A. Chalak, S. Misra and M. S. Obaidat Corresponding Address: Mohammad S. Obaidat, Fellow of IEEE and...

A. Chalak, S. Misra and M. S. Obaidat Corresponding Address:

Mohammad S. Obaidat, Fellow of IEEE and SCSPresident, Society for Modeling & Simulation International

(SCS) Editor-in-Chief, International Journal of Communication Systems, Wiley

Professor of Computer Science and Software Engineering,Monmouth University, W. Long Branch, NJ 07764, USA

E-mail: [email protected]: www.monmouth.edu/mobaidat

Professor Mohammad S. Obaidat 1

A CLUSTER-HEAD SELECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS

mailto:[email protected]

http://www.monmouth.edu/mobaidat

Objectives

• The aim of this paper is to present a new energy-efficient clustering approach for WSNs called Improved Minimum Separation Distance(IMSD).

• In order to prolong the network lifetime, energy resources of each node in WSN need to be effectively managed.

• With respect to energy efficiency, the best known protocols are hierarchical in nature. Energy consumption can be reduced efficiently by using clustering based hierarchical routing protocols.

• IMSD extends the existing MSD clustering algorithm. The simulation results show that IMSD is more energy-efficient, implying that it is more effective in prolonging the network life time.

Prof. Mohammad S. Obaidat

Introduction

• Sensors in WSN are small, inexpensive, low-power, intelligent and disposable. The sensor nodes are self-configuring and contain one or more sensors, integrated with wireless communication devices and data processing components and a limited energy source.

• Due to the large number of nodes and the possibly hazardous environment in which these nodes are deployed, their batteries are often assumed to be nonreplaced.

• The failure of a single node in the network could possibly cause network partition and dissect a part of the WSN off from the rest of the network.

• Network lifetime is, therefore, dependent on the lifetime of individual nodes. This raises the issue of energy-efficient design of the network. This research effort focuses on the design of an energy-efficient cluster head selection algorithm for WSNs.


Introduction

• In a WSN, there are possibly very large number of sensing nodes, and a base station.

• The sensing nodes have to route data about their environment to the base station. A sensing node is sometimes called a source and a base station is sometimes called a sink.

• The sink node collects and interprets the data from all the source nodes in the network. The sink node may be connected to a wired network and may not have an energy limitation. The source nodes, on the other hand, are dependent on their limited batteries and become dead when their batteries are completely exhasuted.


Introduction


Sink

Link to other Network

Nodes

Wireless Sensor Network Area

Introduction

Routing in Wireless Sensor Networks can be divided into flat-based, location-based, and hierarchical-based routing.

Flat-based routing: All nodes are typically assigned equal functionalities.

Location-based: Positions are exploited to route data in network.

Hierarchical-based: Nodes will play different roles in network.


Introduction

• WSN protocols are different from the traditional wireless protocols due to limited power supply, large network size, and inaccessible remote deployment environment.

• A lot of energy is saved by using multi-hop communication than direct communication, as in short range communication due to the fact that consumption of energy is proportional to the square of the distance.

• A routing protocol might be good for continuous data sensing while it may not perform well where it will have periodic monitoring.


Introduction

• Clustering approach is used for wireless communication in wireless sensor networks. In this case, nodes send their data to the nearest cluster head which then forwards it to the base station. In each round, the cluster head node sends out a beacon, and nodes that hear the beacon join the cluster. If there are too many nodes in the cluster, the cluster-head can reduce beacon signal strength so fewer nodes will hear it.

• On the other hand, if the cluster is too small, the cluster head can increase its beacon signal strength to increase cluster membership. Thus, energy efficiency is increased by using clustering technique.

• In this paper, we present the Improved Minimum Separation Distance (IMSD) cluster head selection algorithm, which is based on the total average energy available in the network.


Terminology

LEACH Low Energy Adaptive Clustering HierarchyMSD Minimum Separation DistanceMST Minimum Spanning TreeBS Base Station/SinkCH Cluster Headdec_MSD Decrease MSD by 10%LCF Link Cost FactorSEP Stable Election Protocolgap decrease MSD by 10%


RADIO COMMUNICATION MODEL

• In a WSN, there are two types of energy costs; fixed cost in the electronics when transmitting or receiving a message, and variable energy costs which are proportional to the distance of transmission.

• In our work, we consider a simple model where the radio dissipates = 50 nJ/bit to run the transmitter 𝐸𝑒𝑙𝑒𝑐or receiver circuitry and = 100 pJ/bit/ 2 for the ∈𝑎𝑚𝑝 𝑚amplifying signal.

• These parameters are slightly better than the current state-of-the- art in radio design. We also assume d2 energy loss due to channel transmission.



• Thus, to transmit a k bit message with distance d and considering free space model, the radio model as shown in Figure 1 expends:


and to receive this message, the radio expends:


• For these parameter values, receiving a message is not a low cost operation; the protocols should thus try to minimize not only the transmit distances, but also the number of transmit and receive operations for each message.

• An inefficient use of the available energy leads to poor performance and short life cycle of the network.

• Energy in these sensors is a scarce resource and must be managed in an efficient manner. The most significant factor in the development of WSN is typically power. It is necessary that the transmission distances of nodes is reduced in order to conserve energy.


Background• Low Energy Adaptive Clustering

Hierarchy (LEACH) uses a self-configuration approach and reduces much energy consumption.

• It does not consider the distribution of nodes.

• Cluster head nodes are distributed unequally.

• Data collected by cluster-head directly forwarded to base station.

• As cluster-heads are far away from the base station. Direct transmission is bad for energy conservation.

Clusters constructed by LEACH.

Black nodes represent cluster-heads


Background (contd.)Cluster constructing method has

to be changed. The communication between

cluster-heads and base station also needs to be modified.

In this work, we present a modification of LEACH’s cluster-head selection algorithm to further reduce the total energy dissipation of sensors

Our cluster constructing method avoids the uneven member distribution for clusters.

Clusters constructed by LEACH MSD and MST.


PROPOSED ALGORITHM: IMSD• Low Energy Adaptive Clustering Hierarchy (LEACH) is a

hierarchical routing protocol. It is an energy-efficient protocol, as it group nodes into clusters.

• It uses Cluster Head (CH) to fuse data before transmitting to the Base Station (BS).

• But LEACH has some limitations:– First the geographical distribution of the CHs severely influences

the overall energy consumption of the network. Spreading the CHs more evenly results in prolonging the lifetime of the network.

– Second, in WSNs asymmetric communication is possible. That is, the base station can reach all the sensor nodes directly. While some sensor nodes cannot reach the base station directly, therefore these sensor nodes need help from other nodes to forward their data. Hence routing schemes are necessary.


PROPOSED ALGORITHM: IMSD– Third, as the network size increases, the transmission

distance within the cluster increases. This results in an increase in energy consumption.

• In this paper, we present an energy-efficient cluster head selection algorithm to overcome the above three limitations.


PROPOSED ALGORITHM: IMSD• The geographical distribution of the CHs severely

influences the overall energy consumption of the network.

• For prolonging the lifetime of the network, CHs should be spread evenly. Use of improved minimum separation distance between CHs improves the network lifetime.

• The smallest distance allowed between CHs is minimum separation distance. The minimum separation distance can be smaller than the separation distance between CHs, but should not be larger.


PROPOSED ALGORITHM: IMSD




• An extension to LEACH is used in the proposed routing technique. The cluster formation carried out by the base station uses a centralized cluster formation algorithm.

• The same steady-state protocol as LEACH is used in the proposed protocol. The current location & energy level of each node is informed to the base station during the set-up phase.

• To determine the CH and the cluster for that round, centralized cluster formation algorithm is run at base station.

• All the nodes in the network receive the information that is broadcast by base station after the creation of clusters. Before a node goes to sleep mode until it is time to transmit data to the CH, i.e., until the arrival of next slot, determination of its local TDMA is essential for each of the nodes, except the CH.



• From a list of eligible nodes, a CH is randomly chosen in the CH selection part.

• In the network, the average energy of remaining nodes is calculated to determine which nodes are eligible. The nodes with energy level above the average are eligible, in order to spread the load evenly.

• A new node has to be chosen to guarantee the minimum separation distance if a node that has been randomly chosen is too close, i.e., within the range of the minimum separation distance from all other chosen CHs.

• If the desired number of CHs is not formed, then the minimum separation distance is decremented by 10% and the same algorithm is implemented until the desired number of CHs is attained.



• Clusters are created the same way as in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, generally with at least the minimum separation distance when all CHs have been chosen and separated.

• Asymmetric communication and routing in sensor network (AROS) architecture is used for simulation purpose [6] [7] [8].

• The data is transmitted by CH to base station when the CH gathers data from its cluster nodes. This is based on AROS.

Performance Evaluation


• We have implemented cluster head selection algorithms for WSN with different protocols LEACH, MSD and IMSD. The implementations have been carried out in MATLAB.

• The following assumptions were made:1. Sensors and the base station are all stationary after

deployment; the BS is fixed at a distance from the sensor nodes.

2. All nodes are homogeneous and energy constrained with uniform energy.

1. Each sensor node initially has the ability to transmit data directly to base station.



4. Each node is assigned a unique identifier (ID).5. Nodes can adjust their transmission power according to the

distance from the receiver. 6. A node belongs to only one cluster, but may change its cluster

during each round.7. The nodes have initial uniform energy, of 0.5 Joules.8. Each sensor node is location aware and the base station has

strong computation power.9. All sensors have enough energy to communicate with the sink

directly at the initial stages.10. Base station has location and energy info of all nodes.11. The BS is fixed at a distance from the sensor nodes. Also, the

Base station has "unlimited" power supply and high calculation capacity.



• The node deployment used in the simulation study is shown in Figure 2.

• The nodes are randomly distributed across the sensor field. The base station is situated inside the field.

• Table I indicates the other parameters as set during simulation.

Proposed Approach

• Minimum Separation Distance (MSD)

• Minimum Spanning Tree within Cluster


Minimum Separation Distance (Algorithm)

MSD = Minimum Separation Distancedc = Number of desired cluster heads,energy(n) = Remaining energy for node navg =

eligible = { n | energy(n) ≥ avg }CH = { } while ( |CH| < dc )

add(n,CH)

remove(n,eligible) else

add(n,CH)remove(n,eligible)

endifendwhile

nodesaliveofnumber

nenergy )(

MSDnmdistCHmeligiblennIf )),(,(:


Minimum Separation Distance (Modified Algorithm)

avg_energy = 6.7MSD = 5m CH = { }dc = 3N = 10eligible={C, F, J, D, I}

A

G

EB

I

C

D

F

J

H

10

4

6

8

4

68

97

5


Minimum Spanning Tree

• Replacement of direct communication to MSTs in intra-cluster is the main idea.

• For large network area using direct transmissions is not energy efficient.

• Average transmission distance of each node can be reduced by using MSTs instead of direct transmissions

• Thus the energy dissipation of transmitting data is reduced.

Direct communication in LEACH


Minimum Spanning Tree (Contd.)

• All nodes including the CH are connected by a MST

• CH as the leader collects the data from the whole tree.

• CHs uses Relay nodes to forward the data to the BS.

• As network area is larger, the reduced transmission distance is greater.

• In the direct transmission, information of routing path is simple, and each node only needs to know its CH

• But in trees, each node must know the next node that it would send data to.

MST communication in LEACH


Forming Minimum Spanning Tree

• Every node in network broadcasts HELLO message with a certain frequency.

• Nodes receiving HELLO message respond back with RESPONSE message with network-id, energy-remaining, neighbor-id’s, distance between neighbors.

• With this information every node creates a spanning tree for its cluster. And informs neighbors the next node that it would send data to.

Minimum Spanning Tree (Contd.)


d < MSD

d = distance between two node

MSTd ≥ MSD

Proposed Scheme


Radio Energy Model

Transmit Electronics

Transmit Amplifier

kEelec * 2 * * dkamp

)(dETxk bit packet

d

Receive Electronics

RxE

kEelec *

k bit packet

Transmitter

Receiver

Radio Energy Model Prof. Mohammad S. Obaidat

Radio Energy Model

• In our work, we assume a simple model where the radio dissipates

While estimating the transmit energy, we assumes d2 energy loss in channel transmission.

Operation Energy DissipatedTransmitter Electronics Receiver Electronics 50 nJ/bit

Transmit Amplifier 100 pJ/bit/m2

)( kE elecTx

)( kE elecRx

)( elecelecRxelecTx EEE

amp

Radio Characteristics


Radio Energy Model

),()(),( dkEkEdkE ampTxelecTxTx

2***),( dkkEdkE ampelecTx

)(),( kEdkE elecRxRx

kEdkE elecRx *),(

Thus, to transmit a k-bit message a distance d using our radio model, the radio expends:

(1)

and to receive this message, the radio expends:

(2)


Radio Energy Model

• For these parameter values, receiving a

message is not a low cost operation; the protocols should thus try to minimize not only the transmit distances, but also the number of transmit and receive operations for each message.




• Simulation area: Defines the dimensions of the sensor field. We considered a field measuring 100 x 100 m2.

• Number of nodes: The model of sensor network that was considered consisted of 20/50/100/200/350/500 nodes randomly deployed into the field for 6 different experiments.

• Radio propagation model: Used to predict the distance from the signal power of each node. Free space model and multi path fading model are used in this simulation.



• Energy model - The energy model, which is a node attribute, represents the amount of energy in a node. Energy consumption takes place for each and every packet transmitted (tx-power) or received (rx-power).

• Communication model - The communication between the nodes is considered one way. A cluster head advertises itself to the common nodes which in turn join and transmit data to the CH. Efficient utilization of the energy resources of the sensor nodes increases the lifetime of the network. In the ideal network, all sensor nodes would live exactly the same period of time.

Assumptions

• Sensors and the base station are all stationary after deployment.

• All nodes are homogeneous and have the same capabilities.

• Each sensor node initially has the ability to transmit data directly to base station.

• Each node is assigned a unique identifier (ID).• A node belongs to only one cluster, but may change its

cluster during each round.• Each sensor node is location aware and the base station

has strong computation power.• All sensors have enough energy to communicate with the

sink directly at the initial stages.Prof. Mohammad S. Obaidat

Simulation tool• In this paper, we have analyzed the cluster

head selection algorithms for WSN with different protocols LEACH, MSD and IMSD.

• We used MATLAB for the analysis which is good for analyzing at the Physical layer.

• Matlab is a computing platform that enables various simulation projects. It has been particularly used for PHY layer study of wireless communications.


Simulation tool (cont…)• What does wireless-matlab provide?

Radio propagation: free space, two-ray, and

lognormal shadowing

Mobility: random waypoint model

PHY: broadcast, dynamic transmission rate and

power

MAC: IEEE 802.11 (CSMA/CA and RTS-CTS-

DATA-ACK)

NET: ad hoc routing.


Simulation ParametersITEM DESCRIPTION SPECIFICATION

Simulation area 100m x 100m

Channel type Wireless channels

Radio propagation model Free space model and Multi path fading model

Number of nodes 20/50/100/200/350/500

Energy model Battery

Communication model Bi-direction


Results and Analysis

• We have performed the simulation in order to determine how much we can lower the energy consumption in the sensor network

• The obtained results of IMSD through simulation are compared with the simulated results of LEACH, SEP and MSD.

• The metrics used are:1. No. of rounds of operations; a one round is specific time

slice (or period) after which the algorithm selects new cluster heads for next round/period. So, we calculate the total number of rounds the network sustains with energy dissipation at each round.

2. Average energy consumed,3. No. of nodes alive, etc.


Performance Evaluation Results


• In Figures 3 and 4, we see that a more efficient utilization of power makes the sensor network stay alive a longer period of time.

• We can also see that as soon as the sensor nodes in the network start to die, the whole network dies shortly.

• Figure 5, shows the average energy consumed for every 200 rounds of simulation. It also shows how IMSD average energy sustains for longest duration.

Performance Evaluation Results


Concluding Remarks


• In this paper, we have presented the improved MSD algorithm; having improved minimum separation distances between CHs.

• We have performed simulations in order to be able to determine how much we can lower the energy consumption in the sensor network by separating the CHs, i.e., by distributing the CHs through the whole network.

• We have shown that using improved minimum separation distance between CHs improves energy efficiency, and also the life-time of the network.

Concluding Remarks


• By using IMSD algorithm between CHs we make the network live longer.

• The number of clusters used together with the IMSD affects the energy consumption.

• Future extensions of this work includes more thorough analysis in more scenarios with varying numbers of sensor nodes and network sizes, as well as evaluating alternative algorithms for cluster head selection.

• A comparison between the improved minimum separation distance algorithm and the other available algorithms will be considered in the future.

Thank You

53Prof. Mohammad S. Obaidat

A. Chalak, S. Misra and M. S. Obaidat Corresponding Address: Mohammad S. Obaidat, Fellow of IEEE and...

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