Wireless Sensor Network, 2011, 3, 378-383
doi:10.4236/wsn.2011.312044 Published Online December 2011 (http://www.SciRP.org/journal/wsn)
Copyright © 2011 SciRes. WSN
Energy Consumption Patterns for Different Mobility
Conditions in WSN
Manjusha Pandey, Shekhar Verma
Indian Institute of Information Technology, Allahabad, India
E-mail: {rs58, sverma}@iiita.ac.in
Received September 25, 2011; revised October 15, 2011; accepted November 18, 2011
Wireless sensor networks are challenging networks regarding communication because of its resource con-
strained nature and dynamic network topology. Plenty of research has being going on throughout the world
to optimize communication cost and overhead due to it in the ad hoc networks, thus efforts are being made to
make the communications more energy efficient. The application spectrum and use cases of wireless sensor
networks includes many critical applications as environmental monitoring, to resource monitoring, to Indus-
trial measurements, to public safety applications and last but not the least to sensitive applications as military
sector applications .The erratic size of such networks and along with its exotic topology pose a magnificent
set of challenges to the routing algorithms designed and implemented within such networks. The present
work concentrates on the comparative analysis of some of reactive and proactive protocols used in the wire-
less sensor networks. The parameter for comparative analysis is the energy consumption for different simu-
lation time and for different mobility conditions based scenario.
Keywords: Sensor Networks, Battle Field Monitoring System, AODV, DYMO, LANMAR, OLSR
1. Introduction
A wireless sensor network [1] can be described as a
wireless network comprising of a group of randomly de-
ployed mobile communication nodes that interact among
themselves without the need of use of any centralized
authority or any fixed infrastructure. In these types of
networks each node functions as a host (sender/receiver)
as well as a router itself. Such networks have been con-
sidered with prime importance over the last decade be-
cause of the ever increasing demand for ubiquitous con-
nectivity and emergence of new communication scenar-
ios and applications. Some critical areas of applications
of such networks being in the fields of military and ci-
vilian application as communication in the battle field,
disaster management, vehicular movement or communi-
cation in traffic management and scientific exploration
etc. In all these applications, group communication is
more important. Multicasting provides necessary services
for group communication in such applications. The most
appreciated features of sensor networks include 1) Dy-
namic topology, 2) Bandwidth constrained links, 3) En-
ergy constrained operation, and 4) Limited physical se-
curity. To enable the communication between two nodes
in sensor networks it requires establishing a wireless-
channel (route) between them.
Generally, multi-hop routing is used as the nodes may
or may not be within the wireless transmission range of
one-another and thus highly depend on each other for
forwarding of the data packets to the desired destination.
There can be varied route selection criteria such as en-
ergy efficiency, bandwidth efficiency, low traffic control
overheads etc. Since the topology of a sensor network is
dynamic in nature and thus changes frequently, a routing
protocol must be a distributed algorithm that computes
multiple; cycle free routes while keeping the communi-
cation overhead to minimum and also to suit th e specific
needs, numerous routing protocols have been investi-
gated proposed and implemented [1]. Routing in sensor
network faces extreme challenges because of node mo-
bility/dynamics, extremely large number of nodes and
very limited communication resources (e.g. Bandwidth
and energy).The other technological challenges sensor
network includes communication stability, security, en-
ergy consumption and most importantly quality of ser-
vices. For the experimental setup first the scenarios were
created then the desired statistics were applied followed
by the simulation and results were viewed and analyzed.
Copyright © 2011 SciRes. WSN
We have considered the sensor network models in
context of network simulations and to the best of our
knowledge this is the only work that compares the en-
ergy consumption patterns of routing mechanisms for the
detailed models on performance of sensor networks [2].
Figure 1 presents the flowchart of the implemented ex-
perimental setup.
The four different mobility co nditions for the scenario
discussed as follows illustrate the comparative analysis
by using accurate and representative sensor network mo-
del. Though the present work considers the second sce-
nario for the comparative analysis, the same could be ex-
tended for all the four scenarios implemented.
2. State of Art
There are many network optimization related issues of
concern to be solved in WSNs as rate control, flow con-
trol, medium access control, congestion control, queue
management, topology control and power control, etc.
[3]. It is not easy to provide a complete overview with
respect to all issues relating to network optimization in
WSNs. But one of the most important issues is Energy-
Efficient Routing Design because communication domi-
nates the critical portion of energy consumption, routing
design is considered to be core of sensor network design.
Many routing algorithms have been proposed in prior re-
search. The shortest path is fundamental consideration
for network flow routing. A simple implementation of
this consideration in sensor network is the minimum hop
(MH) routing. Many researchers have proposed and im-
plemented shortest path algo rithms so as to minimize the
utilization of energy.
Figure 1. Flowchart for the simulation and implementation
of the experimental setup.
Battery power is a very critical resource in sensor
networks. This is particularly true for sensor networks
which have been deployed in areas of low or poor acces-
sibility. These sensor networks are expected to operate
for longer periods without human interventio n, ruling out
the possibilities of replacing exhausted batteries. Such
sensors need to be highly energy efficient to conserve
battery power. Among other schemes, like choosing en-
ergy efficient routes, scheduling, data aggregation etc.,
sensors endeavors for the conservation of energy by pe-
riodically switching to a low energy consuming sleep
state .When a node is not in sleep state, it is in another
state termed as active state. Many routing techniques
have been proposed but very few comparisons for dif-
ferent mobility conditions for different protocols have
been done and analyzed. WSNs can be classified ac-
cording to several aspects with impact on the routing
protocol design. On e such aspect is the mobility of nodes
of the network and the base station. The nodes can be
static or mobile. Considering the work done for per-
formance comparisons of routing protocols the major
focus have been on the evaluation based on quantitative
and qualitative metrics. But the comparison of energy
consumption of protocols for different exhaustive mobil-
ity conditions and different simulation intervals of the
same scenario have not been proposed performed and
analyzed yet. Various routing protocols used for the
study are; AODV, DYMO, OLSR, and LANMAR.
In the proactive protocols OLSR [4], LANMAR, the
nodes continuously searches for routing information in
the network so that when a route is required, the route is
known already. The routing information (distance vector
or link state) of all the nodes is stored and updated in
tabular forms at each node. Distance vector (DV) or link-
state (LS) route algorithms used in this routing protocol
find shortest path to the destination.
OLSR [4] is a variation of traditional link state rou ting,
modified for improved operation in ad hoc networks. The
key feature of OLSR protocol is that it uses multipoint
relays (MPRs) to reduce the overhead of network floods
and size of link state updates. Each node maintains a
route to every other node in the network. This technique
significantly reduces the number of retransmissions in a
flooding or br o a dcast procedure.
LANMAR combines the features of Fisheye State
Routing (FSR) and Landmark routing. The key novelty is
the use of landmarks for each set of nodes which move
as a group (e.g., a team of co-workers at a convention or
a tank battalion in the battlefield) in order to reduce
routing update overhead. Like in FSR, nodes exchange
link state only with their neighbors. Routes within Fish-
eye scope are accurate. Simulation experiments show
that LANMAR provides efficient and scalable routing in
Copyright © 2011 SciRes. WSN
large, mobile, ad hoc environments in which group mo-
bility applies.
On the other hand, On-demand routing protocols like
AODV [5], DYMO etc. are more dynamic. Instead of
periodically updating the routing information, these pro -
tocols update routing information whenever a routing is
required. This type of routing creates routes only when
desired by the source node and therefore, in general, the
signaling overhead is reduced compared to proactive
approaches of routing.
DYMO is intended for use by mobile nodes in wireless,
multi hop networks. DYMO determines unicast between
DYMO routers within the network in an on-demand
fashion, offering improved convergence in dynamic to-
pologies. The basic operations of the DYMO protocol
are route discovery (by route request and route reply) and
route maintenance.
3. Art WSN for Battle Field Monitoring
WSN for battle field monitoring System scenario demon-
strates data collection from unattended ground sensors
using mobile nodes. Sensors have been randomly de-
ployed within the observation region. The sensors are
constantly monitoring any phenomena which may be of
interest in the area. The sensed information observed by
any sensor is stored lo cally at the sensor itself. While the
mobile vehicles move inside the area where sensors have
been deployed. The vehicles are having short range of
communication to the sensors and long distance commu-
nication to one remote site that is called fusion centre in
the scenario. The sensors transmit their locally stored
data to the vehicles that at any time are within their radio
The vehicles afterwards relay the sensed data packets
to fusion centre with the help o f long distance communi-
cation to that centre. Nod e types used in this scenario are:
a) Ground Sensors (GS) which refers to ground sensors.
b) Unmanned Vehicles (UV) which refers to mobile ve-
hicles c) Fusion centre refers to a remote site. GS and
UV are both battery-powered devices. Short rage com-
munication between GSs and UVs has been configured
using ZigBee. PHY and MAC protocol used in the sce-
nario is 802.15.4 and the four protocols mentioned as the
paper follo ws are used for the long distance communica-
tion between UVs and fusion centre. Fusion centre is
configured as WiFi (802.11a) also different protocols
have been used for this communication that have been
defined by two communicating interfaces for the UVs
and all the four routing protocols have been used in both
the interfaces.
The scenario consists of: 100 GS nodes (nodes from 1
through 100) with linear battery model and micaZ radio
energy model.5 UV (nodes from 100 through 105) with
random way point mobility initially within the area
where sensors are deployed (velocity range 0.1 - 0.4
damp). Linear Battery model and micaZ radio energy
model have been configured for GSs and UVs. Fusion
center is the no de 121.When th e scenario is run, it shows
that GVs moving within the area they are deployed.The
UVs communicate with the other UVs that are within
their ZigBee communication range. The sensors are hav-
ing CBR flows to fusion centre an d are able to send their
sensed data to the fusion centre.
Figure 2 presents the framework for the battle field
monitoring scenario implemented. The four different
mobility conditions for the scenario considered may be
described as follows:
1) All nodes within the network are static. The GS
(ground sensors) and the UV(Unmanned station) are
static in the first mobility conditio n considered while the
fusion centre remains static in each and every mobility
condition in which the scenario has been implemented.
2) In the second mobility condition for the scenario
implemented is that the GS (ground stations) are static
while the UV (unmanned Vehicle) are mobile.
3) The third mo bility condition con sidered for the sce-
nario says that the implementation of the GS (ground
sensors) is mobile while the UV (unmanned Vehicles)
are static.
4) The fourth mobility condition for which the sce-
nario is implemented refers to the situation when the GS
(ground sensors) are mobile as well as the UV (un-
manned Vehicles) are also mobile.
The present work concentrates on the second senario
descibed and the scenario has been tested for different
simulation intervals in order to present a comparative
Figure 2. Framework of WSN for Battle field monitoring
Copyright © 2011 SciRes. WSN
analysis of various routing protocol energy consumption
4. Simulation Setup of WSN for Battle Field
Monitoring System
The overall objective of this simulation study is to ana-
lyze energy consumption and the performance of differ-
ent existing wireless routing protocols designed and imp-
lemented in ad hoc wireless network environment. The
simulations have been done using QualNet version 5 [6],
a software that provides with the scalable simulations of
Wireless Networks. In our simulation, we have a net-
work of 100 nodes, having 5 tanks and 1 ground station
placed randomly within a 500 m × 500 m area and oper-
ating for over 30 seconds of the simulation time. Multi-
ple executions with different seed numbers have been
conducted for each scenario and collected data has been
averaged over those runs. The two-ray propagation path
loss model has been used in our experiments along with
lognormal shadowing model. The parameters used for
configuration of PHY802.15 of Ground Sensors (GS)
and Unmanned Vehicles (UV).
The access scheme followed is CSMA/CA with ac-
knowledgements. MAC layer parameters used are IEEE
802.15.4 for the ground sensors and IEEE802.1 for the
unmanned vehicles and ground station or fusion center.
The network layer affects the QoS if it has fewer queues,
as it will queue packets of many a different service types
into one queue that is prior i queue. The node movements
(except the ground sensor) in the experiments use the
random waypoint mobility model with mobility speed
ranging from 0m/s to 10m/s. We choose this range be-
cause ad hoc wireless network support this medium mo-
bility unlike static network [7].
5. Simulation Parameters for Experimental
Experimental setup for the present comparative study
was implemented for the following parameters:
Number of Nodes:
UGS 100
UGV 05
Ground station 01
Mac Parameters:
UGS IEEE 802.15.4
UGV IEEE 802.11
Ground station IEEE 802.11
Traffic Parameters:
Data Payload 1024 bytes/packet
Path Loss Model Two Ray Model
Mobility Model Random Waypoint
Interface queue type Priority queue/drop tail
To evaluate the performance of routing protocols [8],
both qualitative and qu antitative metrics must be consid-
ered. Most of the routing protocols ensure the quantita-
tive and qualitative metrics as th e portion of packets sent
by the application that are received by the receiver. The
paper presents a exhaustive study of the energy con-
sumed in sending and receiving modes by the nodes in
the different mobility conditions. The study was further
incorporated for different simulation duration for second
scenario where the Ground Sensors (GS) are static Un-
manned Vehicles (UV) are mobile.
Energy Consumption (mJoule): The MICAZ Mote
devices are in the following four states: transmitting,
receiving, idle and sleep. Energy consumption is the
quantity of energy consumed by mote during the above
mentioned states of the device. The unit of energy con-
sumption used in the simulations is milliJoule [9].
Other Network Parameters:
Antenna Omni direction al
Simulation time 30 sec
Transmission range 35 meter
Transmission Power(dbm) 3.0 dbm
Temperature 290.0
Node speed (mobilit y) Min: 0 m/sec; Max: 10 m/sec
Area 500 × 500 meters
Energy Model MICAZ
Battery Model Simple Linear, 1200 mAhr
6. Experimental Outcomes and Analysis
The scenario under consideration for the consumption of
energy in transmit mode fo r different simulation times is
the second scenario where the ground sensors are static
while the unmanned vehicles are mobile the following
results were viewed for simulation times under conside-
ration being 30 seconds, 300 seconds and 3000 seconds
respectively for the reactive and proactive protocols un-
der consideration.
Following figures presents the simulation results ana-
lyzed and concluded in detail. The first protocol imple-
mented and analyzed is AODV.
1) AODV (Adhoc on Demand Dist ance Vector Routing)
AODV [5] routing uses the number of link hops as its
routing metric. However since the limitation of battery
power is one of the most essential concerns of sensor
networks, routin g algorith ms for sensor networks attempt
to optimize the consumption of this resource. Many re-
searchers have proposed and implemented shortest path
algorithms so as to minimize the utilization of energy.
The results shown in the Figure 3 presents a linear in-
crease that is directly proportional to the increase in the
simulation interval from 30 seconds to 3000 seconds in
the case of AODV routing protocol.
Copyright © 2011 SciRes. WSN
Figure 3. Comparative energy consumption by AODV
routing protocol for different simulation time interval of
the second scenario.
2) DYMO (Dynamic MANET On Demand Routing
On-demand routing protocols like AODV [10],
DYMO [8] etc. are more dynamic. Rather than perio-
dically up- dating the routing information, these prot-
ocols update routing information whenever a routing is
required. DYMO is generally intended for the use by
mobile nodes in wireless, and multi hop networks.
DYMO determines unicast between DYMO routers
within the network itself in on-demand fashion, which
offers improved conve- rgence of the dynamic topologies.
The basic operations defined in the DYMO protocol
include route discovery (by route request and route reply)
and route main- tenance .Though the results depicted in
Figure 4 do not present a steep variation with increase in
simulation time interval.
3) OLSR (Optimized Link State Routing Protocol)
OLSRa variation of traditional link state routing, has
been modified for improved operation in ad hoc net-
works. The key characteristic of OLSR is that it makes
use of multipoint relays (MPRs) that reduce the network
floods overhead and thus the size of link state updates.
The results present an immediate increase in the energy
consumption with increasing simulation time interval
from 300 seconds to 3000 seconds as shown in Figure 5.
4) LANMAR (Land Mark Routing Protoc ol)
LANMAR [11] adds up the features of Fisheye State
Routing (FSR) and Landmark routing. The novelty of the
protocol is the use of landmarks for every set of nodes
which move as a group (e.g., a team of co-workers or a
tank battalion of the b attlefield) to reduce routing update
overhead. Like the AODV routing protocol energy con-
sumptions presents a steep increase with the increase in
simulation time interval. Figure 6 gives a comparative
Figure 4. Comparative energy consumed by OLSR routing
protocol for different simulation time for the second
Figure 5. Comparative energy consumed by OLSR routing
protocol for different simulation time for the second
Figure 6. Comparative energy consumed by OLSR routing
protocol for different simulation time for the second
Simulation can be divided between less flexible but ac-
curate simulation based approach and more generic but
Copyright © 2011 SciRes. WSN
less detailed network simulator models. Simulator which
provides a rich suite of following models: sensing stack
to model wave and diffusion based sensor channels, an
accurate battery model, processor power consumption
model, energy consumption model and sensor network
based traffic model [9].
The study could be used to present the effects of de-
tailed modeling on the performance of higher layer pro-
tocols. Our results show that comparative analysis that
was not affected by the changing scenario conditions
with varied mobility conditions of the UV’s and GS’s As
we know that the wireless sensor networks are resource
constraint networks hence the performance of the routing
protocols for the energy consumed may help for the se-
lection of appropriate protocol. Though the selection of
routing protocol in wireless sensor networks is also de-
pendent on the applications for which the network may
be used but the pr esent results may always be helpful for
the selection of energy efficient routing protocol.
7. Conclusions
The research area of ad hoc network [12] has attracted
the academic world and the industry both due to its var-
ied possible application for anytime, anywhere, any how
communication scenario. This wide spectrum of applica-
tions possible for ad hoc networks has been made the
network vividly applicable. The routing protocol for ad
hoc networks has been a prime research domain during
the present decade. Although extensive efforts have been
exerted so for on the routing problem in wireless com-
munications, there are still some challenges via multi-
casting that confront effective solutions to the routing
problem. A number of such protocols have been devel-
oped. But none of the protocols has been found to be best
suitable for all scenarios. All the protocols have their
own advantages and disadvantages.
Depending on the constraints followed by the net-
works the routing algorithms have been updated and
modified from time to time to make the routing more and
more efficient and accurate. The present work proposes
to find the effects of different patterns of node mobility
within the network. The results though don’t present a
steep comparative orientation of the results towards a
specific routing protocol but the comparative study leads
towards some interesting results.
Further research is needed to find most suitable pro-
tocol for each and every scenario condition so that an
optimized routing protocol could be suggested for vari-
ous real life applications have concurrency to the men-
tioned scenarios of the simulated wireless network envi-
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