ENERGY EFFICIENCY IN AD HOC NETWORKS

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.2, No.1, March 2011
DOI : 10.5121/ijasuc.2011.2112 139
ENERGY EFFICIENCY IN AD HOC NETWORKS
Subhankar Mishra1
, Sudhansu Mohan Satpathy1
and Abhipsa Mishra1
1
Department of Computer Science and Engineering, National Institute of Technology,
Rourkela, Odisha, India
cse.vicky@gmail.com
ABSTRACT
Wireless Ad Hoc Networks comprise a fast developing research area with a vast spectrum of
applications. Wireless sensor network systems enable the reliable monitoring of a variety of environments
for both civil and military applications. The Energy efficiency continues to be a key factor in limiting the
deployability of ad-hoc networks. Deploying an energy efficient system exploiting the maximum lifetime
of the network has remained a great challenge since years. The time period from the instant at which the
network starts functioning to the time instant at which the first network node runs out of energy, i.e. the
network lifetime is largely dependent on the system energy efficiency. In this paper, we look at energy
efficient protocols, which can have significant impact on the lifetime of these networks. The cluster heads
get drain out maximum energy in the wireless ad hoc networks. We propose an algorithm that deals with
minimizing the rate of dissipation of energy of cluster heads. The algorithm LEAD deals with energy
efficient round scheduling of cluster head allocation of nodes and then followed by allocation of nodes to
the cluster heads maximizing network lifetime using ANDA [1, 2]. We compare our results with the
previous works.
KEYWORDS
Clustering, ANDA, energy efficiency, LID, energy factor, network lifetime, LEACH, LEAD, network,
energy, efficiency, dissipation, Ad hoc
1. INTRODUCTION
The limited availability of the energy resources poses as quite a challenge in designing wireless
adhoc networks. and quite significantly these are limited in wireless networks than in wired
networks. The network lifetime is defined as the time instant at which the network starts
functioning to the time instant at which the first network node runs out of energy. In this paper
we deal with the design of techniques to maximize the network lifetime in case of cluster-based
systems.
We have worked and researched on mainly studying and implementing the ANDA, integrating
LID with ANDA, and finally we have proposed our own original algorithm for improving the
ANDA and bringing a maximum network lifetime. We have also used the traditional LEACH
Algorithm concept in designing our own original algorithm. We tend to show in our paper that
the proposed Algorithm quite outperforms the traditional Algorithms proposed related to the
network lifetime field till date.
CLUSTERING is defined as the grouping of similar objects or the process of finding a natural
association among some specific objects or data. Sensor Networks uses a cluster based system
to transmit processed data to base stations. In these systems the network nodes are partitioned
into several groups where each group has one node as the primary cluster head and the rest of
the nodes are the ordinary nodes.

140
Cluster-formation is a two-phase process consisting of cluster-head election and assignment of
nodes to cluster-heads[4, 5]. The cluster-head is the co-ordinator of all transmissions within the
cluster, so also it handles the inter-cluster traffic and also delivers the packets destined for the
cluster etc. Obviously these cluster-heads would experience a very high-energy consumption
thereby leading to exhausting their energy resources more quickly than the ordinary nodes. It is
therefore required that the cluster-heads' energy consumption be minimized (optimal) thus
maximizing the network lifetime [1].
We first discuss the related work done in this field like the ANDA, LID and LEACH and then
move on to our algorithm LEAD, its detailed working and explanation and our simulation
environment that we have deployed the algorithm in.
2. RELATED WORKS
2.1. ANDA
ANDA [1], Ad hoc network design algorithm, assigns the ordinary nodes to the cluster heads
such that energy is not drained out from them easily and the lifetime of the whole system
increases drastically. A matrix is computed which lists, the probable lifetime of the cluster head
if a particular node is assigned to it, for all the cluster heads. ANDA algorithm basically
comprises two algorithms. One, the covering algorithm [1] which is applied to the static and
dynamic case and second, the reconfigure algorithm [1] which applies only to the dynamic
scenario. Dynamic means the nodes change their position after every [delta] time. Covering
performs the optimal assignment of nodes to cluster-heads that presents the longest functioning
time computed from the matrix. Reconfigure algorithm makes use of [delta] to obtain new
nodes assignment every time the network configuration changes. But this algorithm takes into
account a fixed set of cluster-heads which continuously dissipate energy throughout the network
functioning time. We came up with the idea of having dynamic set of cluster-heads, thereby
distributing the energy dissipation among the set of nodes for a better lifetime.
2.2. LID
The LID algorithm is the Lowest Id Algorithm [2, 3] which is clustering algorithm. It defines
which nodes will behave as clusterheads and determines the nodes that constitute the cluster.
LID defines the nodes which will behave as cluster heads. ANDA is then implemented to cover
the nodes. We assign a unique ID to each node in the network. The LID algorithm chooses
arbitrarily the node with the lowest ID as the cluster-head and declares all the nodes within the
range of this cluster-head as its members. This process continues until all nodes in the network
have been grouped into cluster-heads. We calculated the network lifetime using our simulator
and then compared with the ANDA. This whole thing is basically implemented in a dynamic
scenario. The above scenario was implemented on a simulation platform. We took a dynamic
scenario whereby nodes were also changing their positions at regular intervals, thereby
considering speed of nodes. Basing on these factors the LID and ANDA algorithms were
applied, to calculate lifetime.

141
3. LEAD
LEAD deals with dynamic selection of cluster heads among the set of nodes in the network and
then allocation of the rest ordinary nodes to the clusterheads dynamically. It adapts to the
network and selection and allocation is done according to the current status of the network.
LEAD achieves three goals. First, we select a set of cluster heads among the nodes randomly
which is very practical in case of wireless ad hoc networks instead of having a fixed set of
cluster heads. Second, set of cluster heads are selected dynamically after a time ∆ in a round
schedule balancing the load (energy dissipation) throughout the nodes of the network thus
increasing the lifetime. Third, dynamically allocates the nodes to the cluster heads using the
enhanced feature of ANDA thereby reducing the load on each cluster head to sustain them for
other rounds.
LEAD is proactive: each node periodically broadcasts HELLO messages that contain the nodes
status (i.e., whether or not the node is currently a cluster head), its current energy, no of nodes
under it, its range. From these HELLO messages the nodes calculate the lifetime if it is pseudo
assigned to a particular cluster head and thus calculating final allocation of the node. The nodes
together form the matrix of the ANDA using these messages. The state of the nodes change
every ∆ time so they periodically send the HELLO messages after every ∆ time.
3.1. Cluster Head Selection
Let SC = f1...Cg be the set of cluster-heads, SN= f1...Ng be the set of ordinary nodes to be
assigned to the clusters. Initially, when clusters are being created, each node decides whether or
not to become a cluster-head for the current round according to the table created periodically
updated every N/C rounds. This decision is based on the suggested percentage of cluster heads
for the network (determined a priori) and the number of times the node has been a cluster-head
so far. After a broadcast of HELLO messages the total network data is clubbed together and the
nodes are sorted according to their current residual energy.
Selectcluster
If (N/C divides ∆)
Begin nodeSort
for (every i2SC)
for (every j2SN)
if (energy[i] > energy[j])
swap (i, j)
end for
end for
end nodeSort
end if
3.2. Node Assignment
Let SC = f1...Cg be the set of cluster-heads, SN= f1...Ng be the set of ordinary nodes to be
assigned to the clusters. We choose the set of cluster-heads SC Major Contributions to power
consumption in nodes are: power consumed by the digital part of the circuitry, Power
consumption of the transceiver in transmitting and receiving mode and output transmission
power. The lifetime is calculated according to the following equation:-

142
where Ei is the initial amount of energy available at cluster-head i, ri is the coverage radius of
cluster-head i, ni is the number of nodes the control of cluster-head i, and and are constants.
Considering that the limiting factor to the network lifetime is represented by the cluster-heads
functioning time, the lifetime is defined by
We need to devise techniques to maximize LS. The Algorithm for assignment of the nodes is as
follows:
Begin Assignnodes
for (every i SC)
set Ei=initial energy of cluster-head i
for (every j SN)
Compute dij,|nij|,lij
end for
end for
LS (new) = LS (old)= LS
∆=0
while(LS(new)LS(old)-∆)
∆=∆+1
for (every i SC)
for (every j SN)
Recompute Ei=Ei-∆( αri
2
+β|nij|)
Update lij 8 i SC, j SN
end for
end for
Call Selectcluster and update LS
LS(new)=LS
end while
endAssignnodes
4. NUMERICAL RESULTS
The performance of LEAD is derived in terms of the network life time measured at the time
instant at which the first cluster head runs out of energy. We derived results by using a software
tool designed by us using Java. We simulated an ad hoc network composed of slowly changing
network topology. The network nodes are randomly distributed. The simulated area is a 1000 x
1000 matrix. The nodes can attain a maximum speed of 5m/sec and the mobility of nodes is
random. We assumed that the initial energy of all the nodes in the network is 5000J and it can
support up to 1000 nodes and even if a node is not a cluster head it loses some amount of energy
due to radio transceiver and transmission amplifier. Figure 1 shows the variation between the
percentage of nodes becoming cluster heads and lifetime of the network in LEAD.

143
Figure 1. Lifetime as a function of the number of nodes becoming cluster heads. (LEAD)
First, we compare the performance of LEAD with the results obtained by using ANDA (Ad hoc
network design algorithm) in which cluster heads are known apriori. Figure 2 shows the
network lifetime as a function of the number of nodes, for a percentage of cluster heads P=0.05.
The life-time decreases as the number of nodes grow; however for a number of nodes greater
than 100, the life-time remains almost constant as the number of nodes increases. Lifetime
decreases because Clusterheads have to cover more nodes as the number of nodes in the
network size increases. But LEAD shows significant improvement over ANDA, these is
because in ANDA the cluster-heads are determined apriori but in case of LEAD cluster-heads
are chosen in a random basis and each node becomes a cluster head in 1/P rounds i.e. the nodes
that are cluster-heads in round 0 cannot be cluster-heads for the next 1/P rounds. From the
comparison with the performance of ANDA, we observe that the improvement achieved
through LEAD is equal to 35 %. Energy is uniformly drained from all the nodes and hence the
network life-time is significantly increased.
Figure 2. Lifetime as a function of the number of nodes, for a percentage of nodes becoming
cluster heads equal to 0.05. Results obtained through ANDA and LEAD scheme are compared.

144
Figure 3: shows the network life-time as the number of cluster-heads, C. Curves are obtained for
N=1000 and nodes distributed randomly over the network area. In ANDA we observe that as
the number of cluster heads increases for a given number of nodes, the life-time is increased,
this is due to the fact that increasing C, cluster heads now have to cover less number of nodes
and energy of each node is drained at a slower rate. But, in case of LEAD we observe that
network life-time decreases with the increase in percentage of nodes becoming cluster-heads
(P). For less percentage of nodes becoming cluster-heads the time interval between successive
elections of a node as cluster-head is large but if percentage of nodes becoming cluster-heads is
high, the time interval is small i.e. a node is again elected as a cluster-head in less number of
rounds. So, energy is drained early resulting in a decreased network life-time.
Figure 3. Lifetime as a function of the percentage of nodes becoming cluster heads.
Results obtained through ANDA and LEAD scheme are compared.
5. CONCLUSION
This paper mainly deals with the problem of maximizing the life-time of a wireless ad hoc
network, i.e. the time period during which the network is fully working. We presented an
original solution called LEAD which is basically an improvement on ANDA. After making a
brief comparative study of our work, we see that as we move on from ANDA to our proposed
Algorithm, we gradually get an increased network lifetime. From the various graphs and tables,
we can successfully prove that our Algorithm quite outperforms the traditional energy efficient
algorithms in an obvious way. The LEAD algorithm outperforms the original ANDA algorithm
by 35 percent.
ACKNOWLEDGEMENTS
We would like to thank Mrs Suchismita Chinnara for guiding us throughout the working period
and advising us till now. And we are happy to have our parents as our parents as they are best
we could have ever had.

145
REFERENCES.
[1] Chiasserini, C.F.,Chlamtac, I.,Monti,P.,Nucci,A.: Energy Efficient Design of wireless ad-hoc
network.LNCS 2006, vol. 2345, pp. 376-386.
[2] Ephremides, A., Wieselthier, J.E., Baker, D.J.: Proc.IEEE, VOL.75, NO.1, January(1987).
[3] Baker, D.J., Ephremides, A.: The architectural organization of a mobile radio network via a
distributed algorithm.In: IEEE Transactions on Communications, pp.
1694-1701, November (1981)
[4] Kwon, T., Gerla, M.: Clustering with Power Control, Proc.MILCOM’99,November (1999).
[5] Heinzelman, W.B., Chandrakasan, A., Balakrishnan, H.: Energy-Efficient Communication
Protocols for Wireless Microsensor Networks.In Proceedings of Hawaiian
InternationalConference on Systems Science, January (2000).
[6] Heinzelman, W.B.: Application-Specific Protocol Architectures for Wireless Networks, PhD
thesis, Massachusetts Institute of Technology, June (2000).
[7] Akyildiz, I.F. et al.: Wireless sensor networks: a survey, Computer Networks, Vol.38, pp. 393-
422, March (2002).
[8] Hill, J.: System Architecture for Wireless Sensor Networks, PhD Thesis, Spring 2003.
[9] Manjeshwar, A., Agrawal, D.P.: TEEN : A Protocol for Enhanced Efficiency in Wireless Sensor
Networks,1st International Workshop on Parallel and Distributed Computing Issues in Wireless
Networks and Mobile Computing, San Francisco, CA, April (2001).
[10] Jiang, M., Li, J., Tay, Y.C.: Cluster Based Routing Protocol,Internet Draft, 1999.
[11] Royer, E.M., Toh, C.K.: A Review of Current Routing Protocols for Ad-Hoc MobileWireless
Networks.In:IEEE Personal Communications Magazine, pages 46-55, April(1999).
[12] Intanagonwiwat, C., Govindan, R.,Estrin, D.: Directed Di_usion: A Scalable and Robust
Communication Paradigm for Sensor Networks,In Proceedings of the 6th
Annual ACM/IEEE
International Conference on Mobile Computing and Networking(MOBICOM), pages 56-67,
August (2000).
Authors
Subhankar Mishra, Computer Science and Engineering NIT Rourkela.

ENERGY EFFICIENCY IN AD HOC NETWORKS

More Related Content

What's hot (16)

Similar to ENERGY EFFICIENCY IN AD HOC NETWORKS (20)

Recently uploaded (20)

ENERGY EFFICIENCY IN AD HOC NETWORKS