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Data representation and clustering in Sensor Networks

Using Unsupervised Learning Algorithm

Sathya Prakash

Racharla1,

Assistant Professor

Computer Science and

Engineering

CVR College of

Engineering [email protected]

V D S Krishna2

Assistant Professor


Engineering

CVR College of


S. Srinivas3

Assistant Professor


Engineering

CVR College of Engineering [email protected]

Avinash

Amaranayani4

Assistant Professor


Engineering

CVR College of


Abstract Wireless sensor network (WSN) is one of the most promising technologies for some real-time

applications because of its size, cost-effective and easily deployable nature. Data clustering

and data representation are used for statistical analysis with more efficient. Machine

learning algorithms such as linear regression, SVM models, regularization methods,

reinforcement learning, and neural networks significantly reduce energy consumption. It

provides a comparative analysis of the performance of different methods to help the

designers for designing appropriate machine learning based solutions for clustering and

data aggregation applications. In this paper we analyzed different algorithms for data

representation and clustering in sensor networks. Improved performance can be observed in

unsupervised learning comparing to individual component analysis.

Index Terms—Sensor networks, Regularization, Clustering, Machine learning,

unsupervised learning, support vector machine.

I. INTRODUCTION

Wireless sensor networks as the name suggests are a class of networks where the nodes are

sensor nodes. The nodes which sense, which have the capability of sensing the physical

phenomena that occur around them. These sensing can be of different types a particular

sensor node might be able to sense temperature might be able to sense pressure they can

sense if there is any object that is moving around them sensors can also sense colors can

sense vibration occurring around can sense whether there is any sound around the sensors

and so on. Now the sensor nodes have one of the components as a sensor and these sensor

nodes collectively they form a network which is called the wireless sensor network these

wireless sensor networks are very popular currently and they have gained popularity. Since

over a decade now sensor networks are very popular because of diverse types of applications

Journal of Information and Computational Science

Volume 9 Issue 12 - 2019

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they can be used for tracking an object in a particular terrain [1]. These can be used for

medical purposes for healthcare for space applications for agriculture and so on and so forth

there are large number of different applications of wireless sensor networks wireless sensor

networks are key to the formation of internet of things.

In particular, the designers of WSN should address the issues of reliability, clustering,

security, aggregation, localization, event scheduling, fault detection, and energy-aware

routing. Machine learning is a branch of artificial intelligence (AI) which provides the

capacity to automatically learn and refine from experience without being specifically

programmed. Machine learning concentrates on the creation of computer programs which

can access data and use to learn for themselves [10]. It has an important role in several

applications of WSN due to the following reasons:

WSN usually monitors the dynamic environments WSN may gather information

about unreachable locations in exploratory applications

Since WSN is deployed in complicated environments, it is impossible to develop an

accurate mathematical model to describe the system behavior

Due to the excessive amount of data, network designers may be unable to find the

correlations among them

Integration of WSN with new technologies such as IoT, Cyber-physical system has

been introduced; it needs a greater number of smart decision making

The application of advanced machine learning techniques in WSN has been increased

recently. Machine learning is considered as a field of themes and patterns.

Machine learning algorithms are very flexible to apply for many WSN applications. ML

algorithms are often categorized as supervised, unsupervised and reinforcement. Labeled

training dataset is provided with the supervised learning algorithm. To symbolize the

relationship between input and output, a system model is created using the data set. In case

of unsupervised learning, labeled data is not provided with the algorithm. By finding the

similarities between the input samples, it classifies the samples into different groups

(clusters). In reinforcement learning algorithm, the agent learns by means of communicating

with its surroundings. There is a need for a review of Application of ML algorithms

specifically for clustering and data aggregation since not many papers are specifically

discussing the about the Machine Learning algorithms for Clustering and data aggregation in

WSN.

The paper is organized as follows. The second section contains machine learning algorithms

which are generally used in WSN. Section 3 provides different machine learning algorithms

which are used for clustering and data aggregation. Performance analysis and comparison of

different Machine Learning methods are provided in the fourth section. Section 5 provides

an overview of proposed method and the paper is concluded with section 6.

II. MACHINE LEARNING TECHNIQUES IN WIRELESS SENSOR NETWORKS



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Machine learning methods are broadly classified as supervised, unsupervised and

reinforcement learning.

A. Supervised Learning

Supervised learning has the concept of learning from examples. The model is learned by the

relationship between input and output parameters. This approach is used to resolve the

various issues of WSN such as query processing, localization, event detection, object

targeting, medium access control, and security.

B. Unsupervised Learning

In unsupervised learning, No output vectors or labels are provided with the algorithm. The

datasets are classified by finding the similarities between them. These type of algorithms

mainly used for clustering and data aggregation process in WSN. Unsupervised learning

algorithm determines the concealed relationships and it is used for addressing the problems

in WSN, where the relationship between the variables is complex. Two important algorithms

of this type are PCA (Principle Component Analysis) and K-Means clustering.

C. Reinforcement Learning

Reinforcement learning allows the agent to learn from the environment by interacting with

it. Here, sensor nodes learn to capture the best measurement in order to maximize the

advantage. The most famous reinforcement learning algorithm is Q-learning, in which every

node tries to extract measurements which are expected to increase the rewards. Sensor nodes

regularly update the rewards it achieve based on the action taken at a given state . The

diagrammatic representation of Q-learning method is shown in Fig. 1. Total rewards of

future can be computed by the equation:

𝑄(𝑆𝑡 + 1, 𝛼𝑡 + 1) = 𝑄(𝑆𝑡, 𝛼𝑡) + 𝛾(𝑟(𝑆𝑡, 𝛼𝑡) − 𝑄(𝑆𝑡 , 𝛼𝑡)) − −(1)

(,) indicates the incentive for taking the action at at state St and γ is the rate of learning,

which decides how frequently the learning happens. (Between the values 0 and 1).

Fig. 1: Visualization of Q-learning method

III. CLUSTERING AND DATA AGGREGATION



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In WSN, it is difficult to transmit huge data directly to the sink. Data aggregation is the

effective solution for this problem. In data aggregation, data is collected from neighboring

sensors and the aggregator node choose them based on the aggregation model and the fused

information is sent to the base station with appropriate routing mechanism [9]. The routing

structure determines the efficiency of aggregation process. In WSN, nodes are classified into

three types in connection with aggregation: sensor nodes (sense the data), aggregator node

(performs aggregation function) and the querier node (sends the query). Aggregator node

collects data from many sensor nodes; aggregate the gathered information using aggregation

functions (e.g. COUNT, MAX, SUM, MIN) and then send the output to sink node. This

mechanism eliminates the redundancy in the collected data and improves network life time

by reducing the number of packet transmission. Fig. 2 shows the difference between data

aggregation and non-aggregation models. As the effect of aggregation process, number

Fig. 2: Aggregation and Non aggregation model

of packets and number of collisions are reduced and hence the number of retransmissions

also. Less number of retransmission solves wastage of power and time and increases the

network throughput.

Fig. 3 shows data aggregation in WSN, based on the clusters. From first cluster, the cluster

head n11 sends the value 20 to next cluster through intermediate nodes. Cluster head n1 has

a value 21 and n1 aggregate it with previous value and the aggregated value 41 is forwarded

to base station. From cluster heads n14 and n7 have the values 24 and 25 and the aggregator

combines it send as 49 to Base Station (BS). BS aggregates both the values 41 and 49 came

from four different clusters and send the forward the value as 90. Different works are

proposed for the efficient selection of cluster head and aggregating data using machine

learning techniques.



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Fig. 3: Data aggregation example in a clustered architecture.

A. Location Based Distributed Spectral Clustering

The Distributed spectral clustering algorithm is used to create clusters of sensor

nodes based on their position in a sensor network [3]. For machine learning applications in

sensor networks, a collection of data at an aggregator node make it easier for attackers and

hence leads to data congestion. A strong distributed clustering technique is proposed to

avoid this problem. The algorithm is a combination of distributed eigenvector computation

and K-means clustering. For computing eigenvector of the graph Laplacian, a distributed

power iteration scheme is employed [3]. At steady state, every node meets at a value in the

eigenvector of the algebraic connectivity of the graph Laplacian. By using K-means

algorithm, clustering is done on the eigenvector. Information about the sensor’s location is

used only for creating the topology of WSN. The location based algorithm performs for

every graph structure which is connected.

B. Electing Cluster Head (CH) Using Decision Trees

Ahmed et al.[5] proposed a method based on decision tree for solving the problem of

CH selection. The method employs many important features during the iteration of input

vector by decision tree. The features are level of battery, distance to cluster centroids,

indications of vulnerability and degree of mobility. The simulation shows that the technique

increases the performance of CH elections while comparing with Low Energy Adaptive

Clustering Hierarchy (LEACH) algorithm and Analytical Hierarchy Process (AHP). There

are two phases in CH election, Startup phase and Steady phase. In the first step, a set of

nodes are selected arbitrarily as cluster heads. Enquiry message is broadcasted to all sensor

nodes in the network from the BS (Base Station). Node replies to the base station through

CH by sending the control information. After getting control information from every node,



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decision tree algorithm is employed for selecting a new set of suitable cluster heads. Then it

sends the list of the Cluster Heads to every sensor nodes.

CHs broadcast the notification to all the sensor nodes. After the process, each sensor node

attaches itself with single Cluster Head. This attachment is done by estimating the RSS

(Received Signal Strength) from the Cluster Heads. As the final stage of startup phase, every

node send the request for the attachment to a particular Cluster Head and CHs broadcast the

list of cluster members to other nodes. The steady-state stage is partitioned into frames. In

every frame, nodes send their data to the Cluster Head and CHs transfer the gathered

information to the sink situated in the remote location. After every round, the role of being a

CH rotates to balance the load of working as a Cluster Head. This CH selection can be

repeated either on specified time interval or based on a threshold value in the battery.

C. Data Aggregation With Self-Organizing Map (SOM)

Self-Organizing Map (SOM) is an unsupervised learning technique to map from high

dimensional space to low dimensional space. [4] Discussed unique network architecture:

Cluster-based self-Organizing Data Aggregation (CODA). The model trains the nodes such

that those are able to classify using SOM algorithm and reduces the traffic and energy

consumption. Here, winning neuron is represented as j*, and is having a weight vector w(t).

The weight vector is very close to input vector, x(t).

𝑗∗ = 𝑎𝑟𝑔𝑚𝑖𝑛|𝑥𝑗(𝑡) − 𝑤𝑗(𝑡)| 𝑗 = 1,2, … 𝑁 − −(2)

Winning neurons can be updated as:

𝑤𝑗(𝑡 + 1) = 𝑤𝑗(𝑡) + ℎ(𝑡) (𝑥𝑗(𝑡)𝑤𝑗(𝑡)) − − − (3)

w(t) and w(t + 1) shows the neuron values at time t and t + 1. Gaussian neighborhood

function, h(t) is given:

ℎ(𝑡) = 1√2𝜋𝑗⁄ − − − (4)

Using this architecture, CODA reduces traffic and energy consumption and hence increases

the quality of data.

Visual representation of the map based on the initial random weight vector and the map after

1000 iterations is shown in Fig. 4.



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Fig. 4: Map based on initial random weight vector and the map after 1000 iterations

running

D. Collaborative Data Processing Through K-Means Algorithm

Li et al. discussed the basic notions for distributed detection and tracking of single target

with WSN. From monitoring environment, information is collected by collaborative signal

processing framework. The collaborative data processing method can additionally track

several targets if it uses both K-Nearest Neighbor (KNN) and Support Vector Machine

(SVM). Surveillance system captures a huge amount of data from cameras. It requires high

computation and analysis together, and leads to the implementation of practically feasible

techniques. So Tseng et al [6] proposed iMouse which is Integrated Mobile Surveillance and

Wireless Sensor Networks. iMouse adopts high powered mobile sensors for enhancing

conventional surveillance system.

By using K-Means algorithm, this method classifies the monitored field into different

clusters. Each mobile sensor is repeatedly monitors each cluster. Since the implementation is

simple, the idea of data processing by K-Means is attractive. But still, they are vulnerable to

outliers and initial selection of seeds.

E. Role-Free Clustering With Q-Learning

Forster and Murphy discussed clustering method for WSN known as “Role-Free Clustering

with Q-Learning for Wireless Sensor Networks (CLIQUE) . In Role free clustering, each

node checks its capacity to perform as Cluster Head. Role Free Clustering has no CH

election process and in which each node checks its ability to act as a CH. CLIQUE uses Q-

Learning algorithm along with some of the active parameters like energy level. Every sensor

node is an independent learning agent, and actions are routing options using distinct

neighbors as the next hop closer to the Cluster Head. CH is described as the cluster node

with smallest routing cost to all sinks.

F. Gaussian Process Models for Censored Sensor Readings

Gaussian Process (GP) is a mixture of random variables (stochastic variables) with

parameters of mean and covariance. Ertin proposed a method to initialize probabilistic

fashion of different readings primarily based on Gaussian regression. It is a unique modeling

method to create probabilistic fashions for sensor readings. Gaussian process model for

censored sensor reading is an extended version of the Gaussian technique regression and

applies to sensor readings having continuous values. Consider censored variable as a

combination of normal and binary random variables.



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Gaussian process gives natural way to combine information to determine the parameters of

the procedure. GP fro censored sensor readings represents the anisotropic character of the

propagation traits and makes use of the implicit records from the packet reception related

issues.

G. Learning Vector Quantization for Online Data Compression

Some algorithms may not require the complete knowledge about the topology of network.

Adaptive Learning Vector Quantization (ALVQ) is one such method [8]. It extracts

compressed versions of sensor readings. The use of records correlation and historical

patterns, Adaptive Learning Vector Quantization uses the LVQ method for predicting the

code-ebook by the use of beyond training samples. This algorithm reduces the bandwidth

needed for transmission and increases the recovery of the original data from the compressed

data. For code book construction, data is divided into different data pieces (DP) with same

size 𝑊(𝑊 = 𝑛1/2). For every DP, X in the training set, find the best CDP(Code Book

Piece) that can approximate it with less error. This DP is represented as CDPi and CDPi

updation is:

𝐶𝐷𝑃𝑖 = 𝐶𝐷𝑃𝑖 + 𝛼 [𝑋 − 𝑏

𝑎− 𝐶𝐷𝑃𝑖] − − − (5)

a, b are regression parameters; α is training parameter and 0<α<1.

After testing all the DPs in the training data set, code book is adjusted and transmitted to the

BS. Dynamic Bandwidth Assignment Algorithm (DBA) is introduced here. Representation

of DBA model is shown in Fig. 5.

It is mainly for balancing the qualities of sensor data transmission and compression. The

compression quality (Q1,Q2,…Qk) of every sensor is collected by the cluster head and the

assigned bandwidth for last transmission between sensors and cluster head are B1, B2,. Bk.

The computation of average quality of compression can be done by:

𝑄𝐴 = ∑𝑄𝑖

𝑘⁄

𝑖=1,,,,𝑘

− − − (6)

For later data transmission, CH assigns bandwidth to sensor i as Bi α(Qi-QA). α is adjusting

parameter for bandwidth.

I. Data Aggregation Using Principal Component Analysis (PCA)

Data Aggregation based on PCA examines based on projection and not based on eigen

vectors. It is valid to maintain certain mean squared error (NMSE) threshold in signal

recovery and decrease the energy consumption [2]. The method decreases transmissions to

the sink and one or more aggregation nodes within the sensor network. The algorithm is

implemented with less energy consumption and includes signaling and data compression for



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reduced transmission. Uncompressed data is transmitted by leaf nodes and cluster head

(Aggregator node here) does the processing and send the compressed data to sink in order to

reduce energy consumption and balance the overall energy consumption of the network

Fig. 5: Dynamic Bandwidth Assignment model

.

Readings from different sensors may not be correlated or data from same clusters are more

compressible. PCA based aggregation is not limited to the single-hop network. The main

concentration is on how it handles with non-ideal projection basis and synchronizes between

two sensor nodes and it is applied to any network in which more than one aggregator nodes

compress the data and send to sink. Fig. 6 (a) shows the cluster with data aggregation node

and data block to the sink. In (b), X0, Xk, X2k, X3k represent the data blocks to be sent to the

sink.

Fig. 6: (a) Cluster with a data aggregation node (b) Data blocks to the sink

IV. PERFORMANCE ANALYSIS



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The different Machine Learning methods used for data aggregation and clustering in WSN

such as Support Vector Machine, Neural Networks, Decision Tree, K-Means, Q-Learning

etc,.. are discussed above. The performance of different schemes are compared by

considering the important parameters of the WSN such as delay, complexity, energy

consumption, topology awareness and overhead. Table 1 and 2 compare the methods for

clustering and data aggregation using certain parameters mentioned above. The clustering

complexity is low when it uses K-Means, Q-Learning and Decision Tree. Q-Learning,

Decision Tree and Neural Networks provides very low overhead to the network. The

methods using Q-Learning algorithm and Decision Tree employ less delay for the Wireless

Sensor Network.

TABLE I: Comparative analysis of different machine learning based methods used for

clustering and data aggregation

MECHANISMS MACHINE LEARNING

ALGORITHM DELAY OVERHEAD

Location based distributed

clustering K-Means Low Moderate

Collaborative Data

Processing K-Means Moderate Moderate

Electing Cluster Head Using

DT Decision Tree Low Low

Online Data

Compression LVQ High High

Large Scale Network

Clustering

Using NN

Neural network Moderate Low

Role Free Clustering Q-Learning Low Low

Gaussian

Process Model GPR High Moderate

Data

Aggregation

Using SOM

SOM High High

Data

Aggregation

Using PCA

PCA Moderate High

TABLE II: Performance analysis of different machine learning based methods used for

clustering and data aggregation



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MECHANISMS TOPOLOGY

AWARENESS

BALANCING

ENERGY COMPLEXITY

Location based distributed

clustering K-Means Yes Moderate

Collaborative Data Processing K-Means Yes Low

Electing Cluster Head Using DT Decision Tree Yes Low

Online Data

Compression LVQ No High

Large Scale Network

Clustering

Using NN

Neural network Yes Moderate

Role Free Clustering Q-Learning No Low

Gaussian

Process Model GPR No Moderate

Data

Aggregation Using SOM SOM No Moderate

Data

Aggregation

Using PCA

PCA Yes Moderate

V. PROPOSED DATA AGGREGATION METHOD

An improved clustering and data aggregation method is proposed by assuming

predetermined Cluster Heads are having enough energy for transmission and computation.

Here, cluster formation is based on the similarity of data from the sensor nodes. After node

deployment, the data similarity between the neighboring nodes is measured in terms of data

correlation. An appropriate threshold value can be set. If the correlation metric is greater

than the threshold value, these two data belong to cluster with similar data.

Similar data clusters only require data aggregation process. Dissimilar data clusters normally

send data to CH and finally forward to the sink node. In the case of similar data cluster, a

cluster based data aggregation method using ICA (Independent Component Analysis) is

employed. Data aggregation is done by the CH. ICA is computationally efficient for data

reduction. It minimizes mutual information using the concept of differential entropy.

Aggregated data from similar clusters are forwarded to sink node. Hence the computation

and energy consumption can be reduced due to less number of aggregation processes. The

flow diagram of proposed methods is shown in Fig. 7.



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Fig. 7: Flow diagram of proposed data aggregation method

VI. CONCLUSION

There is an increasing interest in Wireless Sensor Network application now days. Wireless

Sensor Network use data aggregation for saving energy by reducing the number of

transmission. WSN needs innovative solutions to overcome the challenges and limitations

faced by the network. Machine learning algorithm gives a group of methods for increasing

the capability of network to adapt with the dynamic environment. The paper presents a

review of different machine learning algorithms which are used for clustering and data

aggregation in Wireless Sensor Networks. Comparisons of the performance of such methods

are given by Table 1 and Table 2. It compares the methods using selected parameters such as

complexity, delay, overhead, topology awareness and balancing energy consumption. An

improved similarity based clustering and data aggregation using Independent component

Analysis is proposed in order to reduce the energy consumption and computation in the

network.

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[2]. Morell, A. Correa and M. Barceló and J. L. Vicario, Data Aggregation and

Principal Component Analysis in WSNs,IEEE Transactions on Wireless

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[3]. G. Muniraju, S. Zhang, C. Tepedelenlioglu and M. K. Banavar Location

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