Cluster Based Data Collection in Wireless Sensor Networks

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Cluster Based Data Collection in Wireless Sensor Networks

Sanjay Purahit¹ , Sapana Singh²

1M.tech Student, IIMT engineering college, Meerut

2Astt. Professor,IIMT engineering college, Meerut

ABSTRACT

Wireless sensor networks have been acquisition interest as a raised area that changes how interact with the physical world. Sensor networks are scattered event-based systems that differ from traditional communication networks in several methods: Clustering is one of the key techniques which reduce energy consumption in WSN. A large class of Wireless Sensor Networks applications involves a set of isolated urban areas (e.g. building blocks) covered by sensor nodes (SNs) monitoring environmental parameters. Mobile sinks (MSs) mounted upon urban vehicles with fixed paths provide the ideal infrastructure to effectively retrieve sensory data from such isolated WSN fields. The main objective is to reduce the overall network overhead and energy expenditure associated with the multihop data retrieval process while also ensuring balanced energy consumption among SNs and prolonged network lifetime. The various algorithms are used to partition the neighboring nodes, which will sense similar sensor nodes into one cluster and wireless sensor nodes autonomously adjust control parameters after observing its environments. This is achieved through building cluster structures consisted of member nodes that route their measured data to their assigned cluster head (CH). It does can efficiently reduce redundant data transmission of cluster Head. In this paper various method has been reported and compared.

1. INTRODUCTION

1.1 WIRELESS SENSOR NETWORK

A sensor node is used for data collection as well as for correlation, network analysis, and fusion of its own sensor data and data from other sensor nodes.

A wireless sensor [17] is a sensing component but it also performs processing, communication, and storage capabilities. Sensor nodes communicate with each other and also with a base station (BS) using their wireless radios, allowing them to disseminate their sensor data to remote processing,

analysis, visualization, and storage systems. A sensor node has onboard storage, and embedded processing capabilities. Energy consumption is the basic issue for the operation of WSNs. It is not feasible to replace the batteries of the sensor nodes for most applications. The lifetime of the network is limited because of limited battery power. The power is a scarce resource in WSNs due to size and cost limitations of sensor nodes.

Wireless sensor networks have many applications.

Some of them are futuristic while a large numbers of them are in practical use. The diversity of applications in the latter category is remarkable – target tracking, environment monitoring, pipeline like water, oil, gas monitoring, structural health monitoring, agriculture, health care, supply chain management, active volcano monitoring, transportation, human activity monitoring, and underground mining, etc.

B A C E D Sink

Sensor Field Sensor Nodes Internet &

Sattelite

User

Figure 1.1: Architecture of Wireless Sensor Network

In figure 1.1, different types of sensors are there that sense the data and send these data to the base station (sink). The base station collects these data and performs analysis. After analysis on that data, the results are distributed to the different type of

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1.1.1. Characteristics of Wireless Sensor Network

The main characteristics [55] of a WSN include:

1. Power consumption constrains for nodes sing batteries or energy production

2. Deal effectively with node failures 3. Nodes mobility

4. Ability to manage communication failures 5. Nodes heterogeneity

6. Ability to large scale of deployment 7. Ability to cope with abrasive environmental conditions

8. Data collection

9. Main focus on energy saving

1.1.2 Protocol Stack in WSN

The sensor nodes have two responsibilities, data originators and data routers in WSN. There are two reasons for communication:

Source functionality: In order to transmit their packets to the base station, source nodes accomplish communication functionalities with event information.

Router functionality: Sensor nodes forwarding the packets to the next destination and then forwarding those packets in the multi-hop route to the base station.

In figure 1.2, the protocol stack is used by the sink and all sensor nodes. This protocol stack combines power and routing awareness, integrates data with networking protocols, promotes cooperative efforts of sensor nodes, and communicates power efficiently through the wireless medium. In this stack, mobility management plane, power management plane, task management plane, localization plane, synchronization plane, and topology management plane are also available.

Basically the protocol stack consists of the physical layer, data link layer, network layer, transport layer, application layer. The physical layer addresses the needs of robust modulation, transmission and receiving techniques. The data link layer takes care of securing reliable communication using error control mechanism, since the sensor nodes can be mobile and environment is noisy. And also cope with channel access using MAC layer to reduce collision with neighbors’ broadcasts. The network layer is taking responsibility of routing the data serviced by the transport layer. The transport layer helps to

maintain the flow of data. Depending on the sensing tasks different types of application software can be built and used on the application layer.

Topology Management Plane Synchronization Plane Localization Plane

Task Management Plane

Mobility Management Plane

Power Management Plane

Application Layer

Transport Layer

Network Layer

Data Link Layer

Physical Layer

Figure 1.2 The Sensor Network Protocol Stack 2. CLUSTER BASED DATA

COLLECTION IN WIRELESS SENSOR NETWORK

In a cluster based network, each cluster has a different number of cluster members and single cluster head. Cluster based data collection is a technique in which data are collected by cluster members from environment. The cluster member send data to own cluster head and the cluster head send data to base station or sink.

2.1 SINGLE CLUSTER BASED DATA COLLECTION

Representative architecture contains only single cluster that contain three different network entities can be identified as follows:-

Cluster Member: A cluster member, also known as a mote, is a node in a wireless sensor network that is capable of performing collecting sensory information and communicating with cluster head in the network.

Cluster Head: cluster head is responsible for coordinating cluster activity and forwarding data to the base station, its energy requirements will be significantly large compared to other sensor nodes.

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30 Sink or Base Station: Base stations are responsible to receive the data from the cluster head and perform analysis on that sensing data.

In Figure 3.1 Each cluster member perform the local approximation on the own raw data that is sense by itself. After performing the local approximation, these local approximate data are sending to the cluster head. The cluster head performs the global approximation on the local approximate data. And send these global approximate data to the sink or base station.

CH Local Data

Approximation

Local Data Approximation

Local Data Approximation Raw Data

Raw Data

Raw Data CM1

CM2

CM3

Global Approximated Data Global Data Approximation

Sink CM = Cluster Member

CH = Cluster Head Local

Approximated Data

Local Approximated Data

Figure 2.1- Overall Architecture of Single Cluster Based Data Collection

2.2 MULTIPLE CLUSTER BASED DATA COLLECTION

In multiple clusters based data collections that contain the multiple numbers of clusters & a single base station. All cluster heads collect local approximate data from all its own cluster members and calculate the global approximate data. After finding the global approximate data, all cluster heads sends these data to the sink or base station.

Sink

Cluster Head Cluster Member Approximate Data Approximate

Data

Approximate Data Wireless Sensor Network

Figure 2.2 Overall Architecture of Multiple Cluster Based Data Collection

Figure 2.2 based on the multiple cluster based data collection. This architecture contains the multiple clusters. Each cluster has a several cluster member and single cluster head that are already discussed in the single cluster based data collection. Each cluster head send the approximate data to the base station and sink.

3. MODULES DESCRIPTION

1. Create a collection of sensor nodes in network 2. Cluster formation and cluster head selection module

3. Approximate data collection module

4. Comparison between NDCA and ADCA based on energy parameter

3.1. Create a Collection of Sensor Nodes in Network

In this module we will create a simple collection of wireless sensor nodes in which each node have its own energy level and weight. Each node has its own routing table in which neighbors of the node are described. All the nodes in this network are static.

3.2. Cluster Formation and Cluster Head Selection Module

Sensor nodes are heavily distributed in wireless sensor network area. It means very similar data in close by sensor node would produce by physical environment and sending those data to the sink is less or more redundant [2] [51]. Because of all these evidences, some kind of grouping of sensor nodes is created. These groups of sensor node can be incorporated or abbreviate data together and transmit only consolidated data. This can minimize global data and also minimize localized traffic in distinct group. This grouping process of sensor nodes in a densely deployed large scale sensor network is known as clustering. In this module, create a cluster and then select a cluster head in this cluster with the help of algorithm [30].

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Sink or Base Station Cluster Head

Cluster Member

Figure 3.1 Clusters in Wireless Sensor Network

In this module, we select the cluster head based on the weight of the nodes. In wireless sensor network each node has a routing table which contains the information about neighbor nodes. Those nodes have the maximum number of neighbor are selected as the cluster head and based on the communication range from cluster head to neighbors then select the cluster member. After selecting the cluster member, the cluster head send the message to all cluster members “I am a cluster head “

3.3 Approximate Data Collection Module Approximate data collection module is divided into two different modules:

1. Local data approximation module 2. Global data approximation module

3.3.1 Local Data Approximation Module In local data approximation [6], each cluster member collects a set of sensed raw data (e.g.

temperature) from the environment. Now local approximation function is applied to all cluster members within their cluster and find out the local approximate data on each cluster members. The cluster member will not send the sensed raw data to the cluster head. Instead it will only send the approximate data to the cluster head within their cluster.

Let T₁, T₂, T₃, T₄…….. T_j are the set of sensed raw data value by each cluster members. In particular time interval, Local approximate data of each cluster members is calculated using Lagrange’s function from equation (3).Now we can find eq (1) LADi = min { T1 , T2 , T3 ,………. Tj }

Or (1)

LAD_i = max { T_{1 ,}T_{2 ,}T3 ,………. T_j }

In equation 1, where “i” is the number of cluster members in a cluster and “j” is the number of raw data values. LAD is the local approximate data that is calculated on each cluster members within a cluster and will send this local approximate value to the cluster heads.

3.3.2 Global Data Approximation Module In global data approximation [6], all cluster members send their local approximate data values to the cluster head within a cluster. So global approximation function is applied to cluster head .Global approximate data value is calculated by using Lagrange’s function from equation (3).Now we can find eq (2)

GAD = min {LAD1, LAD2, LAD3, LAD₄…………..LAD_i}

Or (2)

GAD = max {LAD₁, LAD₂, LAD₃, LAD4…………..LAD_i}

In equation (2) where i^thcluster member have LAD1, LAD2, LAD3…….LADi , the local approximate data within their cluster. Using equation (2) we will find out global approximate data (GAD) value on cluster head and send this global approximate data value to the sink or base station.

Lagrange’s Formula:

( )

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

( ) ( )( ) ( ) ( ) ( )( ) ( )

( )( ) ( ) ( )

…………. (3)

Here “x” is given as time “t” and “f(x)” is given as temperature “T”.

Algorithm:

The following algorithm implements Lagrange’s method to determine the maximum or minimum of a function ( ).

Initialization

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32 Determine a reasonably good estimate

x

₀ for the maxima or the minima of the function ( ).

Step 1

Determine ( ) and ( ) . Step 2

Substitute x_i_₁, the initial estimate

x

₀ for the first iteration, ( ) and ( ) into eqn. 3 to determine

x

iand the function value in iteration i. Step 3

If the value of the first derivative of the function is zero, then you have reached the optimum (maxima or minima), otherwise repeat Step 2 with the new value of

x

_i until the absolute relative approximate error is less than the pre-specified tolerance.

Example of Approximate Data Collection Approach

In this example [6], we are going to present an approximate data collection technique in which a sensor node will only transfer the approximate data rather than raw data in sensor network. This technique reduces the redundant data within the networks and makes the wireless sensor network more energy efficient.

In figure 4.2, there are total number of five nodes and one base station. In five nodes, one is cluster head and remaining four are cluster members. Each cluster member CM1,CM2,CM3, and CM4 are collecting three temperature value T1 , T2 , T3 and calculate optimum local approximate value LAD1 , LAD2 , LAD3 , LAD4 respectively. Then send these local approximate values to their cluster head CH.

Now cluster head CH calculate optimum global approximate value GAD from the values given by cluster members and then CH send the GAD value to the sink or base station.

CM1 CM2

CM3 CM4

CH

Base Station

T11= 25 ̊ C T₁₂= 26 ̊ C T13= 24 ̊ C

T21= 24 ̊ C T₂₂= 25 ̊ C T₂₃= 23 ̊ C

T₃₁= 23 ̊ C T32= 22 ̊ C T₃₃= 21 ̊ C

T₄₁= 22 ̊ C T42= 24 ̊ C T₄₃= 23 ̊ C LAD₁= 24 ̊ C

LAD₃= 21 ̊ C

LAD2= 23 ̊ C

LAD₄= 22 ̊ C

GAD =21 ̊ C

= Cluster Head

= Cluster Member

Figure 4.2 Cluster Based Approximate Data Collection

In above figure, raw data of cluster member CM1 are -

T11 = 25 C, T12 = 26 C, T13 = 24 C.

Now using equation 1, CM1 calculates LAD1 = 24 C.

Like CM1, all cluster member CM2, CM3, CM4 calculates LAD₂ = 23 C, LAD₃ = 21 C, LAD₄ = 22 C respectively using equation 1.Now using equation 2, cluster head CH calculate GAD = 21 C.

Now CH sends the value of GAD to the base station. So redundant value will be removed and there is no wastage of energy to transfer duplicate value.

3.4 Comparison between NDCA and ADCA based on Energy Parameter

In this module, we are showing the comparison between normal data collection approach &

approximate data collection approach based on energy parameter. So we will show how much energy consumption of the overall network when we use the normal data collection approach, using graph and how much energy consumption of the overall network when we use approximate data collection approach, using graph. If we use Normal data collection approach then network requires more energy and this network is not energy efficient but when we use the approximate data collection approach then this reduces the redundant data within the networks and makes the wireless sensor networks more energy efficient.

4. DESIGN

4.1 Data Flow Diagram of the Overall System

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Cluster Member (Sense Temperature, Pressure)

Raw Data

Perform Local Data Approximation

Cluster Head

Perform Global Data Approximation

Sink or Base Station Local Approximate Data

Local Approximate Data

Global Approximate Data

Figure 4.1 DFD of the Overall System

Figure 4.1 shows the data flow diagram of the overall system. In this diagram, the cluster members sense the temperature or pressure of the system and send these raw data to the local approximation model where cluster member find the local approximate value of the raw data. After finding the local approximate data value, cluster head collects all these local approximate value from each cluster members. And perform the global approximation function on cluster head and send this global approximate data value to the sink or base station.

4.2. DFD of Approximate Data Collection Data flow diagram of the approximate data gathering is divided into two part as:

1. DFD of Local data approximation 2. DFD of Global data approximation 4.2.1. DFD of Local Data Approximation In 3rd module perform the local data approximation. Figure 4.4 shows the data flow diagram of local data approximation. In this diagram, cluster member send the raw data to the local data approximation process in which find the local approximate value of the raw data. After find the local approximate data, these local data value are send to the cluster head.

Cluster Member

Local Data Approximation

Cluster Head Raw Data

Local Approximate Data

Object

Process

Flow

Figure 4.2 DFD of Local Data Approximation

4.2.2. DFD of Global Data Approximation In 3^rd module cluster head perform the global data collection. Figure 4.5 shows the data flow diagram of global data approximation .In this diagram; the cluster head receive the local approximate data from cluster members and send these data to the global approximation model. Now to reduce the data, global approximate function is applied on cluster head to find global approximated data.

These global approximate data are sending to the sink or base station for the analysis.

Cluster Head

Global Data Approximation

Sink or Base Station

Local Approximate Data

Global Approximate Data

Object

Process

Flow

Figure 4.3 DFD of Global Data Approximation 5. CONCLUSION AND FUTURE WORK

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34 We have studied that how a cluster member using local approximation to send a data value to its cluster head and how a cluster head using global approximation to send a data value to the base station (sink). These techniques are useful for large amount of sensor nodes distributed in network. As sensor nodes are battery driven, in order to use network for long duration, an efficient utilization of power is essential. So we are using the approximate data collection approach that makes the energy efficient wireless sensor network and also reduce the data traffic inside the networks. The approximate data collection is also enhancing the life time of the wireless sensor network.

My future work is to use another mathematical model for approximate data collection that makes the wireless sensor networks area more energy efficient and also use another algorithm for cluster formation and head selection.

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