update← true25: end if26: if Bu

j < Bumax then

27: Buj ← Bu

max

28: update← true29: end if30: end for31: end if32: end for33: term← not update34: end while35: end procedure

two quantities are computed, namely the minimum lowerbound B`

min and maximum upper bound Bumax

B`min = min

vk∈C(vi)B`

k, Bumax = max

vk∈C(vi)Bu

k .

Then, every node in the cluster updates its bounds ac-cordingly, i.e., B`

k = B`min, Bu

k = Bumax ∀ vk ∈ C(vi).

The procedure ends when the second step does not produceany update of node bounds and, therefore, the cardinality ofeach cluster coincides with that of the previous iteration step.

Remark 2. It can be observed that by adopting CCA, criteriaC1, C2 and C3 are actually fulfilled. Indeed, a generic nodevj is inserted in cluster C(vi) through the instructions in rows10-14. Specifically, this happens only if vj is the neighbor ofanother node vh in the same cluster, i.e., E[j, h] = 1, vh ∈C(vi). At the same time, it is ensured that vj is inserted inthe cluster C(vi) only if ∃ vh ∈ C(vi), s.t. B`

h ≤ mj ≤ Buh ,

thus satisfying C2. Finally, Alg. 1 stops only when there are nomore bounds update ensuring that the corresponding partitionhas the lowest possible number of clusters, fulfilling C3.

It can be proved that the proposed procedure has complexityO(N3). Indeed, the instructions inside the while loop requireO(N2) computations, due to the two nested for loops, whereasthe while loop is executed N times in the worst case. This

Algorithm 2 DCA1: procedure DCA(mi ,b,N (i))2: active(vi)← false3: B`

min ← minvh:ch=ci

B`h

4: Bumax ← max

vh:ch=ciBu

h

5: for each vj ∈ N (i), cj 6= ci do6: if B`

i ≤ mj ≤ Bui then

7: cj ← min (ci, cj)8: ci ← cj9: if B`

min > B`j then

10: B`min ← B`

j

11: end if12: if Bu

max < Buj then

13: Bumax ← Bu

j

14: end if15: active(vi)← true16: active(vj)← true17: end if18: end for19: if B`

min < B`i or Bu

max > Bui then

20: B`i ← B`

min

21: Bui ← Bu

max

22: active(vi)← true23: end if24: for each j ∈ N (i), cj = ci do25: if B`

min < B`j then

26: B`j ← B`

min

27: active(vj)← true28: end if29: if Bu

max > Buj then

30: Buj ← Bu

max

31: active(vj)← true32: end if33: end for34: end procedure

situation happens when the sensors form a line graph, i.e.E[i, j] = 1 ⇔ |i − j| ≤ 1, and are provided with evenlyspaced measurements at distance b, i.e., mi = (i − 1) · b. Inthis scenario, the upper bound of node v1 (which is the last oneto converge together with vN ) at the k-th iteration is given byBu

1 = (k+1) ·b and reaches the maximum value of N ·b onlyat iteration k = N − 1. The algorithm hence converges onlyat the subsequent iteration, thus yielding an overall O(N3)complexity. However, it should be noted that this worst–casescenario is quite uncommon to find in practice and thereforethe complexity is generally lower.

B. Distributed Clustering Algorithm (DCA)

The centralized approach of CCA is based on the assump-tion that all nodes measurements are available at the sametime together with the network topology at a single location.Although this statement might be true for some networkconfigurations, it is not verified in a generic IWSN, and adistributed paradigm that relies only on local communicationexchange is generally preferable. Moreover, the decentralizedstrategy results to be more robust to node failures and dynamicnetwork topology modifications, which is a valuable feature inan industrial environment characterized by noise sources thatmay impair communication and faults due to the applicationto critical operational scenarios.



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The proposed distributed algorithm (DCA) is reported inAlg. 2 where the iterative nature of the procedure regards theexecution of the same instructions by each node vi of thenetwork until the unique partition C? that fulfills criteria C1,C2 and C3 is determined. To this aim, a label ci is associatedto each node vi to specify the cluster to which it belongs to(at the beginning ci = i). This variable is updated during thealgorithm execution, so that the output of the whole procedureis a set of labels, one for each node, describing the partitionof the network, in the sense that ci = cj ⇔ F(vi) = F(vj).As in the centralized solution, a node vi is also associated toa lower and an upper bound, B`

i and Bui respectively, that are

initialized as in Alg. 1.DCA is again based on an iterative exploration of the

neighbors, performed by each node in a distributed manner.In detail, node vi checks the measurement of each node vjbelonging to its neighborhood Ni under the constraint cj 6= ci,meaning that they do not belong to the same cluster. If the twomeasurements are similar, according to criterion C2, then boththe labels ci and cj are set to min(ci, cj) (rows 6-8). Moreover,node vi keeps track of the minimum and maximum valuesassumed by B`

k and Buk respectively (rows 10-15) for any

compatible neighbor vk, in order to update its own bounds atthe end of neighbors exploration (rows 19-23). Subsequently,a further exploration of all the neighbors of vi that belongto its same cluster, i.e. cj = ci, is performed to updatetheir bounds accordingly (rows 24-33). The node vi stopsto perform the iterative execution of this algorithm when itsflag active becomes false. This label is initialized to falseat the beginning of the procedure and set to true in threepossible cases: a new compatible neighbor is found, the nodeis included in another cluster or its bounds are updated.

The rationale behind DCA resides in the iterative boundsupdate and nodes inclusion into clusters, similarly to that ofCCA (Alg. 1); remarkably, though, DCA exploits only localinformation to attain the network partition and it is executedlocally by each node, without requiring a central controller.Notably, for a given network and set of measurements, thetwo algorithms produce the same partition, the one defined inSec II, as confirmed by extensive numerical simulations.

Example 2. Consider a synthetic IWSN composed of N = 100nodes whose communication graph is described by a randomgeometric graph [21], whereas measurements are randomlydrawn from a uniform distribution with range between 0and 100, mi ∈ U ([0, 100]), and the clustering bound is setto b = 5. One realization of such scenario is reported inFig. 2 where both the network topology and the convergencebehavior of DCA in terms of number of clusters are shown.The initialization of DCA corresponds to the creation of onecluster per node and the iterative procedure makes the numberof clusters decrease as nodes are merged together, according tothe update of the node bounds. In this context, a step is definedas the full execution of Alg. 2 by every node in the network.As expected, the result of the distributed procedure convergesto the centralized solution after three steps. Specifically, in thereported case, the network optimal partition C? is constitutedby 2 clusters.

(a)Number of iterations

0 1 2 3 4

Num

ber

of c

lust

ers

0

20

40

60

80

100CCADCA

(b)

Fig. 2. Example 2: (a) topology of the IWSN as a random geometric graph;(b) convergence of the DCA solution to the CCA one.

C. First Assessment of DCA

Here, the performance of the distributed partitioning strat-egy DCA is compared with two other procedures: the k–meansclustering approach (hereafter, KMC) and the DMGA strategy,described in Sec. IV-A of [14].

The evaluation is conducted on a network made of Nnodes ranging from 8 to 100, organized into NC clustersof fixed size equal to 4 (hence, NC spans from 2 to 25).The measurement of a generic node in the i–th cluster isuniformly distributed in the range [Ti − b/2, Ti + b/2], whereTi is the cluster average measurement and it is generated asTi = T0+(i−1)·2b, i = 1, . . . , NC (b is the clustering bound).Two different network topologies are considered: in one case,a random geometric graph is chosen within each cluster andany two clusters are connected by one link between two CHnodes with probability pL = 0.9 (Fig. 3a); in the other case,instead, the communication network is given as a completegraph (Fig. 3b). In both cases, the number of misclassifiednodes is considered versus the network size. Indicating withC? the optimal partition yielded by CCA and with C a genericpartition, the misclassification cost function is given by

d (C, C?) =N∑i=1

χi,

where χi is a function that is equal to zero if the set of clusterelements of node vi in partitions C and C? coincides (node viis correctly classified) and it is equal to one otherwise (nodevi is misclassified).

For KMC, the parameter k is set equal to NC and thecentroids are randomly inizialized. For DMGA, instead, thecorrelation coefficient cij is defined as the ratio between thedistance of two measurements and the measurements range,i.e.,

cij = 1− |mi −mj |b (2 ·NC − 1)

.

Also, the DMGA parameters have been chosen equal toNmin = 4 and cmin = 1− (2 ·NC − 1)

−1.Fig. 3 shows how the performance of KMC are totally inde-

pendent of the network topology as such strategy is exclusivelybased on the similarity of the measurements and does nottake into account the communication links. Specifically, thefact that the algorithm neglects the structure of the networkand the consequent totally random initialization of centroidscause KMC to get stuck on local optima, which results in



6

20 40 60 80 100Number of network nodes

0

20

40

60

80

100N

odes

inco

rrec

tly c

lass

ified

DCADMGAKMC

(a)

20 40 60 80 100Number of network nodes

0

20

40

60

80

100

Nod

es in

corr

ectly

cla

ssifi

ed

DCADMGAKMC

(b)

Fig. 3. Node classification comparison: (a) not fully connected networktopology; (b) fully connected network topology.

the significant percentage of misclassified nodes observablein Fig. 3. On the contrary, applying the DMGA procedure,the set of nodes that are incorrectly classified is empty whenthe network is modeled through a complete graph, while asignificant number of nodes is not classified appropriatelywhen a less connected communication structure is adopted.Instead, and most remarkably, the partition provided by DCAalgorithm coincides with the optimal one in both cases, thanksto the fact that the proposed procedure considers at thesame time both the IWSN connectivity properties and themeasurements similarity. Moreover, as explained in Secs. III-Aand III-B, CCA and DCA always yield the same clusteringsolution, which is equal to the optimal one defined in Sec. II.

IV. APPLICATION TO THE INDUSTRIAL SCENARIO

Many practical applications of the clustering procedure canbe envisaged within the context of IWSNs. They range fromthe fault detection along a production line to the monitoringof a structured environment in building or factory automation,from the tracking of mobile nodes in a productive industrialarea to the optimization of energy resources for a surveillancesystem. In this sense, two different real world scenarioshave been considered for validation of the proposed DCA,specifically:#1 a factory process line, where an item undergoes several

production stages on its way from raw material to endproduct;

#2 a structured indoor environment, wherein the task is thatof indoor monitoring, since building energy managementissues may arise and anomaly detection is also important.

Scenario #1 concerns condition and monitoring of highlydynamic processes in modern factory facilities, where thetiming behaviors of the control variables and of the quantitiesof interest need to be accurately monitored in order to ensureefficiency, performance and quality to the process/service. Afactory intelligence unit should manage to follow the prod-uct/process chain, to identify the different stages, and to detectpossible faults and anomalies that may occur.

Indeed, this issue can be experienced in a large varietyof production plants and processes. Just to provide a cou-ple of examples: in the context of the food industry, thetraceability of the product and the proper management ofthe ambient conditions throughout the whole supply chainare of paramount importance to guarantee the quality andsafety of food products and to extend their shelf life [22]. In

semiconductor manufacturing, the development of intelligentmonitoring systems based on IWSN solutions can increase theautomation and maintainability of such complex process andequipments, thus leading to real–time problem diagnostics andproduction optimization procedures [23].

Scenario #2 is characteristic of many industrial, commercial,and public service installations, and refers to environmentalsensing and service monitoring. In particular, service monitor-ing aims at offering to the end–users a designed (or expected)quality of service, and proposing to the providers a tool tocontrol and optimize the use of resources and increment theawareness of their employment. These instances are strictlyrelated to building automation, which has received a surgeof attention in the last few years towards the deployment ofgreen building solutions in the industry [5], [8]. Environmentalsensing, instead, is referred to the task of measuring quantitiesthat can be only partially controlled but are fundamental forthe efficiency of equipment and operators, to detect pollution,avoid hazard and ensure security, and also yield comfort inthe workspace [18].

In detail, the focus in this section is posed on the validationof the distributed strategy DCA, since it has already beenshown to converge to the centralized solution, and it representsthe most interesting approach for practical applications due toits intrinsic robustness and flexibility. Thus, the performanceof the proposed partitioning procedure is studied with respectto different kind of faults in the first scenario, while it iscompared with those of KMC and of DMGA/GAD algorithmsin the second case.

A. Numerical Validation: Process Monitoring Application

This scenario is schematically represented in Fig. 4, wherea plant with multiple production stages is considered, anda number of items move along the production line. Eachstage is assumed to be characterized by a specific value ofan observable physical quantity (e.g., temperature, pressure,vibration, or a combination of heterogeneous variables): in thisrespect, a gradient of such quantity can be measured acrossthe different production phases and a monitoring IWSN is in-stalled, made up by different subnetworks, each correspondingto a distinct production phase. Also, the item that is undergoingthe production process is equipped with or accompanied by awireless sensor, so that its own local measurement can be takenalong the production line.

In correctly working conditions, the fixed sensors wouldbe grouped into four clusters, corresponding to the differentproduction stages, and the mobile node associated to theitem can be inserted into a specific cluster, meaning that itis undergoing the corresponding stage, or grouped by itself,when traveling from one stage to another. It follows that,since the expected outcome of the clustering procedure fora mobile node is a priori known, any deviation from suchbehavior can be seen as an anomaly and detected by anintelligent supervisory system, which may be distributed aswell. In this context, DCA is able to keep track of theitem state and detect possible anomalies, given the fact thatit takes into account both communication network topology



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Fig. 4. Application scenario #1: sample industrial line–production process characterized by L = 4 stages (gray boxes) each monitored by Nl = 4,l = 1, . . . , L sensors (white numbered circles), and several moving items (depicted as red squares), which are associated to measurement sensors.

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Fig. 5. Application scenario #1: system behavior in pres++ence of anomalies of different kind, i.e. (a) measurement fault, (b) timing mismatch and (c)communication error. For each anomaly, it is reported the measurements of fixed nodes (FN) and mobile node (MN) in the top row, the clustering trends inthe middle row, and the fault detection signal in the bottom row.

and measurements distribution. This detection can be actuallybased on a simple cluster label comparison, by opportunelychoosing the IDs for the fixed and the mobile nodes. Withreference to the example in Fig. 4, all fixed sensors have alower ID with respect to the mobile nodes: since any nodecan retrieve the cluster it is included in by looking at its thecluster label (that assumes the value of the lowest node ID inthe same cluster), a mobile node can recognize if it is groupedalone (cluster label equal to its ID) or grouped in a specificcluster (cluster label lower than its ID). Consequently, since theevolution of its cluster label over time can be a priori stored inthe node’s memory, the node itself can autonomously detect ananomaly when the actual evolution differs from the expectedone.

The application of DCA to this framework allows to identifyseveral different anomalies. In particular, in this context thefollowing ones are considered:

• Measurement fault: the value of the observed quantitymeasured by the mobile node at one stage is significantlydifferent from that expected. In this case, the nodeclusters by itself when it should be clustered with thestage nodes.

• Timing mismatch: the mobile node reaches a certainstage earlier or later than expected. In this case, theevolution of the cluster label is anticipated or delayedwith respect to the nominal trend.

• Communication fault: the mobile node experiencescommunication loss and is no longer able to exchangemessages with other nodes. In this case, the node isalways grouped by itself.

The intelligent supervisory system behavior in presence ofthese types of anomalies has been simulated for differentscenarios, with multiple fault instances and increasing networkcomplexity ranging from tens to thousands of sensor nodes:

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the results of these numerical experiments consistently showthat the proposed method always allows to detect the differentkinds of anomalies with no occurrence of false positives.

An example in this sense is reported in Fig. 5 for a mobilenode that experiences a failure, in the scenario representedin Fig. 4: here, the observed physical quantity is the processtemperature and L = 4 production stages are characterizedby temperature ranges ∆T1 = [5, 10]

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C, while the initialtemperature measured by the mobile node is below 0 ◦C(temperature of the raw material before the process). A valueof b = 4 ◦C has been selected as the clustering bound.The resulting system behaviors (actually, referring to onerealization of such scenario) are shown in terms of measure-ments trends, clusters evolution, and fault detection signal.This latter signal is obtained as the mismatch (computed inpractice via logical XOR) between the actual clusters evolutionvalue with the reference expected one. Interestingly, in thecase of communication fault, namely loss of signal from themonitoring mobile station, the measurement evolution of themobile node is statistically close to the reference one, to thepoint that no anomaly can be identified by looking only at themeasurements, while the cluster label comparison promptlyreveals the anomaly.

B. Experimental Dataset: Environmental Sensing ApplicationThe DCA approach has also been applied to a dataset

coming from a sparse sensor network deployed in a publicindoor environment characterized by heterogeneous usagezones, with reference to Fig. 6a–6b, respectively Area 1to 4. The considered monitoring network is composed byN = 17 wireless t-mote nodes [24] allocated in 4 differentareas composed of multiple rooms and a sample connectionis assumed as shown in the graph reported in Fig. 6a. Thisdataset has been derived from a 4 months monitoring periodthat includes weekends and holidays; in detail, each sensormeasures a temperature with a fixed sampling interval of fiveminutes.

In such a context, the application of DCA to a set of staticmeasurements collected at a specific time instant (e.g. at 12a.m. of a generic weekday) with a suitable clustering bound byields a network partition such that there is a two–way corre-spondence between clusters and areas, as reported in Fig. 6a.Indeed, this cluster–area correspondence is not achieved bypartitioning the network through KMC and DMGA strategies(see Fig. 6b). On the one hand, even if with k = 4 KMCidentifies four clusters, they do not coincide with the differentareas of the building, mainly because of the reduced signalvariability in the whole environment. On the other hand, theimplementation of DMGA with Nmin = 2 to allow for theidentification of small groups, leads to seven clusters becauseof the network sparsity. Remarkably, DCA strategy can handleboth of these aspects correctly detecting the four areas, whichare characterized by a specific measurement behavior and,hence, may be managed in a dedicated way by an intelligentenvironment controller.

Static data processing, however, may be not informativeenough for a building management system with the aim

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Fig. 6. Application scenario #2: (a) DCA network partition; (b) KMC(green) and DMGA (blue) network partitions.

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of energy profile optimization and efficiency. In this sense,the application of the clustering algorithm during a largeobservation interval allows to extract general trends and tobuild an effective anomaly detection strategy. In order tovalidate this claim, anomalous values are artificially insertedin the measurements collected by the network during 8 weeksof operations. In particular, two anomaly models are hereconsidered [25]:

• constant: the sensor reports a constant value that corre-sponds to the measurement at the beginning of the faultperiod multiplied by a factor γ = 2;

• noise : the sensor measurement is affected by an additiveGaussian noise with zero mean and standard deviationσ = 10◦C.

Different anomaly occurrence rates have been considered andthe duration of each fault is a uniform random variable withan average value of 12 samples (1 hour)

In this framework, a simple yet effective fault detectionstrategy based on DCA is adopted: the algorithm runs in asupervised manner during the first week of operation, withoutany fault, and, at each sample, every node stores the list ofits neighbors, i.e., the nodes in its same cluster. Then, in thefollowing weeks, DCA is applied at any new measurements(with a possible presence of faults) and each node comparesthe detected neighbors with those stored for the correspondinginstant of the training week: if less than 50% of currentneighbors are not in the list, the node self–declares as faulty.

To evaluate the performance of this strategy against a state–of–the–art anomaly detection scheme, GAD algorithm [14] isapplied to the same dataset. The anomaly detection phase startsafter the first week and the following parameters are used:



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Nmin = 2, cmin = 0.5 and ∆t = 90 samples (7.5 hours). Ithas to be noted that the choice of setting the minimum numberof cluster elements to 2 is imposed by the considered scenario,characterized by relatively few nodes, sparsely connectedand with a significant measurement variability. However, thischoice strongly affects the detection capabilities of GAD,which generally requires denser clusters. To cope with thisissue, the algorithm has been slightly modified, labeling asfaulty all the nodes whose status is uncertain. This conservativechoice is motivated by the significant robustness required byindustrial sensing applications, which translates into the urgeto detect as many faults as possible, enduring a possible highoccurrence of false positives. Following a similar reasoning,the bound of the DCA algorithm is set to an adequately lowvalue (b = 2◦C), so as to privilege detection of faults withrespect to false positives.

The performance comparison between the two anomalydetection strategies is shown in Fig. 7 for several values ofoccurrence rate. Fig. 7a reports the percentage of anomaliesdetected on the total number of generated ones. The detectionstrategy based on DCA outperforms GAD for every valueof anomaly rate, yielding an accuracy always greater than85%, while using the other strategy it drops below 60% fora fault rate of 20%. To highlight how both strategies sufferfrom the presence of false positives due to the significantirregularity of the measurement trends, the F–score, a metricthat sums up accuracy and precision, is depicted in Fig. 7b.It can be observed that this metric is low, especially forlow anomaly occurrence rates, where the incidence of falsepositives is significant. However, also from this point of view,the anomaly detection strategy based on DCA confirms itsvalidity, outperforming GAD by a wide margin.

V. CONCLUSIONS AND FUTURE WORKS

Within the framework of distributed intelligent systems, thispaper aims at designing strategies to effectively partition anIWSN into non–overlapping clusters of nodes.

To this purpose, three clustering criteria are proposed, thattake into account both communication network topology andthe measurements gathered by the sensor nodes. Indeed, thesefeatures results to be important in noisy industrial environ-ments, where both the network connectivity and the mea-surement consistency concur to guarantee IWSN performancein terms of timeliness, reliability and security. In order toaccomplish this task, two clustering strategies are proposedfollowing either a centralized or a distributed approach, theformer (CCA) relying on the presence of a central coordinatingunit, the latter (DCA) employing the network itself as acomputational grid, without the need of identifying CHs.

Effectively, the distributed solution converges to the cen-tralized one after some iterations and hence emerges as thepreferred one for IWSNs thanks to the inherent properties ofautonomicity, scalability and robustness. The proposed DCAprocedure is then tested both in numerical simulations andon a real–world dataset, to provide an assessment of itsperformance in environmental monitoring and fault detectionapplications employed in building and process automation.

In this evaluation, the performance of DCA are comparedwith both a classical k-means approach and a most recentprocedure, leading to conclude that the proposed algorithmoutperforms other approaches in accomplishing the clusteringtask and can be used, for example, as a basis to developefficient anomaly and fault detection strategies to be employedin intelligent industrial monitoring applications.

Future research directions will be focused on the real–timeimplementation of the proposed clustering algorithms in atestbed wireless sensor network to investigate the practicalchallenges that could arise from their adoption. Moreover,future developments include the theoretical and simulativecomparison of the proposed methods in more specific sce-narios, as well as the deployment of such procedures in actualIWSN applications.

REFERENCES

[1] V. Gungor and G. Hancke, “Industrial wireless sensor networks: Chal-lenges, design principles, and technical approaches,” IEEE Transactionson Industrial Electronics, vol. 56, no. 10, pp. 4258–4265, Oct 2009.

[2] G. Hancke, V. Gungor, and G. Hancke, “Guest Editorial Special Sec-tion on Industrial Wireless Sensor Networks,” IEEE Transactions onIndustrial Informatics, vol. 10, no. 1, pp. 762–765, Feb 2014.

[3] P. Leitao, V. Marik, and P. Vrba, “Past, present, and future of industrialagent applications,” IEEE Transactions on Industrial Informatics, vol. 9,no. 4, pp. 2360–2372, Nov 2013.

[4] J. Akerberg, M. Gidlund, and M. Bjorkman, “Future research challengesin wireless sensor and actuator networks targeting industrial automa-tion,” in 9th IEEE International Conference on Industrial Informatics(INDIN), July 2011, pp. 410–415.

[5] V. C. Gungor and G. P. Hancke, Industrial Wireless Sensor Networks:Applications, Protocols, and Standards, 1st ed. Boca Raton, FL, USA:CRC Press, Inc., 2013.

[6] F. Osterlind, E. Pramsten, D. Roberthson, J. Eriksson, N. Finne, andT. Voigt, “Integrating building automation systems and wireless sensornetworks,” in IEEE International Conference on Emerging Technologiesand Factory Automation (ETFA), Sept 2007, pp. 1376–1379.

[7] J. Chen, X. Cao, P. Cheng, Y. Xiao, and Y. Sun, “Distributed collabora-tive control for industrial automation with wireless sensor and actuatornetworks,” IEEE Transactions on Industrial Electronics, vol. 57, no. 12,pp. 4219–4230, Dec 2010.

[8] E. Fadel, V. Gungor, L. Nassef, N. Akkari, M. A. Maik, S. Almasri, andI. F. Akyildiz, “A Survey on Wireless Sensor Networks for Smart Grid,”Computer Communications, vol. 71, no. C, pp. 22–33, Nov. 2015.

[9] S. Ivanov, K. Bhargava, and W. Donnelly, “Precision farming: Sensoranalytics,” IEEE Intelligent Systems, vol. 30, no. 4, pp. 76–80, July 2015.

[10] J. Neuzil, O. Kreibich, and R. Smid, “A distributed fault detection systembased on IWSN for machine condition monitoring,” IEEE Transactionson Industrial Informatics, vol. 10, no. 2, pp. 1118–1123, May 2014.

[11] M. Gholami and R. W. Brennan, “Comparing two clustering-basedtechniques to track mobile nodes in industrial wireless sensor networks,”Journal of Systems Science and Systems Engineering, vol. 25, no. 2, pp.177–209, 2016.

[12] X. Ding, Y. Tian, and Y. Yu, “A Real-Time Big Data GatheringAlgorithm Based on Indoor Wireless Sensor Networks for Risk Analysisof Industrial Operations,” IEEE Transactions on Industrial Informatics,vol. 12, no. 3, pp. 1232–1242, June 2016.

[13] W. Zhang, G. Han, Y. Feng, L. Cheng, D. Zhang, X. Tan, and L. Fu,“A Novel Method for Node Fault Detection Based on Clustering in In-dustrial Wireless Sensor Networks,” International Journal of DistributedSensor Networks, vol. 2015, Jan. 2015.

[14] P.-Y. Chen, S. Yang, and J. McCann, “Distributed real-time anomalydetection in networked industrial sensing systems,” IEEE Transactionson Industrial Electronics, vol. 62, no. 6, pp. 3832–3842, June 2015.

[15] M. M. Afsar and M.-H. Tayarani-N, “Clustering in sensor networks:A literature survey,” Journal of Network and Computer Applications,vol. 46, pp. 198 – 226, 2014.

[16] I. Eyal, I. Keidar, and R. Rom, “Distributed data clustering in sensornetworks,” Distributed Computing, vol. 24, no. 5, pp. 207–222, 2011.



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[17] A. A. Abbasi and M. Younis, “A survey on clustering algorithms forwireless sensor networks,” Computer Communications, vol. 30, no.1415, pp. 2826 – 2841, 2007.

[18] Y. Ma, Y. Guo, X. Tian, and M. Ghanem, “Distributed clustering-basedaggregation algorithm for spatial correlated sensor networks,” IEEESensors Journal, vol. 11, no. 3, pp. 641–648, March 2011.

[19] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of StatisticalLearning: Data Mining, Inference, and Prediction, Second Edition, ser.Springer Series in Statistics. Springer, 2009.

[20] G. Bianchin, A. Cenedese, M. Luvisotto, and G. Michieletto, “Dis-tributed fault detection in sensor networks via clustering and consensus,”in 54th IEEE International Conference on Decision and Control (CDC),Dec 2015, pp. 3828–3833.

[21] M. Penrose, Random geometric graphs, ser. Oxford studies in probabil-ity. Oxford, New York: Oxford University Press, 2003.

[22] S. Piramuthu and W. Zhou, RFID and Sensor Network Automation inthe Food Industry: Ensuring Quality and Safety Through Supply ChainVisibility. Wiley, 2016.

[23] G. A. Susto, S. Pampuri, A. Schirru, A. Beghi, and G. De Nico-lao, “Multi-step virtual metrology for semiconductor manufacturing: Amultilevel and regularization methods-based approach,” Computers &Operations Research, vol. 53, pp. 328–337, 2015.

[24] Moteiv Corporation, “T-mote Sky: Low power wireless sensor module,”June 2006. [Online]. Available: http://www.eecs.harvard.edu/∼konrad/projects/shimmer/references/tmote-sky-datasheet.pdf

[25] A. B. Sharma, L. Golubchik, and R. Govindan, “Sensor faults: Detectionmethods and prevalence in real-world datasets,” ACM Transactions onSensor Networks, vol. 6, no. 3, pp. 23:1–23:39, Jun. 2010.

Angelo Cenedese received the M.S. (1999) and thePh.D. (2004) degrees from the University of Padova,Italy, where he is currently an Associate Professorwith the Department of Information Engineering. Hehas held several visiting positions at the UKAEA-JET laboratories in the Culham Research Centre,UK (2002-04), the JAERI Institute, Japan (2000) andGeneral Atomics, CA (2004), the UCLA Vision Lab,CA (2010), the F4E European Agency, Spain (2014).He has been involved and he is currently involvedin several projects on estimation and control of

distributed systems and of control of complex systems, funded by Europeanand Italian government institutions and industries, with different roles ofparticipant and/or principal investigator. His research interests include systemmodeling, control theory and its applications, sensor and actuator networks,multi agent systems. On these subjects, he has published more than 100 papersand holds three patents.

Michele Luvisotto received the Masters degreein automation engineering from the University ofPadova, Padova, Italy, in 2014. He is a PhD studentwith the Department of Information Engineering,University of Padova, Padova, Italy. He has beena visiting researcher at ABB Corporate ResearchCenter in Vasteras (Sweden) from March to Septem-ber 2016. His research interests include wirelessnetworks and real-time industrial communication.

Giulia Michieletto received the Masters degreein automation engineering from the University ofPadova, Padova, Italy, in 2014. Since November2014, she is a PhD student at the Department ofInformation Engineering at the University of Padova.She has been a visiting researcher at LAAS-CNRS(Toulouse, France) from March to July 2016. Herresearch interests include decentralized estimationand control for multi-agent systems.

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