Clustering Based Fuzzy Logic - Final

8/13/2019 Clustering Based Fuzzy Logic - Final

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Clustering Based Fuzzy Logic for

Multimodal Sensor Networks: A

Preprocessing to Decision Fusion

Rabie A. Ramadan1

Computer Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt

Abstract. The advances of Micro-electromechanical systems (MEMS) technology lead to new types of sensors named mul-timodal sensors where multiple features can be sensed and reported by one sensor. Forming a wireless sensor network of suchsensors poses new challenges to the wireless sensor networks in addition to the current challenges. Currently, each multimodal

sensor reports periodically a message for each feature or a long message that contains all the features compared to the tradi-tional sensors. Such multimodal sensor networks could be used for multiple purposes and serve different applications. Howev-er, data handling and information processing as well as data/decision tasks became much harder than before. In this paper, weintroduce a set of clustering algorithms taking into consideration the reported multiple features as well as some of the sensors

parameters such as nodes residual energyand clusterheadsdegree. The paper utilizes different clustering techniques includ-ing fuzzy logic. The proposed algorithms are designed to simplify the next step operation which is data/decision fusion and

decision making operations. Through an extensive set of experiments, the proposed algorithms are evaluated.

Keywords: Sensor Networks, multimodal sensor networks, clustering, intelligent classroom

1Rabie A. Ramadan. E-mail:[email protected]

1. IntroductionWireless sensor networks (WSNs) have a scientific

interest from academia and industry alike due to their

wide range of applications. Some of these applica-

tions are the battle field[19],habitat environment[1],

critical infrastructure[17], acoustic [6] monitoring ,

and chemical and radiation detection[11] as well as

in smart environments. Such applications raise new

challenges to wireless sensor networks. For instance,

real time and reliable monitoring is now essentialrequirements. At the same time, sensors have to func-

tion for long time to reduce the overall energy cost

and to keep the overall network operational. There-

fore, energy consumption minimization task is themain concern of WSN algorithm. In fact, energy con-

sumption is considered at different phases of the

WSN formation; for example, energy consumption is

considered during the deployment process , the de-

sign of Medium Access Control (MAC) algo-

rithms [7], the developing of routing protocols, and

during the implementation of information processing

techniques[10][28].

WSN consists of many tiny but smart sensing de-

vices; these devices are capable of sensing some of

the monitored field phenomena/features, process the

captured features, and transmit them to one or more

of their neighbors. The sensed data is transmitted in

ad hoc fashion from one node to another to a centra-

lized node named sink. The sink node collects the

sensed information from different sensors for deci-

sion making. In a traditional WSN, a sensing deviceis used to sense single feature from the monitored

field. However, with new advances in MIMS tech-

nology, a sensing device could have one or moresensors on board. One example on such types of sen-

sors is Imote2 board, shown in Fig 1a, where 3-Axis

Accelerometer, Temperature, Humidity, and Light

Sensors are mounted on its board[9].Another exam-

ple is Intel sensor board, shown in Fig 1b, in which it

is designed to have connectors for 3D Accelerometer,advanced temp/humidity sensor, and light sensor[8] .
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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As can be seen, these types of new sensors raise new

complexity issues to the data processing in sensornetworks. Huge data will be reported to the sink node

for analysis, pattern recognition, and decision mak-

ing. Therefore, some preprocessing operations suchas clustering and data fusion became essential tasks

for reliable WSN operation.

(a) (b)Fig. 1: sensor boards (a) Imote2 basic sensor board

(ITS400)[9],(b) Intel sensor board [8]

Clustering techniques are not new in wireless

sensor networks in which many of the clustering

algorithms are proposed mainly for energy savings.

Some of these algorithms are the ones reported in

and [14] and [24]. However, these algorithms did

not take into consideration the number of the re-

ported features or the data similarities among these

features. In addition, fuzzy logic clustering such as

C-Mean is heavily used in other fields as well as in

WSN field. For instance, in[27] , the authors usedfuzzy logic to form normalized clusters where Each

sensor node uses the energy level, local density

within its sensing range and time as parameters for

clustering. The authors used fuzzy logic also for the

purpose of reducing the overlapping among cover-

age among the selected cluster heads.Our proposal in this paper is different in which

along with the sensors residual energy and cluster

heads distribution, we consider the number of re-

ported features and the similarities among the sen-

sors readings during the clustering process. The

term features used in the rest of the paper means

data generated by each sensing device mounted onthe sensors board. For instance, if the sensors

board has three sensing devices mounted to measure

temperature, humidity, and pressure, this sensing

board is considered to have three sensing features.

In addition, we also consider the cluster head degree

during the clustering process. The cluster head de-

gree means, in this context, the number of nodes

that can join this cluster head. We believe that con-

sidering such parameters will lead to better cluster-

ing as well as it prolongs the WSN lifetime.

In summary, our contributions in this paper

include proposing a set of new multimodal WSNclustering algorithms. The new algorithms consider

the monitored features by the sensors as well as some

of the sensors parameters such as cluster headsdegree and residual energy. In the first contribution,

two new clustering algorithms are proposed namely

LEACH with Multimodal support (LEACH-M) and

Multimodal Limited Similarity Clustering (MFLC)

which are extensions to LEACH algorithm with

considering number of features during the clusteringprocess. The only thing different from LEACH in

LEACH-M is that each node reports multiple features

instead of one. MFLC on the other hand adds the

features into the clustering probabilities. Anotheralgorithm named Data Similarity Based Clustering

(DSBC) is also investigated. DSBC takes most of thesensed features as well as some of the sensors

parameters into consideration during the clustering

process. The last contribution in this paper is an

algorithm named Data Similarity Based Fuzzy

(DSBF). The algorithm does the clustering as otheralgorithms but applies fuzzy logic in its two phases

which are defining the similar nodes and clustering

phases. The motivation behind using fuzzy logic is

that fuzzy logic proves its efficiency in case of

uncertainty in the input parameters. In our problem,

definitely, we have uncertainty in the sensors reading.For instance, the temperature that leads to fire could

be within a range not based on a certain threshold.

Fuzzy logic can efficiently handle such uncertainty in

the input readings as well as in the clustering

parameters.

The used parameters in all algorithms are sensorsresidual energy and node degree (number of similar

neighbors), and sensors monitored features.

Regarding the sensors monitored features; there are

large numbers of features that can be monitored. In

fact, these features may differ from a sensor network

to another based on the target applications. Therefore,

in this paper, we describe a general solution that isapplicable for any kind of sensor network

applications.

The paper is organized as follows: the following

section includes our motivation to the proposed clus-tering algorithms, section 3 reviews of the related

work, section 4 is an overview on some of the con-

cepts used in this paper, the used network model is

presented in section 5 while the clustering algorithms

are detailed in section 6, section 7 includes the simu-

lation results, finally, the paper concludes in section8.


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2.MotivationThe proposed algorithms in this paper are moti-

vated by two different scenarios. The first scenario is

the data and decision fusion at intelligent classrooms

where different heterogeneous sensors report theirdata to a centralized node (computer) that controls

different actuators such as blind, air condition, light,

a) Weather station b) External temperature, hu-midity , and light sensor

c) Internal temperature

and light sensor

d) Internal Humidity sensor

e) Two presence sensors

f) XBee wireless sensor

g) i.LON server h) Star network boardFig. 2: Set of sensors in intelligent classroom

and smart board operations. Our intelligent classroom

at Ambient Intelligent Center (AMIC) located at

German University in Cairo (GUC) has many of theheterogeneous sensors that collaborate together to

ubiquitously control the classroom environment.

These sensors are connected through two types of

networks which are LonWork (Ethernet) and a Star

networks. The LonWork network has different sen-

sors such as humidity, temperature, presence, andlight sensors. The star network connects a weather

station with multiple sensors, and internal and exter-

nal temperature, humidity, and light sensors as wellas RFID devices to recognize the lecturer. Fig 2

shows some of these sensors and the main controller

of the LonWork and Star networks. Analyzing the

huge data collected from this large number of sensors

and recognizing the right event to control the differ-

ent actuators require accurate data mining and fusion.In large classrooms, where thousands of students

might be present, especially in countries like Egypt,

hundreds of wireless sensors might be required and

multi-hop network might be formed. Getting the rightdecision based on the reported data from these sen-

sors would be impossible without suitable clusteringand data/decision fusion technique.

The second motivation scenario is WSN for fire

detection; in this scenario, sensors are deployed to

detect if there is a fire or not in critical infrastructure

such as airport and important buildings. In such case,sensors are used to report different features that col-

lectively lead to the detection of fire such as humidi-

ty, temperature, pressure, and light. If we depend on

the sink node to analyze the received data, it might

not discover it. The failure of detecting the fire is due

to the huge data the sink has to analyze as well as theinefficiency of centralized data mining techniques in

such data stream scenario. Therefore, decision and

data mining at a suitable cluster head might lead to

accurate event detection and correct decision mak-

ing.

3.Related WorkClustering of sensor nodes is considered as one of

the very successful techniques of mining useful in-

formation and discovering patterns in distributed

environments. It is a particularly useful techniqueespecially for applications that require scalability to

hundreds and thousands of nodes. Clustering also

supports aggregation of data in order to summarize

the overall transmitted data. However, the current

literatures either focus on node or data clusteringalone. Clustering of sensor nodes deals with two

main operations: 1) identifying cluster heads, and 2)

assigning nodes to respective cluster heads. These

two operations should be done at a very energy-

efficient level. On the other hand, data clustering

deals with collecting similar data for aggregation


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purposes. The process of choosing the cluster head

should take into consideration node design factorssuch as energy level of the sensor node and load ba-

lancing, as well as their similarities in terms of the

sensed data. A successful clustering algorithm is theone that produces an optimal amount of clusters, with

each having a single cluster head responsible for inter

and intra-cluster communication.

The problem of clustering data has been greatly

studied. It has been used for very large databases[22].

Another main use of clustering protocols has beeninvestigated for ad-hoc networks such as in [20]

and [12].Similarly, sensor networks clustering algo-

rithms have been proposed by several researches. For

instance, the Low-Energy Adaptive Clustering Hie-rarchy (LEACH) [24] is one of the early clustering

algorithms in WSN. LEACH depends on a randomfunction in selecting the cluster heads and it rotates

between cluster heads in order to preserve energy and

distribute evenly the load across the nodes in the

network. A more adaptive approach is (Hybrid Ener-

gy Efficient Distributed Clustering) (HEED) [14],where the cluster head formation depends on the

energy level of the sensor node. In case of HEED, the

authors argue that the algorithm yield more distri-

buted clusters and is efficient in terms of processing.

However, HEED is hard to be adapted to multimodal

WSNs.Little attention has been given to clustering of sen-

sor nodes according to their data readings similarity.

For example, H. Jin et al. [26] suggest a framework

for data mining in sensor networks, and propose mul-

ti-dimensional clustering, which clusters the nodes

according to their sensed attributes. Similarly, theDistributed, Hierarchical Clustering and Summariza-

tion algorithm (DHCS) seems to provide a better

performance for dense networks [4]. The algorithm

adopts several techniques, such as difference and

hopcount thresholds to model node and distance-

based clustering, but does not consider energy level

during the clustering process. Smarter clustering al-gorithm based on fuzzy logic is proposed by Indranil

G. et. al. in [16]. The author uses a fuzzy logic to

select cluster heads based on their energy and cen-

trality. Another recent clustering algorithm based onfuzzy logic controller (FLC) is proposed by Yahya et

al. in [27] where the authors tried to select a best

cluster heads for the purpose of coverage and load

balancing. The main clustering parameters that the

authors considered were the sensors energy and the

number of loyal followers where they assume smartnodes that can decide to join or not a cluster head

node. The results of these clustering algorithms seem

promising. However, the estimation of the centrality

point of each node in [27] is computed based on acentralized manner where it is assumed to be one of

the functions of the sink node.

A common problem with the previous clusteringalgorithms is that they tend to ignore the data similar-

ity in their clustering processes. In addition, some of

the algorithms are centralized where the sink node

has to be involved during the clustering process

which costs many of the message overheads. In this

paper, we introduce hybrid algorithms that utilizesome of the sensors parameters as well as data simi-

larities. Our algorithms are mainly designed to con-

sider multimodal sensors. However, it fits the tradi-

tional wireless networks as well.

4.OverviewFor the paper to be self contained, in this section,

the main concepts of multimodal WSN is introduced,

fuzzy logic controller (FLC), and LEACH as a WSN

clustering algorithm.

4.1.Multimodal WSNIn our previous work[18],a framework that simpl-

ifies dealing with heterogeneous multimodal sensornetworks was proposed. Our view to the multimodal

wireless sensor network goes beyond the current

usage of the traditional WSNs in which the network

could be designed to serve different purposes. There-

fore, even the number of features reported by each

sensor might differ. To reduce the amount of data

reported, we forced the nodes to use a sliding win-

dow. In addition, nodes report only when a change of

the sensed data (output of the sliding window) is ef-

fective. Therefore, the sink node or the cluster head

(in a clustered network) has to save the previous

readings received from the nodes to keep track of

their status. Certainly, this approach saves much ofthe sensors energy and prolongs the network lifetime.

However, clustered networks are proved to be energy

efficient than non-clustered networks. Not only that,

but also clustering methods play an important role inthe reliability and load balancing of the network.

Thus, in this paper, our clustering algorithms consid-

er the type of the reported features during the cluster-

ing process.


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4.2.Fuzzy Logic ControllerFuzzy logic is a powerful method that deals with

problems with uncertainties. It has been used to solve

many of the problems that traditional methods were

not able to solve. This science is introduced by Za-deh[23] in 1965 to emulate human usage of linguis-

tic variables instead of precise numerical variables.

Instead of using a crisp set with a collection of ele-

ments with each element belongs to specific set or

not, a fuzzy set is a set with elements belongs to a

graded membership function within interval [0,1]. Afuzzy logic controller consists of a fuzzifier, fuzzy

rules, fuzzy inference engine, and defuzzifier func-

tion. The fuzzifier takes the crisp input from the sys-tem and determines the degree that it belongs to the

appropriate fuzzy sets. Fuzzy rules according to Ma-

madani method are conditional statements in the

form of:

IF a is A

THEN b is B ,

where a and b are linguistic variables and A and B

are linguistic values determined by fuzzy sets on theuniverse of discourse X and Y, respectively. The

output rules aggregation is the function of the infe-

rence engine. Finally the defuzzifier is to transfer the

fuzzy output to the crisp output back to control the

desired system. In the following sections, we elabo-rate on each module according to its usage in cluster-ing process.

4.3.LEACH OverviewIn this subsection, we introduce one of the most

used clustering algorithms in sensor networks that we

will be using for comparison which is LEACH [24].LEACH is one of the first major improvements on

conventional clustering approaches in wireless sensor

networks. Conventional approaches algorithms in-

cluding Minimum Transmission Energy (MTE) [21]and direct-transmission do not lead to even energy

dissipation throughout a network. LEACH provides

load balancing of energy usage[25]by the rotation of

clusterheads. The algorithm is also organized in such

a way that data-fusion can be used to reduce the

amount of data transmission. The decision of whether

a node elevates to clusterhead is made dynamically ateach interval. In addition, the elevation decision is

made by each node independent of other nodes to

minimize overhead in clusterhead establishment.

Moreover, the clustering results of LEACH seems

promising; thus it is one of the most studied and refe-

renced algorithm. Due to the previous reasons, weselected LEACH to be the base algorithm for our

proposed ones.

Since it is simple LEACH is a round basedprotocol in which it starts by a clustering phase

followed by a reporting phase. In the clustering

phase, an adaptive selection of the cluster heads is

operated. On the other hand, sensors report their data

in the reporting phase; TDMA protocol might be

used in this phase. After certain period of time, theprotocol starts over to select other cluster heads.

Selecting other cluster heads in later rounds avoids

selecting the used cluster heads. Fig. 3 shows the

flow chart of LEACH. As shown in the Figure, theprotocol terminates only when the network lifetime is

close to end. The important thing in this protocol isthe distribution process in selecting the cluster heads.

In such process, at the beginning of each round a

node Ss , where S is the number of nodes in thenetwork, computes its probability to be a cluster

head; if the computed probability is larger than

certain threshold, it announces itself a cluster head

and close proximity node might send a join request to

it.

Fig. 3: LEACH protocol flow chart[3]


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As can be seen, LEACH is very simple;

however, it is efficient in terms of nodes clustering.In addition, it allows the cluster heads to aggregate

the received data which saves a lot of the sensors

energy. However, the algorithm assumes that singlefeature is reported by all the nodes and does not even

take the reported features into consideration during

the clustering process. Therefore, one of our

proposals in this paper is to extend LEACH to

support multimodal WSN and evaluate its

performance. In addition, we propose anotheralgorithm, where the number of reported features by

the nodes is taken into consideration during the

clustering process.

5.Network ModelGiven a set of sensor nodes S deployed in a moni-

tored fieldA, each sensor drives a continuous stream

of data. These sensors are assumed heterogeneous in

terms of their hardware as well as their reporting

phenomena. For instance, sensors may differ in their

initial energy, memory, sensing range, communica-

tion range, and processing capabilities. Sensors areassumed reporting only discrete events. In other

words, each reported feature (attribute) is associated

with discrete time event as well as the sensors loca-tion (through GPS or any other estimation method).

Continues sensor data stream can simply be con-

verted to discrete events with some preprocessingsuch as quantization. In addition, to save the sensors

energy and reduce the amount of reported data as

much as possible, sensors reporting is limited to the

change of state. A certain threshold [18] is primarily

set for each sensors feature; if the measured value

increases more than the threshold, the sensor willreport its value; otherwise, no reporting occurs. On

the other hand, removing duplicate information tech-

niques could be used if the previous assumption is

not applicable to some of the sensors. The reader isreferred to the reporting framework proposed in[18]

for more information on how to save sensors energy

by sending only the changed features based on a

specific threshold.

For the energy model used in this paper, we follow

the same model presented in [5].As shown in equa-

tion (1), the total energy consumption ),( dLTxE for

transmitting L-bit message over a distance d can be

expressed as the sum of both terms )(LelecTxE

and ),( dlampTxE where )(LelecTxE is energy

consumption due to the electronics parameters such

as digital coding and modulation and filtering.)(LelecTxE could be extended to include energy

consumption of a single bit elecE .

),( dlampTxE is the amplifier energy consumption

to transmit acceptable bit error rate for signal trans-

mitted to a receiver. ),( dlampTxE can be ex-

pressed in terms of fs or mp based on the trans-

mitter amplifier mode. In addition, there are loss fac-

tors for free spaces ( 2d loss) and multipath fading

( 4d loss) , respectively. 0d is a threshold that can

be determined by equating the two expressions in

which an empirical value ofmp

fsdd

0 .

02

...

04

...

)1(),()(),(

ddifdfsLelecEL

ddifdmpLelecEL

dlampTxELelecTxEdLTxE

Features that are reported by each sensor is mod-

eled by a vector of three attribute-value tuples

(attribute-name = v, location=(x,y,z), time = t),where v is the sensed feature, (x,y,z) is the sensors

location, and tis the event time stamp. Sensors report

their location with each update due to mobile nodes(if any). In stationary sensor network, the location

information might be reported once and thereafter

could be omitted from update message. Location

information is considered important in WSN due to

the decision making process. In other words, even, in

stationary network, the cluster head as well as thesink node might need to know from where they got

the reported data for further analysis and decision

making. In addition, the location information is im-

portant in case of sensors query in a query-basedWSN. If a sensor is used to sense multiple featuresfrom the monitored field, a vector of these attributes

will be reported; single location information and time

stamp will be added to the end of the vector. For

instance, if s1 is used to measure the temperature,

humidity, and wind speed, the reported attributes will

in the following form:s1 = {temp = v1, hum=v2, wind=v3, location =

(x,y,z), time=t}, where temp represents the tempera-

ture attribute, hum is the humidity, wind is the wind

speed, (x,y,z)is the sensors location, and tis the time


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stamp. If there is no change in the attribute value, the

value is replaced by 0 to refer to invalid data. As canbe seen, each node is considered as data source with

single or multiple features. With huge number of

sensors, data explosion might occur at the receivingend as well as the network energy will be depleted in

short time. Therefore, in the next sections, efficient

clustering is proposed as a solution to the previous

problems. However, there are many parameters and

uncertainties that make such algorithms not an easy

task.

6.Clustering AlgorithmsIn this section, we introduce a set of clustering al-

gorithms that we stated in the introduction section.

The algorithms are LEACH-M, MFLC, DSBC, and

DSBF. Throughout the next sections, we elaborate on

the details of these algorithms.

6.1.Multimodal Limited Similarity Clustering(MFLC)

As mentioned, there are two types of sensor net-

works which are single and multimodal sensor net-

works. A single feature sensor network is a networkwith each sensor node reports only one feature. On

the other hand, a multimodal sensor network is a

network with nodes report more than one feature.

Further, the network could be classified into homo-

genous and heterogeneous sensor networks. In ho-

mogenous sensor networks, nodes are typical in

every aspect. Node clustering based on similarity in

this case will be beneficial in terms of energy and

reliability wise if nodes with similar reporting fea-

tures are clustered together. Similarity in this context

means nodes that report close data values. Close va-

riance in sensed values might indicate that sensors

are in close proximity as well. In addition, sensorsenergy could be saved due to aggregation of similar

data. However, in heterogeneous sensor networks,

sensors differ in their characteristics such as initial

energy, sensing range, and communication range(s).Here, we introduce MFLC as a new clustering

algorithm that fits the purpose of multimodal sensor

networks either in heterogeneous or homogenous

networks. MFLC adapts LEACH clustering tech-

nique to support the multimodal sensor networks.MFLC differs from the LEACH on the criteria used

for a node to decide to be a cluster head or not.

LEACH selects the cluster head randomly; such cri-

teria would not be appropriate in multimodal sensornetworks as we will see later in the simulation result

section. Therefore, MFLC includes more appropriate

criteria which are the number of features to be re-ported by each node and the nodes residual energy.

Taking the number of features into consideration

during the clustering process will balance the load

over the selected cluster heads. At the same time, it

enhances the aggregation and data fusion which leads

to less number of messages to be transmitted fromthe cluster head to the sink node.

Equation (2) shows the new formula for the

nodes cluster head selection:

)2()()(*

max

)(*)/1mod(*1

)(smEscE

FsF

prppsT

The formula considers p, r , )(sF , maxF , )(sEc ,

and )(sEm parameters where p is the nodes desire

to be a cluster head, r is the current round, )(sF is

the number of features reported by node Ss ,

maxF is the maximum features reported by the net-

work, )(sEc is nodes Ss residual energy, and

)(sEm is nodes Ss initial energy .

Fig. 4 shows the MFLC algorithm details where

the sink node is assumed powerful enough to connect

to all nodes in the monitored field. The algorithm

works as follow:

1- In the initialization phase where the sink node(SN) broadcasts its position, its residual ener-gy, and the maximum number of features ex-

pected to be reported from all nodes. After

nodes received the SN message, each node

looks for its neighbors and fills its neighbors

list l.

2- In the second step, nodes start working on theclustering where each node applies equation

(2) and computes T(s).At the same time, each

node runs a random generator algorithm to

generate a random number between 0 and 1.

Based on these two values, T(s)and the gen-

erated random number, the node decides to be

a cluster head or not. If a node decided to be a

cluster head, it sets the CHparam to true and

broadcasts a message to all of its neighbors to

notify them that it is assigned itself as a clus-

terhead. Then, it waits for a certain period oftime to hear from other cluster heads (if any).

If it hears from any other cluster head, it adds

it to its CH-list for further usage. If a node is


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not a cluster head, it decides to join one of its

neighbor cluster heads based on the clusterheads residual energy. Any node without a

cluster head, it is forced to be a cluster head.

3- In step 3, nodes start to report to their clusterheads using TDMA protocol. Nodes may ap-

ply a sliding window for the sensed data.

Now, the cluster heads applies an appropriate

aggregation method such as the average on the

received similar features and try to send the

aggregated value(s) to the sink node. A clus-ter head might not be directly connected to the

sink node. Therefore, a multi-hop reporting

must be used. We propose, as shown in step

4a, the cluster head to choose one of its neigh-bor cluster heads found in its CH-listwith the

highest residual energy to send to. However,the CH-list might be empty; thus, the cluster

head has to select one of its neighbors that it

does not belong to its cluster to report to. If all

of the neighbors belong to other clusters, it

might select a node at random or based on theneighbors energy or number of features hop-

ing that it will reach one of the other cluster

heads.

4- Finally, in step 4, when the round time expiresand the network still alive, the clustering algo-

rithm is repeated; otherwise, the algorithmterminates. The network is considered dead or

out of service when a node goes out of energy

and cannot function any more.

6.2.Data Similarity Based Clustering (DSBC)In this subsection, we present our second cluster-

ing algorithm. The algorithm is designed to cluster

the multimodal sensor nodes based on their similarity

measures. It considers a similarity threshold that is

expected to enhance the clustering especially if mul-

tiple features are reported by a sensor node. In DSBC,nodes are considered similar when they report similar

number of features and this number of features is

greater than a predefined threshold value called si-

milarity threshold. Such characteristic might save a

lot of the sensors energy as well.

DSBC algorithm is divided into two main phaseswhich are clustering phase and data reporting phase

shown in Fig. 5. These two phases are periodically

repeated and new cluster heads are elected for the

purpose of load balancing and to cope with the envi-

ronment changing conditions.

6.2.1. Clustering PhaseIn the clustering phase, we assume that each nodehas as similarity factor which is a predetermined

value based on the total number of features that are

measured by the network. Also, it is assumed that

nodes are able to cooperate to exchange their sensed

data with their neighbors. This allows the nodes to

know the measured features by each other. Each node

constructs an attribute/features vectorA; values in the

attribute vector are arranged according to previous

knowledge of the sensed features of the network.

Each node also keeps what is named Difference

Threshold Vector (dt) which is a vector stating the

maximum allowable difference to measure nodes

features similarity (follows the same order as theattribute vector). In addition, each node has a node

degree variable (X) to store the number of similarnodes. As can be seen in Fig. 5, DSBC algorithm

clustering phase consists of five main steps.

1. The first step of the clustering process,marked 1.1 in the figure, instructs each sensor

node to save its readings in the designated

field of the attribute vector after sensing the

surrounding environment. Then, each node

broadcasts its readings to all immediate 1-hop

neighbors that are within its communication

range.

2. Step 1.2 deals with data readings comparison,where the sensor node Ss (S is a set of sen-sors), determines its similarity to the other

node Ss 1 using the threshold vector (dt).The node degree of Ss is then incrementedif the two nodes are similar. Ss 1 identifier(ID) will be placed in the similar neighbor

list sl . Therefore, the node degree in this

context means the number of similar sensors

around Ss . Such consideration increasesthe reliability of the data reported to the sink

node even if simple aggregation method is

used per cluster head.3. In the third step, each node broadcasts its node

degree along with its residual energy sE as

given in step 1.3. Each node Ss comparesits node degree with corresponding node de-

grees of other nodes belonging to sl , and if it

finds itself the highest node degree, it broad-

casts a CHannouncement message. In case of

more than one potential CH with the same

node degree, highest residual energy is used to

break ties. All surrounding nodes that receive

the announcement check if it is coming from a


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node in the sl and if so, the node checks the

value of chReceived. If false, it is not a clus-ter head or a member of a cluster yet, it sets it

to true and sets the source node in the selected

CHannouncement to be its cluster head. Then,

the node sends a registration message back to

the cluster head, including its current readings.

4. Step 1.5 states that nodes that are not similarto any neighboring nodes and have not ac-

cepted any CHannouncements become forced

cluster heads themselves.

5. The final step of the clustering phase shown inFig. 5 is step 1.6. In this step, each elected CHcollects all registration messages and their IDs

and places them in the member node list

6.2.2.Data Reporting PhaseAfter receiving all the registration messages from

cluster members nodes, the CH chooses a suitable

fusion method to fuse the data received from the sim-

ilar nodes. For instance, the CHsends the fused data

to the base station (Sink Node) in the form of a mes-

sage that states the number of nodes in the cluster

(including the CH), as well as the average vector of

all the readings of the nodes in the cluster. The CH

then periodically collects information from all its

members nodes and fuses the information to the base

station. Data and decision fusion will perfectly suitethis phase as well.

6.3.Data Similarity Clustering Based FuzzyLogic (DSBF)

Here, we introduce a new clustering algorithm that

is similar to DSBC. However it considers the uncer-

tainty in the clustering parameters. Again, the algo-

rithm considers sensors residual energy, the number

of similar neighbors, and the sensed features. The

similarity here is considered in terms of the number

of similar features reported by each sensor as well as

the close variance in the similar reported features.

DSBF works in three phases; in the first phase, the

node degree based similarity feature is computedwhile in the second phase the cluster heads are

elected. Both phases use fuzzy logic in their core

processes. In the third phase data is reported. After

all, the algorithm is periodically repeated for load

balancing since some of the cluster heads energy

might be exhausted due to sending and receiving.

One may think that using fuzzy logic in the first

two phases of the algorithm may consume much ofthe sensors energy especially when the algorithm is

periodically repeated. However, based on the fact

that in a platform like Telos platform [2], sending asingle byte is equivalent to executing about 4720

instructions. Thus, to reduce energy consumption, it

is imperative to minimize communication overhead

even if the number of computations increases.

6.3.1.Phase One: Computing Node DegreesIn this phase, node degrees are computed based on

nodes similarities in terms of their reported features.

To do so, we use a rule based fuzzy logic controller.

However, since a network may contain a large num-ber of features to be reported to the sink node, we

limit the linguistic variables used to describe the

crisp input to three variables which are low, medium,

and high. Fig. 6 shows an example on the fuzzy set

for three measured features which are feature 1 (f1),

feature 2 (f2), and feature 3 (f3).

The low and high variables are represented by

semi trapezoid membership function while the me-dium is represented by a triangle membership func-

tion. Therefore, the number of rules used in the fuzzy

rule based is 3(N)

rules whereNis the number of con-

sidered features. It is worth mentioning that with

increasing number of features, generating all of thenumber of rules might be a problem. However, dy-namic rule generation and rule reduction methods

might be a solution. The addressing of such problem

will be considered in the future work.

The fuzzy set for the output which is the opportu-

nity for a node being similar is represented using five

linguistic variables which are very low,low, medium,high, and very high. Again, the lowand very high are

represented by semi-trapezoid while other variables

are represented by triangle functions. For the defuzzi-

fication, it seems that the center of gravity (COG) of

fuzzy sets is an essential feature that concurrently

reflects the location and shape of the fuzzy sets con-cerned. Therefore, we use COG as our defuzzifica-

tion process as shown in equation (3).

)(

*)(

a

aaCOG

A

A

(3)

Where, )(aA is the membership function of setA.

Thus, given two nodess1ands2, their features are

used as input to the fuzzy sets. If the fuzzy output forboth of them falls into the same category, then both

nodes are considered similar.


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6.3.2.Phase Two: Cluster Head ElectionIn phase 1, node degrees based on node similari-ties are identified. In this phase, the cluster heads are

elected based on different parameters such as nodes

residual energy and degree. It is worth mentioningthat using multiple parameters in electing the cluster

heads in DSBC algorithm where fuzzy logic is not

used was not possible. Using many parameters in

DSBC leads to multi-objective problem which com-

plicates the clustering process in each node. On the

other hand, fuzzy logic allows using multiple para-meters in the cluster heads election phase. The only

complexity in this case is the number of generated

rules with the increasing number of parameters which

could be solved using any of the rule reduction me-thods such as rough set.

Again the linguistic variables used to describe thecrisp input are limited, in our case, to low, medium,

and high. Also, the fuzzy set for the output which is

the opportunity for a node being a cluster head is

represented using six linguistic variables which are

very low, low, medium, high, and very high. Again,the low and very high are represented by semi-

trapezoid while other variables are represented by

triangle functions. For the defuzzification, COG is

applied on the output. Nodes will join the elected

cluster head by the fuzzy logic controller; if there is

more than one cluster head announced, a node choos-es the cluster head with the highest residual energy.

Assigned cluster heads without members, if any, are

forced to join one of their neighbors cluster heads.Fig. 8 shows an example of fuzzy sets for nodes de-

gree and energy as well as the output set.

6.3.3.Phase Three: Data ReportingIn this phase, cluster heads applies suitable data

fusion and/or aggregation method and start sending

their data to the sink node. The sink node makes its

final decision based on the minded received data.

7.Simulation ResultsIn this section, we evaluate the proposed clustering

algorithms through different set of experiments. Our

simulation environment is designed especially for the

test purposes. Since multimodal sensors are not yetimplemented in the current sensor network simula-

tors, we designed our simulator using java frame-

work. In addition, for fuzzy logic controller, we used

jFuzzyLogic library implemented by Pablo Cingolani

et al.[15].Sensors are deployed randomly based on anormal distribution function. In addition, sensors

parameters follow the specifications of MICA2[13].

Moreover, two different types of environments are

tested in these experiments which are stable and un-

stable environments. Environments could be classi-fied into several categories; such as hostile, unreach-

able, dormant, etc. Environments that are of interest

to our research are the ones that affect the value of

the monitored features. For example, a stable envi-

ronment is one with features that dont change very

often or not by much, such as a fire monitoring net-work. In this application, temperature, for instance,

does not suddenly drop or increase. On the otherhand, an unstable environment is described as a con-

tinuously changing environment, where values of

features could be low at one point and high at the

next. These types of environments are usually knownas event-driven environments. Examples of these

environments include presence detection in intelli-

gent classrooms or tsunami detection systems. In

tsunami monitoring system, for instance, the wave

strength could change suddenly and frequently. At

the same time, the waves might greatly differ from

one place to another.

Throughout the following experiments, we tend to

use sensors with heterogeneous initial energy, com-munication range, and three features. We limited

ourselves to three features per sensors for fair estima-tion to the performance of the proposed algorithms.

The selected features are temperature, humidity, and

pressure. In addition, all of the results presented in

the following subsections are based on the average

results over different runs with different environment

settings. For DSBC algorithm, we conducted someexperiments to show the sensitivity of different val-

ues for . However, the results were obvious since

increasing the value of adds more restriction on

considering two nodes are similar. Therefore, we

concluded that setting to 50% is fair choice and it isfixed throughout our experiments in this section.

Based on the three features used, the fuzzy sets for

the features used in the following experiments are

shown in Fig. 7 while the fuzzy sets for nodes de-

gree, node energy, and output fuzzy set are plotted

in Fig. 8. Fig. 9 shows a sample from the set of rulesused by DSBF algorithm in its first phase while

Fig .10 shows a sample from the rules used at the

second phase.


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Step 1: Initialization

1- The sink node (SN) broadcasts its position and the maximum number of features that expected to be reported to allnodes

initMsg( maxF , x, y)

2- nodes S in the sensor networka. Find all neighbors within your range using HELLO msg.

HELLOMsg( NodeID, )(sEc )

b. Generate a neighboring list lbased on the received msgsStep 2: Clustering

3- nodes S in the sensor networka. Compute

)(

)(*

)(*

)/1mod(*1)(

max sE

sE

F

sF

prp

psT

m

c

b. If (T(s) > rand[0,1] )i. broadcast cluster head announcement CHMsg

ii. Set CHparamtrueiii. Construct CH-listbased on the CHMsgs received from other CHs

c. Elsei. Wait for CHMsg

ii. If received CHMsg, join a node with more )(sEc iii. Else , be a cluster head

d. If a cluster head remains without any members, it joins any neighbor cluster head.Step 3: Reporting

4- SCH a. If (CH-list is not empty and SNis not reachable)

i. Select a CHwith larger )(sEc // forces multi-hop routing through a neighbor CH)

ii. Else if (SNis reachable )- report to the SN

iii. Else // forces multi-hop routing through a neighbor node)- Select one of its neighbors that it is not in its cluster (highest )(sEc node)/ or a random node to

be a next hop routing node. The selected node is forced to report the received data to its currentcluster head.

Step 4: Re-Clustering

5- If the round time is expired and the network still a livea. Go to step 2

6- Elsea. Stop

Fig. 4: MFLC algorithm details


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Algorithm 1: DSBC Algorithm

A: Attribute Vectordt: Difference threshold vector

sl : Similar Neighbor list for sensor Ss .

MCH: Member Node list for CH sensor Ss

sX : Node degree for node Ss which is the number of similar node in the similarity list

CHreceived: A binary variable that it is set to true if Ss received a cluster head (CH)announcement; false

: Similarity factor : Similarity value

sE : Residual energy for node Ss

Phase 1: Clustering Phase

1. For each sensor Ss 1.1Broadcast A and to all neighbors1.2For all of the received vectors A, apply the following rules to define the similarity- If the same readings are present in both attribute vectors, and the values are within range of dt, compute the

similarity value according to:

hasSsfeaturesofnumberTotal

featuressimilarofnumber

- )( if Add iID to sl , where Si - Increment Xs1.3Broadcast sX and sE to all neighbor nodes1.4If sX has the maximum degree, Ss announces itself as a CH

1.4.1 Other nodes join the CH with the maximum degree If more than one CH with the same degree,choose CH with the highest energy

1.4.2 Set CHreceived= true.1.4.3 Sends registration message to elected CH along with A.

1.5 If Ss cannot find a similar node and didnt receive any CH announcements, it announces itself a clus-ter head CH.

1.6 Node Ss that is a CH saves all registration messages in MCH.Phase 2: Data Reporti ng

1.Each node Ss sends its sensed data based on the selected window size to its CH2.The CH aggregates the received data and sends it to the sink node

Fig. 5: DSBC Algorithm Outline

x0 x1 x2 x3 x4 x5(a) Feature 1

y1 y2 y3 y4 y5 y6(b) Feature 2

z0 z1 z2 z3 z4 z5

(c) Feature 3

Fig.6: Example of sensors features fuzzy sets


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Fig.7: Example of sensors features fuzzy sets

Fig.8: Example of clustering parameters using fuzzy

RULE 1 : IF Feature1 IS low AND Feature2 IS low AND Feature3 IS lowTHEN chance IS vlow;

RULE 2 : IF Feature1 IS low AND Feature2 IS low AND Feature3 IS high

THEN chance IS low;RULE 3 : IF Feature1 IS low AND Feature2 IS low AND Feature3 IS medium

THEN chance IS low;

RULE 4 : IF Feature1 IS low AND Feature2 IS high AND Feature3 IS lowTHEN chance IS low;

RULE 5 : IF Feature1 IS low AND Feature2 IS high AND Feature3 IS medium

THEN chance IS medium;

RULE 6 : IF Feature1 IS low AND Feature2 IS high AND Feature3 IS highTHEN chance IS high;

RULE 7 : IF Feature1 IS low AND Feature2 IS medium AND Feature3 IS lowTHEN chance IS low;

RULE 8 : IF Feature1 IS low AND Feature2 IS medium AND Feature3 IS medium

THEN chance IS medium;RULE 9 : IF Feature1 IS low AND Feature2 IS medium AND Feature3 IS high


RULE 10 : IF Feature1 IS medium AND Feature2 IS low AND Feature3 IS low

THEN chance IS low;

RULE 11 : IF Feature1 IS medium AND Feature2 IS low AND Feature3 IS high

THEN chance IS medium;RULE 12 : IF Feature1 IS medium AND Feature2 IS low AND Feature3 IS medium


RULE 13 : IF Feature1 IS medium AND Feature2 IS high AND Feature3 IS lowTHEN chance IS medium;

RULE 14 : IF Feature1 IS medium AND Feature2 IS high AND Feature3 IS medium

THEN chance IS high;RULE 15 : IF Feature1 IS medium AND Feature2 IS high AND Feature3 IS high

THEN chance IS high;

RULE 16 : IF Feature1 IS medium AND Feature2 IS medium AND Feature3 IS lowTHEN chance IS medium;

RULE 17 : IF Feature1 IS medium AND Feature2 IS medium AND Feature3 IS medium

THEN chance IS high;RULE 18 : IF Feature1 IS medium AND Feature2 IS medium AND Feature3 IS high


Fig. 9: DSBF Phase 1 sample rules


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RULE 1 : IF Energy IS low AND nDegree IS low

THEN chance IS vlow;

RULE 2 : IF Energy IS low AND nDegree IS medium

THEN chance IS low;RULE 3 : IF Energy IS low AND nDegree IS high


RULE 4 : IF Energy IS medium AND nDegree IS lowTHEN chance IS low;

RULE 5 : IF Energy IS medium AND nDegree IS medium

THEN chance IS medium;RULE 6 : IF Energy IS medium AND nDegree IS high


RULE 7 : IF Energy IS high AND nDegree IS lowTHEN chance IS medium;

RULE 8 : IF Energy IS high AND nDegree IS medium

THEN chance IS high;RULE 9 : IF Energy IS high AND nDegree IS high

THEN chance IS vhigh;

Fig. 10: DSBF Phase 2 sample rules

7.1.Average Number of Nodes per Cluster andAverage Number of Unclustered Nodes

Fig. 11 shows the average number of cluster heads

per network when different number of nodes is usedin a stable environment. In this set of experiments,

the average results over 10 networks starting from

100 nodes to 1000 nodes per network are presented.

Due to large number of charts, we show only the av-

erage results which seems to represent a trend in thealgorithms behaviors. As can be seen in Fig. 11, due

to the total randomness of LEACH-M in electing the

cluster heads, the percentage of the cluster heads

cannot be controlled. That is why LEACH authors

restrict the number of cluster heads to 5%. This per-

centage allows better distribution to the clusterheadsin the network. On the other hand, since MFLC uses

the number of features as a clustering condition, it

seems to perform a bit better than LEACH in terms of

unclustered nodes. However, DSBF algorithm gives

the best results in terms of the number of cluster

heads as well as the number of unclustered nodes.

DSBF produces in the first round 7% of the total

number of nodes as cluster heads and almost 0.3% of

unclustered nodes. Almost similar results are pro-

duced by DSBC. In conclusion, the results show that

data similarity algorithms have direct positive effect

on the number of cluster heads and the number of

unclustered nodes in the first round; other rounds

follow the same trend as well.

Fig. 11: Percentage of number of cluster heads and unclusterednodes in stable environment

In unstable environment, the algorithms behavioris changed. Fig. 12 shows the average percentage of

the cluster heads as well as the number of unclustered

nodes. It is worth mentioning that LEACH-M and

MFLC have not been affected by the change from

stable to unstable environment. However, DSBC is

largely affected. In fact, it performed worth thanLEACH-M algorithm. The reason behind this per-

formance drop is that DSBC mainly depends on a

similarity threshold vector. In a stable environment, it

is easy to adjust this similarity vector while in unsta-

ble environment, each round requires different simi-

larity vector. On the other hand, this set of experi-

ments show the beauty of fuzzy logic in DSBF where

the percentage of the cluster heads is almost the same

as well as the percentage of the clustered nodes .


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Fig. 12: Percentage number of cluster heads and unclustered

nodes in unstable environment

7.2.Cluster Formation Cost vs. Number ofNodes

In this section, a new set of experiments are con-duct on wide range of number of sensors starting

from 100 to 4000 nodes. With each network size, the

average percentage of consumed energy due to the

clustering is computed compared to the overall net-

work energy. The overall network energy is simply

the sum of the initial nodes energy. Fig. 13 shows the

cluster formation cost for LEACH-M, MFLC, DSBC,

and DSBF. As can be seen, the clustering overhead of

LEACH-M is the minimum while the DSBF is the

maximum. The results seem reasonable due to the

amount of computations that each node needs to per-form. However, DSBF overhead is rewarded by even

cluster distribution that leads to network load balanc-

ing as well as efficient data aggregation and energy

saving in the reporting phase. This observation is

clearly confirmed in section 8.4 where the network

lifetime is studied.

7.3.Average Dead Nodes Per RoundIn Fig. 14, we plot a sample of dead nodes per

round for a network with 500 nodes with different

problem settings. The experiments are conductedover stable and nonstable environments as well.These set of experiments simply show how the pro-

posed clustering algorithms perform within each

round. As shown in the Figure, nodes in LEACH-M

and MFLC die much faster than in DSBC and DSBF.

The reason behind this feature is the weak distribu-

tion of the cluster heads in LEACH-M and MFLC.

Fig. 13: Clustering overhead percentage

Fig. 14: Number of dead nodes per round

7.4.Network LifetimeIn this subsection, we evaluate the network life-

time for a network with 500 nodes. The average re-

sults over 10 simulation runs are presented in Fig. 15.In these experiments, the clustering algorithm termi-

nates when the network is disconnected due to lack of

nodes energy or nodes cannot reach the sink node.

Although DSBF has the largest overhead, see Fig. 13,

it survives for longer than any of the other algorithms

in both stable and unstable environments due to the

cluster head distribution and best node similarity

clustering. DSBF depends on the uncertainty in re-

ported data and considers a range of values to take

decide if nodes are similar or not. This is differentfrom other algorithms where a strict threshold is used.

LEACH-M and MFLC are almost having the same

number of rounds. However, DSBC does not perform

well in unstable environments. As mentioned before,

the lack of adjustment to the threshold vector leads to

bad distribution to the cluster heads in DSBC.


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Fig. 15: Algorithms lifetime

8.Conclusion and Future WorkIn this paper, we presented different clustering al-

gorithms for multimodal wireless sensor networks.The first algorithm, LEACH-M extends the LEACH

concept to include different features to be reported by

each sensor. In the second algorithm, MFLC, we

adapted the LEACH-M to include the number of fea-

tures in the clustering equation. Data similarity clus-

tering algorithm, DSBC, is also presented to involve

the similarity of the features readings of different

sensors. In the last algorithm, DSBF, we utilized the

fuzzy logic in the two phases of the algorithm. In the

first phase, we used fuzzy logic to handle the simi-larities among the nodes while in the second phasefuzzy logic handles the clustering process. After

large number of experiments with different problem

settings as well as stable and nonstable environments,

we could conclude that DSBF has the best perfor-

mance in terms of overall energy consumption and

network lifetime. In addition, it works fine with dif-ferent environments.

The only concern that needs to be considered in

our future work is the time taken to run the algorithm

in each node with large number of sensing features.

In other words, currently there is few number of sens-

ing devices that mounted on the sensors board.Therefore, few numbers of features will be reported.

However, with large number of features, a large

number of rules will be generated by the fuzzy logic

controller and might lead to rule explosion. In the

future work, this problem also needs to be tackled.On the other hand, DSBC seems to perform well in

most of WSN condition. However, its performance is

comparable to MFLC in case of using unstable envi-

ronments. LEACH-M and MFLC are fast and easy

algorithms to be implemented. However, they did not

seem to fit well the multimodal WSN.

The obvious future work to the clustering done in

this paper is the investigation to the data/decisionfusion algorithms. Other clustring issues that actually

raised by one of the reviewers of this paper which it

is worth investigated are: 1) sensors with multi sens-ing features might consume energy more than other

sensors with less number of sensing feature. In other

words, the correlation between the number of sensing

features and the sensors consumed energy might

have effect on the clustering performance and on the

selection of the clusterheads; 2) sometimes, energyparameter could have more priority (weight) more

than the reported features similarities or vice versa;

the question is how this will affect the clustering per-

formances.

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