Multi Threshold Adaptive Range Clustering

(M-TRAC) Algorithm for Energy Balancing in

Wireless Sensor Networks

Nishant Joshi D, K S Shivaprakasha, Muralidhar Kulkarni Centre for Wireless Sensor Networks, Dept of E&C

National Institute of Technology Karnataka, Surathkal, Karnataka

Email: nishantjoshi14@gmail.com, shivaprakasha.ks@gmail.com, mkuldce@gmail.com

Abstract: Wireless Sensor Networks (WSNs) have

become one of the most interesting areas of the

modern communication systems. As routing is

mainly data centric in WSNs, normal IP based

routing does not suffice. As sensor networks are

mostly deployed in areas where batteries cannot be

recharged often, energy conservation is one of the

important design parameters for a routing protocol.

Many energy efficient protocols have been proposed

in the literature. Cluster based algorithms have

proved to be better compared to flat routing. In this

paper, we propose a novel cluster based energy

efficient routing algorithm for WSNs, the Multi

Threshold Adaptive Range Clustering (M-TRAC)

algorithm, which incorporates centralized network

management with variable thresholds assuring a

uniform load distribution amongst nodes so as to

improve the network lifetime.

Keywords: Tunable transmission range, Critical

transmission range, Inter transmission drain, Average

cluster size

I. INTRODUCTION

A Wireless Sensor Network (WSN) is a network of

number of sensor nodes called motes deployed in a

large geographical area either randomly or uniformly.

All nodes are capable of sensing some physical entity

like temperature, pressure, humidity etc. The sensed

information will be then communicated to the central

entity called the Base Station (BS) [1, 2, 3, 4].

Generally sensor nodes consist of a processing unit, a

sensor, transmitting and receiving units, position

finding system, memory units and power units. Sensors

are generally powered by batteries which cannot be

recharged often. Thus data communication in WSN has

to be done with minimum energy consumption. Unlike

other networks with fixed infrastructure, WSNs pose a

real challenge in terms of energy consumption during

data transmission. This fact has an impact on most of

the parameters like the amount of data gathered by

nodes, the volume of information that can be carried for

a larger distance etc. Thus energy efficiency is one of

the most crucial issues to be dealt while designing a

WSN [5].

Routing in WSNs differs from normal IP based routing in the following perspectives [6, 7]. Global addressing

cannot be used as most of the WSN applications are

data centric i.e the data to be transmitted is more

important than the node transmitting the data. A high

degree of correlation is expected amongst the nodes in

the close proximity which results in redundancy. Thus

data obtained from nodes within a common

geographical area have to be combined and sent to the

BS. The process is known as data aggregation. For

cluster based algorithm, Cluster Head (CH) generally

does the data aggregation. Lastly IP based routing is not energy constrained as the routing takes place over a

fixed infrastructure. But energy awareness is one of the

very crucial parameters to be considered while

designing a routing protocol for WSN [8, 9, 10, 11].

Many protocols considering energy awareness have

been proposed in the literature. In this paper an attempt

has been made in proposing a new cluster based energy

aware protocol by performing slight modifications in

the existing protocols.

Most of the algorithms consider a fixed transmission

range which sometimes consumes more energy and has

a limited scope of network connectivity. As most of the

modern sensor nodes support tunable radio ranges, we

explore the advantage of variable transmission ranges.

The simulation results have showed that the proposed

protocol performs well assuring an enhanced network

lifetime.

The rest of the paper is organized as follows: Section II

gives an insight on the previous works carried out in

this area. Section III details the proposed M-TRAC algorithm. Section IV discusses the simulation results

and analysis details. Finally Section V gives the

concluding remarks.

978-1-4673-1989-8/12/$31.00 ©2012 IEEE

II. RELATED WORK

A lot of research has been done in the area of energy

aware routing for WSNs. Many parameters were

considered to achieve better energy efficiency in the

network. We briefly describe some of these algorithms.

LEACH (Low-Energy Adaptive Clustering Hierarchy)

is one of the most popular algorithms for WSNs. It’s a

cluster based algorithm in which CHs are chosen on

rotation basis [12]. Geographic and Energy Aware

Routing (GEAR) was proposed in [13]. In GEAR every

node during transmission selects its next hop based on

the residual energy and geographical location.

Power-Efficient Gathering in Sensor Information

Systems (PEGASIS) is a cluster based routing

algorithm in which every node will use multihop communication to reach the CH. Every intermediate

node does data fusion thus improving the energy

efficiency [14].

In WSNs, most of the energy wastage is due to the

flooding process during route setup. In [15] authors

have proposed Gossip Based Routing (GBR) in which

every node will flood the packet only with a certain

probability. In Energy Efficient AODV (EEAODV)

algorithm the route is selected based on the residual

energy of the nodes in the network [16]. An additional field is introduced in the RREQ packet to record the

minimum residual energy in the path. A path with the

highest minimum energy will be opted.

Energy Efficient CH Selection Algorithm (EECSA) is

an improvement over LEACH in which the residual

energy of nodes is considered during selection process

[17]. SeNsOr netWork CLUSTERing (SNOW) is a

cluster based algorithm. CHs with higher energy are

selected as regional heads which communicate data to

the BS [18].

In [19] authors have proposed Hybrid Energy Aware

Routing Protocol (HEARP) which combines the

features of both LEACH and PEGASIS. Energy

Efficient Cluster-based Routing Algorithm (EECRA) is

a cluster based algorithm in which the CHs are selected

not only based on the residual energy but also on the

node degree [20].

An improvement over LEACH has been proposed in

[21], Cluster Based Energy Efficient Routing Protocol

(CBERP) in which BS selects the CHs initially. Multihop transmission using chain of CHs is done in

CBERP. As the BS is not energy constrained, it can be

over burdened without affecting the network

performance [22]. Also the usage of multiple thresholds

has been proposed in [22], which further assures the

uniform load balancing in the network.

III. PROPOSED M-TRAC ALGORITHM

A lot of study has been done in the area of energy efficient routing for WSNs. Cluster based algorithms

were proved to be better compared to flat routing. In

this paper we propose a modification over the existing

clustering algorithms so as to achieve an improved

network lifetime.

The following are the important assumptions have been

made for the proposed M-TRAC algorithm.

Nodes are deployed randomly and are

stationary.

All nodes are equipped with GPS. The location and energy information of all nodes are

conveyed to the BS during setup phase.

All nodes are capable of supporting tunable

transmission range.

Communication between cluster members and

the CH will always be direct and between CH

and the BS is multihop.

A Network Parameters

The following parameters have been defined for the M-TRAC algorithm.

Inter Transmission Range (Tinter): The transmission

range of a CH when communicating with other CHs.

Intra Transmission Range (Tintra): The transmission

range of a CH when communicating with its cluster

members and vice versa.

Inter Node Degree (Dinter): The number of CHs

neighboring to a given CH.

Intra Node Degree (Dintra): The number of nodes in the

intra range of a CH. In other words number of nodes in

a cluster.

Threshold (T): A variable entity which sets a stringent

constraint in the CH selection process. Nodes with

residual energy atleast equal to T are eligible candidates

to become CH.

Minimum Energy (Emin): The reserved energy at each

node so as to facilitate normal sensing and data transfer operations.

Total nodes (N): Total number of nodes in the network.

Total Clusters (Nc): Number of clusters in the network.

Average Cluster Size (Cavg): Average number of nodes

in a cluster.

Inter Transmission Drain (TDinter): Drain incurred

during transmission with Tinter..

Intra Transmission Drain (TDintra): Drain incurred

during transmission with Tintra..

NHT: Number of nodes with Higher Intra transmission

range

Critical Transmission Range (Ctr): Minimum

transmission range required to make the network

connected.

B Threshold

Generally all energy aware algorithms rely on a threshold and nodes with energy above threshold will

form clusters. But in most of the algorithms a static

threshold is used. Network performance can be still

improved by incorporating multiple thresholds.

Multiple thresholds guarantee uniform load distribution

amongst nodes in the network. However it cannot be

too large which will unnecessarily leads to frequent

network setups. Thus a tradeoff has to be made in the

uniform load balancing and frequent network re-setup.

When the network is young it can take a risk of forwarding more packets and as the network becomes

old a stringent constraint has to be imposed on the

nodes so as to assure a better lifetime. The proposed

algorithm exploits this fact and the expression for the

threshold for the current iteration or round is given as

follows.

CT=Emin + OT [1 - ] (1)

Where

CT= Current Threshold OT= Old Threshold

Ravg = Average Residual Energy

A new set of CHs has to be formed by the BS when any

of the residual energy goes below CT.

When Ravg reaches Emin,

CT= Emin

(2)

Thereby assuring a minimum energy of Emin amongst

the selected CHs.

C Objective Function

An appropriate objective function has to be formulated

so as to guarantee a required degree of energy

efficiency. Various parameters like number of cluster

heads, intra node degree, intra and inter transmission ranges affect the network lifetime. Thus the effects of

all these parameters have to be taken into account

during the network setup phase [23]. In this paper we

have identified four such objective functions detailed as

follows:

Selecting appropriate transmission ranges for intra and

inter cluster communication also plays an important

role in the performance of WSN. Transmission ranges

not only influence the number of clusters and cluster

size but also the amount of data transmitted. During

network set up time some nodes may remain unconnected with any clusters in which case the Tintra of

such nodes has to be increased to Tinter so as to facilitate

such remote nodes to find a nearby CH to join.

However such connections will definitely add to the

energy drain and thus has to be minimized. Therefore

the objective function also has to consider the

minimizing of such high intra transmission range nodes.

Minimize NHT (3)

One of the major requirements for enhancing network lifetime is to minimize the total energy drain in the

network for data transmission.

Minimize [{([Nc * Cavg]+ NHT)*TDinter}+{(N-Nc-NHT) * TDintra}(4)

The selected transmission ranges have to satisfy the

following conditions

CTr <= Tinter < Max available transmission range (5)

Min available transmission range <= Tintra < CTr.

(6) The process of selecting CHs should ensure the

selection of nodes with higher residual energies. In

other words total available energies in the selected CHs

has to be maximized

Maximize { } (7)

Tinter and Tintra will be selected amongst the available

ranges so as to fulfill the above objective functions.

D Network Setup

The minimum required energy to become CH of the

current round is calculated by

T= average energy amongst the CHs of the previous

round.

However it can be scaled down depending upon the

availability of the number of eligible nodes.

Appropriate selection of T is desired to assure a minimum number of packet transmissions from the

selected CHs of the current round. In our simulations

Emin is assumed to be 0.1 which leads to a minimum

value for T as 0.25.

Amongst nodes with energy higher than T, the BS

selects all CHs based on its intra and inter node

degrees. Appropriate weighting factors have to be given

as follows so as to assure a better connectivity.

Maximize K1 Dintra+ K2 Dinter (8)

For our simulations, we have used K1= 0.1 and K2= 0.9.

The nodes selected as CHs will be intimated by the BS.

The selected CH will then broadcast a HELLO packet

with a range Tintra. All nodes receiving the HELLO

packet for the first time will accept the invitation and

become the members of the respective cluster. If a node

receives the invitation from more than one CH, it

accepts only the first invitation and discards all others.

During network setup some remote nodes may remain

unassociated with any of the clusters. The Tintra of such nodes will be increased to Tinter so that it can reach a

nearby CH. The process continues till all clusters are

formed in the network.

E Data Transmission

The data transmission takes place whenever there is a

significant change in the sensed entity. In other words

the communication is event driven. The sensed data

will be conveyed to the CH by direct transmission

incurring a drain of TDintra. Whereas mutihop

transmission is used between CHs and the BS. a minimum energy spanning tree is formulated by the BS

spanning all CHs with minimum energy drain path and

the same will be intimated to all CHs. Each node

maintains a cache to store the address of the next CH to

reach the BS.

F Network Re-setup

The process of data communication over the discovered

paths continues till any of the node’s residual energy

goes below CT. The complete process of network setup is then reinitiated by the BS. CT will become OT for

the next round.

IV. SIMULATION RESULTS & ANALYSIS

We consider the following parameters for the

simulation:

A discrete event network simulator CASTALIA is

used for the analysis [24].

The simulation is carried out for the networks with

25, 50 and 75 nodes.

A network size of 100 X 100 is used for the analysis

Simulation lasts till the network partitioning in all cases.

Initial energy at each node is considered to be 1 J

Analysis is restricted to the communication drain in

the network.

Radio model used is CC2420 compatible to IEEE

802.15.

Size of the data packet is 1024 bytes.

A round or iteration is defined as a span of time for a

given CT. More the number of nodes, more will be

the rounds.

Figure 1 gives a plot of total number of packets

transmitted as a function of network size. It is clear

from the plot that more the number of nodes, better will

be the performance as more alternative paths to reach

the base station. Average residual energy of the

network is the mean energy of nodes after each round.

Threshold for every round is calculated using the

equation 1. It is a decreasing function of residual energy

which in turn dependent on time.

A plot of average residual energy and the threshold as a function of simulation time for a network size of 25, 50

and 75 are as shown in Figures 2, 3 and 4 respectively.

Average residual energy decreases with time but more

the number of nodes less will be the energy available in

the network during partitioning ensuring better energy

utilization.

The nodes with energy less than minimum threshold T

(0.25 in our simulations) will be no more selected as

CHs. Such nodes are said to be non eligible nodes in the

network. Graphs of number of non eligible nodes w r t simulation time for networks of size 25, 50 and 75 are

shown in figures 5, 6 and 7 respectively.

When no more paths to the BS exist in any of the

transmission ranges, network is said to be partitioned. Nodes next to the BS are prone to severe energy loss

due to data forwarding and are likely to become non

eligible early. In most of our scenarios nodes nearby to

the BS form a bottleneck and lead to network

partitioning.

Fig. 1. Number of Data Packets Transmitted

Fig. 2. Residual Energy and Threshold for N= 25

Fig. 3. Residual Energy and Threshold for N= 50

Fig. 4. Residual Energy and Threshold for N=75

Fig. 5. Number of Non Eligible Nodes as a Function of Time for N=

25

Fig. 6. Number of Non Eligible Nodes as a Function of Time for N=

50

Fig. 7. Number of Non Eligible Nodes as a Function of Time for N=

75

Fig. 8. Number of CHs in % as a Function of Network Size

The number of CHs in the network also plays a vital

role in its performance. The selection also has to ensure

a uniform load balancing amongst nodes. Figure 8

shows a plot of percentage number of nodes in the

network becoming CHs for various network sizes. It

can be seen from the graph that N= 50 and 75 performs better than N= 25 as around 70% of the nodes have

served as CH giving a better load distribution.

V. CONCLUSIONS

Wireless computing being a ubiquitous part of the

modern communication era poses many challenges in

the design issues. Energy efficiency is one such

challenge. In this paper, an attempt has been made in

introducing a new protocol using the M-TRAC

algorithm which assures an optimal load balancing so as to enhance the network lifetime. Centralized

approach reduces the energy drain for the process of

network setup. Tunable transmission range facilitates

the BS to have more options in the CH selection.

Selection of appropriate transmission range also

reduces the energy drain and thus improves the network

connectivity. An objective function is formulated and

the network setup is done using the same. Simulations

were carried out using CASTALIA network simulator.

Simulation results show that the proposed M-TRAC

algorithm offers a good network lifetime. As a part of our future work, the algorithm can be still improved by

considering the MAC layer issues. Stringent scheduling

policies can still improve the performance and assures

more critical load balancing.

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