Towards Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

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Citation:

Daisuke Takaishi, Hiroki Nishiyama, Nei Kato, and Ryu Miura, "Towards

Energy Efficient Big Data Gathering in Densely Distributed Sensor

Networks," IEEE Transactions on Emerging Topics in Computing (TETC),

vol.2, no. 3, pp. 388-397, Sep. 2014.

URL:

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6800057

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Towards Energy Efficient Big Data Gatheringin Densely Distributed Sensor Networks

Daisuke Takaishi∗‡, Hiroki Nishiyama∗§, Nei Kato∗¶, and Ryu Miura†∥∗Graduate School of Information Sciences, Tohoku University, Sendai, Japan

†Wireless Network Research Institute,National Institute of Information and Communications Technology, Tokyo, Japan

{‡takaishi,§bigtree,¶kato}@it.ecei.tohoku.ac.jp,∥ryu@nict.go.jp

Abstract—Recently, the “big dat” emerged as a hot topicbecause of the tremendous growth of the Information andCommunication Technology (ICT). One of the highly anticipatedkey contributors of the big data in the future networks isthe distributed Wireless Sensor Networks (WSNs). Althoughthe data generated by an individual sensor may not appearto be significant, the overall data generated across numeroussensors in the densely distributed WSNs can produce a significantportion of the big data. Energy-efficient big data gathering in thedensely distributed sensor networks is, therefore, a challengingresearch area. One of the most effective solutions to address thischallenge is to utilize the sink node’s mobility to facilitate the datagathering. While this technique can reduce energy consumptionof the sensor nodes, the use of mobile sink presents additionalchallenges such as determining the sink node’s trajectory andcluster formation prior to data collection. In this paper, wepropose a new mobile sink routing and data gathering methodthrough network clustering based on modified Expectation-Maximization (EM) technique. In addition, we derive an optimalnumber of clusters to minimize the energy consumption. Theeffectiveness of our proposal is verified through numerical results.

Index Terms—Big data, Wireless Sensor Networks (WSNs),clustering, optimization, data gathering, and energy efficiency.

I. I NTRODUCTION

RECENT development of various areas of Information andCommunication Technology (ICT) has contributed to an

explosive growth in the volume of data. According to a reportpublished by IBM in 2012 [1] , 90 percent of the data in theworld was generated in the previous two years. As a conse-quence, the concept of the big data has emerged as a widelyrecognized trend, which is currently attracting much attentionfrom government, industry, and academia [2]. As shown inFig. 1, the big data comprises high volume, high velocity,and high variety information assets [3], which are difficult togather, store, and process by using the available technologies.The variety indicates that the data is of highly varied structures(e.g. data generated by a wide range of sources such asMachine-to-Machine (M2M), Radio Frequency Identification(RFID), and sensors) while the velocity refers to the highspeed processing/analysis (e.g., click-streaming, fast databasetransactions, and so forth). On the other hand, the volumerefers to the fact that a lot of data needs to be gatheredfor processing and analysis. Although currently used services(e.g. social networks, cloud storage, network switches, and soforth) are already generating much volume of the big data,

Fig. 1. Major trends of big data gathering

it is anticipated that more and more data will be generatedby sensors/RFID devices such as thermometric sensors, atmo-spheric sensors, motion sensors, accelerometers, and so on. Infact, according to a report by ORACLE [4], the volume ofdata generated by sensors and RFID devices is expected toreach the order of petabytes. Interestingly as shown in Fig. 1,the sensors are responsible for generation of big data in bigvolume and also in a wide variety.

Gathering the large volume and wide variety of the senseddata is, indeed, critical as a number of important domains ofhuman endeavor are becoming increasingly reliant on theseremotely sensed information. For example, in smart-houseswith densely deployed sensors, users can access temperature,humidity, health information, electricity consumption, and soforth by using smart sensing devices. In order to gather thesedata, the Wireless Sensor Networks (WSNs) are constructedwhereby the sensors relay their data to the “sink”. However,in case of widely and densely distributed WSNs (e.g. inschools, urban areas, mountains, and so forth) [5], [6], thereare two problems in gathering the data sensed by millions ofsensors. First, the network is divided to some sub-networksbecause of the limited wireless communication range. Forexample, sensors deployed in a building may not be ableto communicate with the sensors which are distributed inthe neighboring buildings. Therefore, limited communicationrange may pose a challenge for data collection from all sensornodes. Second, the wireless transmission consumes the energy

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of the sensors. Even though the volume of data generated byan individual sensor is not significant, each sensor requiresa lot of energy to relay the data generated by surroundingsensors. Especially in dense WSNs, the life time of sensorswill be very short because each sensor node relays a lot ofdata generated by tremendous number of surrounding sensors.In order to solve these problems, we need an energy-efficientmethod to gather huge volume of data from a large numberof sensors in the densely distributed WSNs.

To achieve energy-efficient data collection in densely dis-tributed WSNs, there have been many existing approaches.For example, the data compression technology [7] is capableof shrinking the volume of the transmitted data. Althoughit is easy to be implemented, the data compression technol-ogy requires the nodes to be equipped with a big volumeof storage and high computational power. In addition, thetopology control technology can evaluate the best logicaltopology and reduce redundant wireless transmissions [8], [9].When the redundant wireless transmissions are reduced, therequired energy for wireless transmissions can be also reduced.Furthermore flow control and routing can choose the pathwhich consists of nodes having high remaining energy [10],[11]. However, these technologies are not able to deal withthe divided networks problem.

To deal with both the divided sub-network problem andthe energy consumption issue, the mobile sink schemes havereceived great attention in literature. In such schemes, the datacollector, referred to as the “sink node” (or simply the sink)is assumed to be mobile such as Vehicle, Unmanned AerialVehicle (UAV), and so on. As the sink node moves aroundthe sensing area, the sensor nodes send data to the sink nodewhen the sink node comes in their proximity. Thus, energyconsumption can be decreased by reducing the amount ofrelays in the WSN. Since the mobile sink schemes aim toreduce wireless transmissions, the trajectory of the sink node isdecided based on the sensor nodes’ information (e.g., locationand residual energy). The sink node divides the sensor nodesinto a number of clusters based on a certain condition. Then,the sink node roams around in these clusters.

In this paper, we propose an energy minimized cluster-ing algorithm by using the Expectation-Maximization (EM)algorithm for 2-dimensional Gaussian mixture distribution.Our proposal aims to minimize the sum of square of wire-less communication distance since the energy consumptionis proportional to the square of the wireless communicationdistance. Moreover, we first focus on the “data request floodingproblem” to decide the optimal number of clusters. The datarequest flooding problem refers to the energy inefficiency thatoccurs when all the nodes broadcast data request messages totheir respective neighboring nodes. This problem wastes en-ergy, particularly in the high density WSNs. Previous researchwork advocates increasing the number of clusters to reduce thedata transmission energy. However, in this paper, we point outthat an excessive number of clusters can result in performancedegradation, and therefore, we propose an adequate method forderiving the optimal number of clusters.

The remainder of the paper is organized as follows. Sec-tion II reviews some related works and presents our research

motivation. In Section III, we present our proposed clusteringalgorithm based on a modified EM technique. Section IVillustrates the derivation of the optimal number of clusters.Performance evaluation is presented in Section V. Finally,Section VI concludes the paper.

II. RELATED WORKS AND OUR MOTIVATION

The review conducted by Sagirogluet al. [3] highlightedthat big data and its analysis are at the core of modernscience and business. Sagirogluet al. identified a number ofsources of big data such as online transactions, emails, audios,videos, images, click-streams, logs, posts, search queries,health records, social networking interactions, mobile phonesand applications, scientific equipment, and sensors. Also, itwas pointed out, in their work, that the big data are difficultto capture, form, store, manage, share, analyze, and visualizevia conventional database tools. Furthermore, the three maincharacteristics of big data, namely variety, volume, and veloc-ity are discussed in that work that were briefly described inSection I.

According to the report by ORACLE in [12], the conceptof big data is stimulating a wide range of industry sectors.Specific examples of big data generated by sensors wereprovided in the report. For instance, manufacturing companiesusually embed sensors in their machinery for monitoring usagepatterns, predicting maintenance problems, and enhancing theproduct quality. By studying the data streams generated by thesensors embedded in the machinery allow the manufacturersto improve their products. The numerous sensors deployed inthe supply lines of utility providers generate a huge volumeof data, which are consistently monitored for productionquality, safety, maintenance, and so forth. Other examples ofsensors generating a bulk of the big data consist in electronicsensors monitoring mechanical and atmospheric conditions. Inaddition, sensors used for healthcare services (to monitor bio-metrics of the human body, patients’ conditions, healthcarediagnoses, treatment phases, and so forth) are identified tobe a rich source of big data in the report presented in [12].However, how to gather the sensed data from these numeroussensors in an energy-efficient manner remained beyond thescope of the report.

The work in [13] presented a cloud-based federated frame-work for sensor services. The main objective of the workwas to enable seamless exchange of feeds from large numbersof heterogeneous sensors. Various applications using big datagenerated by densely distributed WSNs have also emerged inliterature. In addition, in [14] and [15], big data in terms ofthe healthcare information (e.g., blood pressure and heart rate)sensed by numerous sensors are used to realize remote medicalcare services. Furthermore, patients’ location information areused to arrange prompt dispatch of ambulances. Large volumeof data gathered from location-sensors attached to animalsenabled researchers to observe various animal habitats [16],[17]. Because widely and densely distributed WSNs collectvarious types of data, the overall data which are gatheredis, indeed, overwhelming. To efficiently gather the big datagenerated by the densely distributed WSNs is, however, not

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an easy task since the WSNs may be divided into sub-networksbecause of the limited wireless communication range of thesensors.

In conventional research works, data gathering using themobile sink in WSNs has been widely studied in literature.Data Mobile Ubiquitous LAN Extensions (MULEs) [18] isthe one of the most prominent and earliest studies on themobile sink scheme. Data MULEs follow the basic steps ofall the mobile sink schemes. First, it divides sensor nodesinto clusters. Second, it decides the route for patrolling eachcluster. The work in [18] assumes a simple data collectionscheme whereby the mobile sink node divides sensor nodesinto grids regardless of the sensor nodes’ location, and patrolsthe grids by using random walk between the neighboring grids.However, this type of clustering, which is not based on thenodes’ location, might result in inefficient data gathering. Ifthere is no sensor node remaining in the cluster, patrollingthe empty cluster results in waste of time and degradedefficiency. Also, patrolling based on randomness might resultin unbalanced visits to clusters with different numbers ofsensor nodes. Thus, the mobile sink might fail to collectinformation.

Low-Energy Adaptive Clustering Hierarchy (LEACH) [19]is one of the most famous clustering algorithms in WSNs usingthe static sink node. In LEACH, the clustering algorithm isexecuted by the each sensor node. Sensor nodes exchangeinformation on their residual energies, and the nodes withhigher residual energy are given a higher probability ofbecoming a cluster head. By doing periodical re-clustering,energy consumption of each node becomes eventually equal.However, LEACH still has several shortcomings. For example,because LEACH is based on the assumption that each nodecan communicate with all other nodes, the WSNs deployedin wide areas are not able to use the algorithm. Most of thedistributed algorithms like LEACH naturally consider the limi-tation of the node’s communication range.K-hop OverlappingClustering Algorithm (KOCA) [20] andk-hop connectivityID (k-CONID) [21] are examples of the distributed clusteringalgorithms. Authors of KOCA focused on multiple overlap-ping clusters, and designed the KOCA algorithm based on aprobabilistic cluster head selection and nodes’ location. Thek-CONID algorithm is also a probabilistic algorithm. The nodesexchange their random IDs with each other, and the node thathas the minimum ID within k-hop is selected as a cluster head.

In WSNs, minimizing data transmission is difficult for a dis-tributed clustering algorithm. If a WSN is physically dividedinto sub-networks, a node cannot possess information about allthe nodes in the WSNs. Thus, the algorithm cannot achieveoptimization. To realize minimum energy clustering, we needto use the centralized clustering algorithm. Moreover, thecentralized clustering algorithm, which is conducted by a supernode, is suitable for the mobile sink scheme. Power-EfficientGathering in Sensor Information Systems (PEGASIS) [22] andKAT mobility (K-means And TSP mobility) [23] are oneof the centralized clustering algorithms. PEGASIS algorithmconstructs chain clusters of nodes based on location, andrepeats cluster head selection. PEGASIS algorithm considersthe limitation of the communication range, and achieves

uniform energy consumption. However, the algorithm stilldoes not achieve minimization of energy consumption becausethe clustering algorithm uses greedy algorithm. KAT mobilitydivides the nodes into clusters by using k-means algorithm.Becausek-means algorithm is the centralized clustering al-gorithm based on the node’s location, the clustering result iscloser to the total optimization. While the result is the optimalcluster that reduces energy consumption, the KAT mobilityalgorithm is designed without considering the communicationrange limitation. Therefore, the mobile sink might fail tocollect information from all nodes.

Contemporary research on the sensor node clustering algo-rithm can be classified into three types, namely centralizedalgorithms without considering nodes’ information (i.e., loca-tion or communication range), distributed algorithms withoutconsidering nodes’ information, and distributed algorithmsthat consider the nodes’ location and communication range.However, to achieve both minimization of data transmissionand data collection from all the nodes, we need to use acentralized algorithm, which considers the nodes’ locationand communication range. Unlike existing algorithms, ourproposed clustering algorithm achieves both minimization ofdata transmission and data collection.

Earlier research works on sensor node clustering algorithmsdemonstrates that the increasing number of clusters reducesenergy consumption for data transmission. Certainly, the ideaholds since increasing the number of clusters decreases thecluster-sizes and shortens the transmission length. Some re-searchers consider that certain limitations on the number ofcluster can be decided by other factors. For example, in [24],the limitation is the maximum acceptable latency of datacollection. The authors of [24] also defined the limitationby a node’s buffer size. While these limitations are realisticassumptions, they do not consider the energy consumption fordata requests. In our paper, we first focus on the effect ofdata request messages by increasing the number of clusters.Based on a simple and common data gathering model of thedensely distributed WSNs, we demonstrate that the number ofdata request messages has a noticeable impact on the energyconsumption of the sensor nodes. When the connectivity ofthe nodes becomes bigger, the impact becomes larger also. Inthis paper, we present how to evaluate the optimal numberof clusters to minimize the energy consumption of the sensornodes.

III. C LUSTERING-BASED BIG DATA GATHERING IN

DENSELY DISTRIBUTEDWSN

In this section, we first outline the clustering problem inWSN using mobile sink and the challenges in solving thisproblem. After that, we introduce the considered networkmodel and the overview of EM algorithm for clustering. Basedon EM algorithm, we proposed our clustering method and theprocedure to gather data using the proposed method.

A. Clustering problem

When considering the scheme of data gathering in WSNusing mobile sink, the biggest challenge in reducing energy

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Fig. 2. An example of the considered network.

consumption is how to decide the location where data gath-ering is conducted. In other words, this problem has samemeaning as answering the following two questions. 1) Whatis the best algorithm for dividing nodes into clusters? 2)How many clusters is optimal in terms of reducing energyconsumption? As we assume that required energy for datatransmission of node is proportional to the square of trans-mission distance, the best clustering algorithm to minimizeenergy consumption for data transmission must minimize thesum of square of data transmission distance in a network.EM algorithm is powerful and well-known tool to solve theclustering problem by repeatedly calculate the simple mathformula. Since the EM algorithm can minimize the sum ofsquare of distance between every node and cluster centroid,we adopt EM algorithm over the 2-dimensional Gaussianmixture distribution. However, there is a limitation of themaximum communication range in the realistic situation. Notall nodes can connect to each other and also to the clustercentroid. Nodes that cannot directly communicate with thecluster centroid need to communicate in a multi-hop manner.In multi-hop communication, communication distance is a sumof distance between nodes in multi-hop path. Therefore, asshown in Fig. 2, communication distance is different fromdirect distance. However, the EM algorithm minimize the sumof square of direct distance, not communication distance. Thus,we need to adapt the EM algorithm to the situation of limitedmaximum communication range and improve it such as tominimize the sum of square of communication distance.

B. Considered network model

In this paper, we consider a network which consists of amobile sink and many sensor nodes spread within a limitedfield. Every sensor node knows its location by using local-ization technology, and the mobile sink knows all nodes’locations. Regardless of being a sink or the sensor, a node has alimited communication rangeR and communication is alwayssuccessful if it is withinR. The mobile sink node patrolsthe cluster centroids that are calculated to minimize energyconsumption for data transmission, and collects data from

sensor nodes. Sensor nodes are equipped with a buffer memoryand store sensed information until mobile sink approaches thecluster centroid. The information is transferred to the sinknode by multi-hop fashion. In this paper, we assume a denselydistributed WSN in a large area such as schools, urban areas,mountains, and so forth and thus WSNs are divided into sub-networks. Fig. 2 shows a simple example of the assumednetwork.N sensor nodes illustrated by circles are distributedin the targetL × L area.K centers of clusters illustratedby filled circle is to be visited by mobile sink. A solid-fillarea and a dotted circle means “group” of nodes and cluster,respectively. In this paper, “group” means a set of nodes thatcan communicate with each other. The nodes that belong todifferent groups cannot communicate with each other due tobeing far away. There areG groups in the field, andNg andKg refers to the number of nodes and number of clusters inthegth group, respectively. The number of groups is calculatedby the nodes’ location and communication rangeR. In caseof Fig. 2,N1 = 7 andK1 = 2 because there are 7 nodes and2 clusters in group 1.

C. Overview of EM algorithm for clustering

The EM algorithm is a classical clustering algorithm, whichassumes that nodes are distributed according to Gaussianmixture distribution,

p(x) =

K∑k=1

πkN (x|µk,Σk) , (1)

whereK andπk indicate the total number of clusters and themixing coefficient of thekth cluster, respectively.N (x|µ,Σ)is defined as follows,

N (x|µ,Σ)

=1

(2π) |Σ|1/2exp

{−1

2(x− µ)

TΣ−1 (x− µ)

}, (2)

wherex is the position vectors of all nodes. Cluster parame-ters,µk andΣk, are the position vector of centroid of clusterk and 2×2 covariance matrix of thekth cluster, respectively.

At the first step, EM algorithm calculates each node’s valueof degree of dependence that is referred to as responsibility.The responsibility shows how much a node depends on acluster. Thenth node’s value of degree of dependence onkthcluster is given by following equation.

γnk =πkN (xn|µk,Σk)

K∑j=1

πjN(xn|µj ,Σj

) . (3)

Because of its definition, the responsibility takes values be-tween 0 and 1. At the second step, the EM algorithm evaluatesK weighted center of gravity of a 2-dimensional locationvector of nodes. This evaluation uses the responsibility valueas weight of nodes. At the third step, the locations of thecluster centroids are changed to the weighted centers of gravityevaluated in the second step. And EM algorithm evaluates the

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value of the log likelihood as shown below.

P = ln p(X|µ,Σ,π)

=

N∑n=1

ln

{K∑

k=1

πkN (xn|µk,Σk)

}.

(4)

Until the value of log likelihood converge, the EM algorithmrepeats all steps. This value of log likelihood is monotonouslydecreasing, and the EM algorithm always terminates. Becausethe EM algorithm repeatedly updates cluster centroids’ posi-tion vector,µk, and nodes’ responsibility tokth cluster,γnk

,the sum of square of distances of each node to cluster graduallydecreases and finally becomes optimal.

D. Proposed clustering method

Our objective is to propose a clustering method based onthe EM algorithm. In supposed widely and densely deployedWSNs, which have high variety and high volume of data, weneed to consider “groups”, which refer to sets of nodes thatcan communicate with each other. Therefore, nodes that cannotcommunicate with each other belongs to different groups. Tocollect data from all nodes, the number of clusters must be setto more than the number of groups. Our proposed clusteringmethod can be summarized in Algorithm 1.

At first, the mobile sink sets the cluster centroids,µ, torandom locations. By using a random position vector ofcluster centroids, communication distances of each node tocluster centroids,Dnk, are calculated. Thereafter, the mixingcoefficient,π, and covariance matrix,Σ, are calculated.

After the cluster initialization phase, our proposed methodselects a groupg that has the largest value of proportion ofnumber of nodes to the number of clusters in groupg, shownas follows,

vg =Kg

Ng. (5)

In the selected group that has the highest value ofvg, ourproposed method picks up all nodes that belong to groupg and updates these node’s responsibility value,γnk. Thisresponsibility value reflects how much noden belongs tocluster k. By using the updated responsibility,γnk, clustercentroids,µ, and covariance matrix,Σ, are re-calculated, andthe number of nodes which belongs tokth cluster is calculatedas shown in the following equation,

Nk =∑

xn∈X

γnk. (6)

These calculation are repeatedly executed until the differ-ence between the newly calculatedP and previously calculatedP becomes smaller than small number,ϵ.

E. Data gathering procedure using the proposed clusteringtechnique

After clustering, the mobile sink patrols every cluster cen-troid and collects the data from the nodes in the cluster. It iseasy to see that delay is a main problem of using mobile sinkin WSNs. This delay is the waiting time from data generationto data sending. Because the mobile sink moves relatively slow

Algorithm 1 Proposed clustering algorithmInitialize cluster centroids,µ, to random locations.Calculate clusters’ parameters,π andΣ.CalculateDnk andP.while |P − Pnew| < ϵ do

Select a groupg which has the biggest valuevg.for k ∈ Kg do

for n ∈ Ng doCalculatenth node’s responsibility value,γnk.

end forCalculate number of nodes belong to cluster,Nk.Update the clusters’ parameters,π, µ andΣ, by usingNk.

end forEvaluate the log likelihoodPnew.

end whileReturn cluster centroids,µ, covariance matrix,Σ, and thenumber of nodes that belongs to each cluster.

compared with electrical communication between nodes, themobile sink scheme causes long delay. To shorten this delay,we need to minimize total patrolling path length. Thus, inour scheme the mobile sink patrols along Traveling SalesmanProblem (TSP) path of all cluster centroids.

Once the mobile sink arrives at the cluster centroids, itcollects data from sensor nodes. Directed Diffusion [25] isone of the most famous data collection schemes in WSNs.In our method, we consider using a typical example of them,i.e., “One Phase Pull [26] where the mobile sink node sendsdata request message at the cluster centroids. When a sensornode receives a data request message from clusterk, the nodere-broadcasts the data request message and replies data to theneighboring node, which is the parent node in the data requesttree of clusterk. Then, the node relays data messages to thesink.

To minimize the total required energy to send data, allnodes send the sensed information according to the value ofresponsibility of the cluster. The responsibility value is calcu-lated based on the given parameters,µ, π, andΣ, accordingto (3). These parameters are added to data request messageand sent by the sink. Only after the sensor deployment, eachnode exchanges its own position vector,x, with sensor nodesbelonging to same groups. Because the exchange of positionvector is executed only one time after the sensor deployment,the energy consumption is not significant. As a result, when anode belongs to only one cluster, the node can send all datato the sink node. And when a node belongs to more than oneclusters, the node sends data according to the responsibilityof each cluster. In case ofγn1 = 0.6 and γn2 = 0.4, if thenth node receives a data request message that is sent by thesink node at the centroid of cluster 1, the node replies 60%of data. And if the node receives data request message that issent from cluster 2, the node sends 40% of data to the sinknode at the centroid of cluster 2. By sending data using thiscluster adapted Directed Diffusion scheme, we can minimizetotal required energy to send data.

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(a) Low connectivity network (b) High connectivity network

Fig. 3. Data request flooding in low and high connectivity network.

IV. D ERIVING THE OPTIMAL NUMBER OF CLUSTERS IN

THE PROPOSED CLUSTERING METHOD

The data gathering method presented in the previous sec-tion aims to minimize energy consumed by gathering data.However, it still has a remaining issue, which is to find theoptimal number of clusters. Previous researches in literatureoften consider increasing the number of clusters lead tothe decrease of energy consumption for data transmission.However, such researches do not take into consideration theenergy consumption of data request message. In this section,we point out this problem, and show an analysis to derive theoptimal number of clusters.

A. Definition of connectivity

To analyze the correlation between energy consumption andconnectivity, we formulate the connectivity of nodes. In thispaper, we define the connectivity as the portion of nodes thatcan communicate with each other.

C =

∑Gg=1 Ng(Ng − 1)

N(N − 1). (7)

This metric takes a value between 0 and 1. When all nodes cancommunicate with each other, the value of connectivity is 1.If every node is isolated, the value is 0. When the mobile sinkstarts computing the optimal number of clusters, the mobilesink node knows every sensor nodes’ location. Therefore, themobile sink can calculate the connectivity valueC based onnodes’ location.

B. Data request flooding problem

In WSN using mobile sink, the sink node sends data requestmessage to invoke data transmission from sensor nodes whenit arrives at the cluster centroids. The nodes that receivedata request message send the data to the sink node andbroadcast data request message to their neighboring nodes.That data request message is repeatedly broadcasted until allnodes that belong to the same group receive the message.Although some nodes may receive data request message morethan 2 times, they only send data and broadcast the datarequest message once after the first time of receiving themessage. These broadcasts of data request message causehigh energy consumption because the network will be floodedwith redundant wireless communication. Thus, reducing datarequest transmission is also important for mobile sink scheme.

The impact of data request flooding issues becomes signif-icant when connectivity becomes larger as an example shownin Fig. 3. In Fig. 3(a) and Fig. 3(b), there are two groupsand one group, respectively. Six sensor nodes are scatteredon the ground. Furthermore, the sink nodes traverse the twocluster centroids, and broadcasts the data request message. Inthe case of Fig. 3(a), nodes can only communicate with thenodes that belongs to the same group. Sink node broadcastsdata request message to each node at cluster 1, and these nodesbroadcast the data request message. Therefore, the sum of thetransmission of data request message of both cluster 1 andcluster 2 is 6. On the other hand, in Fig. 3(b), where nodescan communicate with all nodes, the data request messagesent at the cluster 1 is transferred to all nodes. Furthermore,all nodes broadcast the data request message. Therefore, sumof the transmission of data request message of both cluster 1and cluster 2 is 12.

Even if number of nodes and clusters stay the same, the datarequest flooding problem becomes more serious with higherconnectivity. Moreover, it is clearly understood that the totalnumber of transmitted data request messages increase whenthe numbers of clusters increases. Because of this problem, itis necessary to find the optimal number of clusters in termsof connectivity and energy consumption.

C. Computing the optimal number of clusters

To decide the optimal number of clusters, we need to defineobjective function. The objective function is defined as thesum of required energy of data and data request messagetransmissions. Thus, the objective function,W (K), can bedefined as the sum of energy consumption in one cycle ofmobile sink patrol as follows.

W (K) = DReqEReq(K) +DDatEDat(K), (8)

whereEReq(K) and EDat(K) are the sums of the square oftransmission distance of data requests and data messages,respectively.DReq and DDat indicate the data size of dataand data request messages, respectively.EDat(K) is evaluatedaccording to the following equation:

EDat =

N∑n=1

K∑k=1

Hnk∑h=1

γnk · l2h, (9)

where Hnk is the hop count fromnth node tokth clustercentroid andlh is communication distance of each hop. Whennth node cannot communicate withkth centroid, we set thevalue of Hnk to 0 and the value of required energy to 0.Moreover, each node re-broadcasts each data request messageone time with the maximum transmission power. Since thedata transmission energy,EDat(K), is a decreasing functionof K while data request transmission energy,EReq(K), isan increasing function ofK, there is a trade-off relationshipbetween the first and second terms in the right side of (8). Byconsidering the condition that the number of clusters,K, mustbe greater than the number of groups,G, the optimal numberof clusters,Kopt, is defined by the following equation.

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Kopt = max(G, arg minK

(W (K))). (10)

In order to calculate the required energy to transmit datarequest messages, we consider one group of node that hasNg nodes andKg cluster centroids. Data request message issent from every cluster and every node re-broadcasts it onetime. Thus, the total required energy to transmit data requestmassage is formulated as follows:

EReq=

G∑g=1

KgNgR2, (11)

whereR is the maximum transmission range of sensor nodes.For simplicity, constant variables are omitted. If there is noimbalance of location of cluster centroids, the number of nodesthat belongs to each cluster is the same.

Kg

Ng=

K

N(12)

Here, if the number of nodes is larger than 1, the connectivity,C, can be approximated as follows:

C =

∑Gg=1 Ng(Ng − 1)

N(N − 1)≑

∑Gg=1 N

2g

N2. (13)

Therefore, from (11), (12) and (13),EReq can be calculated asfollows:

EReq= KNR2C. (14)

This analysis says that the required energy for data requesttransmission is proportional to connectivity. Thus, it can beseen that the number of clusters has a significant effect on con-nectivity. Moreover, the function is a monotonically increasingfunction ofK which indicates that a lower number of clustersis better for reducing energy of data request transmissions.

By calculating the required energy for data transmission asin (14), and data request transmission as in (9), the optimalnumber of clusters, (10), can be calculated by using (8) and(10).

V. PERFORMANCE EVALUATION

We conducted performance evaluation by using a clusteringsimulator built by C++ programming language. In this section,we first evaluate the clustering efficiency. Then we evaluatetotal energy consumption to evaluate our proposed methd ofoptimizing number of clusters.

A. Efficient data collection

In this experiment, we measure the energy consumption fordata transmissions,EDat, and the efficiency of our proposedclustering algorithm by varying the number of nodes. TableI shows simulation parameters used in the first experiment.Sensors are uniformly deployed in a 5000× 5000 squaremeters area. The nodes’ communication range is set to 438.57meters, and we measureEDat and efficiency of our proposal

TABLE IENVIRONMENTS OF1ST EXPERIMENT

Node distribution Uniformly randomNumber of cluster,K 10Number of node,N 20 - 100Communication range,R 438.57mLength of one side of field,L 5000m

TABLE IIENVIRONMENT OF 2ND EXPERIMENT

Number of cluster,K 5 - 50Number of node,N 50 - 100Communication range,R 200m - 600mLength of one side of field,L 1000m - 5000mClustering algorithm Proposed algorithm

clustering by varying the number of sensor nodes.EDat rep-resented in (9) simply shows how much energy is neededfor data transmissions from sensor nodes to the mobile sink.However, if locations of every centroid is far away from nodesand they cannot establish connection,EDat value is calculatedas 0 according to (9). This value does not reflect energysaving, but it only indicates failure of clustering. The clusteringalgorithm that do not consider connectivity suffer from thisfailure (e.g. pure EM algorithm). Thus, we also use a secondmetric, referred to as efficiency, which combinesEDat valueand number of connected nodes.

Efficiency=(Number of connected node)

EDat. (15)

We compare our clustering algorithm with EM algorithmand k-COIND algorithm, which are centralized clusteringalgorithm and distributed clustering algorithm, respectively.Our clustering algorithm is a centralized algorithm consideringconnectivity, unlike EM algorithm.

Figure 4(a) and 4(b) are experimental results of requiredenergy and efficiency respectively. As can be seen from (9),(11), the energy consumption is proportional to the squareof data transmission range, we measure energy in units ofm2, i.e., omitting constant variables. Figure 4(a) shows theproposed scheme and pure EM algorithm can reduce requiredenergy significantly compared withk-CONID algorithm. Thereason of this difference is based on difference betweencentralized and distributed cluster establishment. The central-ized algorithm can calculate more efficient clustering thanthe distributed one. Our proposed scheme behaves similarto the EM algorithm, but has less energy consumption. Thisimprovement occurs from considering connectivity and com-munication distance. Fig. 4(b) shows that EM algorithm is theworst clustering algorithm when node density is low. SinceEM algorithm does not consider node connectivity and iscentralized algorithm, when the number of nodes is low andnode density is small, centroids of EM algorithm can connectonly to a small number of nodes. Our proposed schemesucceeds to adapt to node density variation and minimizestransmission energy.

8

2.0 107

4.0 107

6.0 107

8.0 107

1.0 108

1.2 108

1.4 108

1.6 108

20 30 40 50 60 70 80 90 100

Ener

gy c

onsu

mpti

on[m

2]

Number of nodes

ProposalEM Algorithm

k-CONID

×

×

×

×

×

×

×

×

(a) Required transmission energyEDat.

2.0 10-4

4.0 10-4

6.0 10-4

8.0 10-4

1.0 10-3

1.2 10-3

1.4 10-3

1.6 10-3

20 30 40 50 60 70 80 90 100

Eff

icie

ncy

Number of nodes

Proposa lEM Algorithm

k-CONID

×

×

×

×

×

×

×

×

(b) Efficiency of each clustering algorithm.

Fig. 4. Energy consumption for data transmission and efficiency.

0

1.0 108

2.0 108

3.0 108

4.0 108

5.0 108

6.0 108

5 10 15 20 25 30 35 40 45 50

Number of Cluster

DataRequest (Connectivity = 0.2)Request (Connectivity = 0.4)Request (Connectivity = 0.6)Request (Connectivity = 0.8)Request (Connectivity = 1.0)

×

×

×

×

×

En

erg

y c

on

sum

pti

on

[m

]

2

×

(a) Energy consumption for data transmissions and data request mes-sages.

0

1.0 108

2.0 108

3.0 108

4.0 108

5.0 108

5 10 15 20 25 30 35 40 45 50

En

erg

y c

on

sum

pti

on

[m

]

Number of Cluster

Connectivity = 0.2Connectivity = 0.4Connectivity = 0.6Connectivity = 0.8Connectivity = 1.0

2

×

×

×

×

×

(b) Sum of energy consumption of data transmissions and data requestmessages.

Fig. 5. Effect of the number of cluster.

B. Optimal number of clusters

To evaluate our proposed method of optimizing number ofclusters, we measure the energy consumption by varying thenumber of clusters. Energy consumption is defined as the sumof energy consumption of data transmissions and data requests.Given parameters are enumerated in Table II. We set parameterDDat/DReq to 512.

Figure 5(a) shows the required energy for data transmissionsand data request transmissions, and Fig. 5(b) shows theobjective function. As described in the previous subsection,energy is measured in units of m2. Black dots are the optimalnumber of cluster computed by using our method. Dash linesin Fig. 5(b) are the area where the number of clusters issmaller than the number of groups. In those areas, a mobilesink cannot collect all data. By using our method, the optimalnumber of cluster is decided as 17, 23, 32, 47, and 50 whenconnectivity is 1.0, 0.8, 0.6, 0.4, and 0.2, respectively. Theseresults show that “traditional method” which increases thenumber of clusters is not always the best solution to reduceenergy consumption.

VI. CONCLUSION

In this paper, we investigated the challenging issues pertain-ing to the collection of the “big data” generated by denselydistributed WSNs. Our investigation suggested that energy-efficient big data gathering in such networks is, indeed, neces-sary. While the conventional mobile sink schemes can reduceenergy consumption of the sensor nodes, they lead to a numberof additional challenges such as determining the sink node’strajectory and cluster formation prior to data collection. To ad-dress these challenges, we proposed a mobile sink based datacollection method by introducing a new clustering method.Our clustering method is based upon a modified Expectation-Maximization technique. Furthermore, an optimal number ofclusters to minimize the energy consumption was evaluated.Numerical results were presented to verify the effectiveness ofour proposal.

ACKNOWLEDGMENT

Part of this work was conducted under the national project,Research and Development on Cooperative Technologies andFrequency Sharing Between Unmanned Aircraft Systems

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(UAS) Based Wireless Relay Systems and Terrestrial Net-works, supported by the Ministry of Internal Affairs andCommunications (MIC), Japan.

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Daisuke Takaishi received his B.E. in InformationEngineering from Tohoku University, Japan, in 2013.He is currently pursuing M.S. degree in the GraduateSchool of information Sciences (GSIS) at TohokuUniversity. He was awarded the IEEE VTS Japan2013 Student Paper Award at 78th Vehicular Tech-nology Conference (VTC2013-Fall). His researchinterests are in the areas of UAS Networks and adhocnetworks. He is a student member of IEEE.

Hiroki Nishiyama is an Associate Professor at theGraduate School of Information Sciences (GSIS),Tohoku University, Japan. He received his M.S.and PhD in Information Science from Tohoku Uni-versity, Japan, in 2007 and 2008, respectively. Hehas published more than 100 peer-reviewed pa-pers including many high quality publications inprestigious IEEE journals and conferences. He wasawarded Best Paper Awards from many internationalconferences including IEEE’s flagship events, suchas the IEEE Global Communications Conference in

2013 (GLOBECOM’13), GLOBECOM’10, and the IEEE Wireless Commu-nications and Networking Conference in 2012 (WCNC’12). He was alsoa recipient of the IEEE Communications Society Asia-Pacific Board Out-standing Young Researcher Award 2013, the IEICE Communications SocietyAcademic Encouragement Award 2011, and the 2009 FUNAI Foundation’sResearch Incentive Award for Information Technology. He has served as aCo-chair for Cognitive Radio and Networks Symposium of IEEE InternationalConference on Communications 2015 (ICC’15), a Co-chair for Selected Areasin Communications Symposium of IEEE ICC’14, an Associate Editor forIEEE Transactions on Vehicular Technology, an Associate Editor for SpringerJournal of Peer-to-Peer Networking and Applications, and the Secretary ofIEEE ComSoc Sendai Chapter. His research interests cover a wide range ofareas including satellite communications, unmanned aircraft system (UAS)networks, wireless and mobile networks, ad hoc and sensor networks, greennetworking, and network security. One of his outstanding achievements isRelay-by-Smartphone, which makes it possible to share information amongmany people by using only WiFi functionality of smartphones. He is aSenior Member of the IEEE, as well as a member of Institute of Electronics,Information and Communication Engineers (IEICE).

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Nei Kato received his Bachelor Degree from Poly-technic University, Japan, in 1986, M.S. and Ph.D.Degrees in information engineering from TohokuUniversity, in 1988 and 1991 respectively. He joinedComputer Center of Tohoku University as an as-sistant professor in 1991, and was promoted tofull professor position with Graduate School ofInformation Sciences, Tohoku University, in 2003.He became a Strategic Adviser to the President ofTohoku University in 2013. He has been engaged inresearch on computer networking, wireless mobile

communications, satellite communications, ad hoc & sensor & mesh networks,smart grid, and pattern recognition. He has published more than 300 papersin peer-reviewed journals and conference proceedings. He currently servesas a Member-at-Large on the Board of Governors, IEEE CommunicationsSociety, the Chair of IEEE Ad Hoc & Sensor Networks Technical Committee,the Chair of IEEE ComSoc Sendai Chapter, the Associate Editor-in-Chief ofIEEE Internet of Things Journal, an Area Editor of IEEE Transactions onVehicular Technology, an editor of IEEE Wireless Communications Magazineand IEEE Network Magazine, He has served as the Chair of IEEE ComSocSatellite and Space Communications Technical Committee(2010-2012), theChair of IEICE Satellite Communications Technical Committee(2011-2012),guest-editor of many IEEE transactions/journals/magazines, symposium co-chair of GLOBECOM’07, ICC’10, ICC’11, ICC’12, Vice Chair of IEEEWCNC’10, WCNC’11, ChinaCom’08, ChinaCom’09, Symposia co-chair ofGLOBECOM’12, TPC Vice chair of ICC’14, and workshop co-chair ofVTC2010. His awards include Minoru Ishida Foundation Research Encour-agement Prize(2003), Distinguished Contributions to Satellite Communica-tions Award from the IEEE ComSoc, Satellite and Space CommunicationsTechnical Committee(2005), the FUNAI information Science Award(2007),the TELCOM System Technology Award from Foundation for ElectricalCommunications Diffusion(2008), the IEICE Network System ResearchAward(2009), the IEICE Satellite Communications Research Award(2011), theKDDI Foundation Excellent Research Award(2012), IEICE CommunicationsSociety Distinguished Service Award(2012), five Best Paper Awards fromIEEE GLOBECOM/WCNC/VTC, and IEICE Communications Society BestPaper Award(2012). Besides his academic activities, he also serves on theexpert committee of Telecommunications Council, Ministry of Internal Affairsand Communications, and as the chairperson of ITU-R SG4 and SG7, Japan.Nei Kato is a Distinguished Lecturer of IEEE Communications Society, afellow of IEEE and IEICE.

Ryu Miura received the B.E., M.E., and Dr. Eng.degrees in Electrical Engineering from YokohamaNational University, Yokohama, Japan, in 1982,1984, and 2000, respectively. He joined NationalInstitute of Information and Communications Tech-nology (NICT, reorganized from CRL), Tokyo,Japan, in 1984. Since then, he has been engagedin R&D on communication systems using satellitesand high-altitude/long-endurance aerial platforms.During 1991-1992, he was a visiting researcher inAUSSAT, Pty. Ltd., Sydney, Australia, where he

served for the prototyping of mobile satellite communication systems. During1993-1996 and 2009 and 2011, he was with Advanced TelecommunicationsResearch Institute (ATR), Kyoto, Japan, where he was involved in R&D ondigital beamforming antennas and intelligent transport systems for drivingsafety support, respectively. Now he is a director of Dependable WirelessLab, Wireless Netowork Research Institute, NICT, Yokosuka, Japan, where heworks on the wireless systems for disaster-tolerant and body area networks.Dr. Miura is a member of IEICE.