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International Journal of Computer Networking,
Wireless and Mobile Communications (IJCNWMC)
ISSN 2250-1568
Vol. 3, Issue 1, Mar 2013, 137-148
TJPRC Pvt. Ltd.
EFFECT OF MOBILITY MODELS ON PERFORMANCE OF MOBILE WIRELESS
SENSOR NETWORKS
MANDAR KARYAKARTE1, ANIL TAVILDAR1 & RAJESH KHANNA21Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
2Thapar University Patiala, Punjab, India
ABSTRACT
Mobile Wireless Sensor Networks (MWSN) has potential applications in the diverse areas such as wildlife
monitoring, monitoring for pollutant levels, remote health care monitoring etc. MWSN are very similar to Mobile Ad-hoc
NETworks (MANETs), but resource constraint attributes of MWSN demand performance analysis of effects caused by
various node mobility patterns. Random Waypoint model is widely used in studies of MWSN, which does not give a true
random node movement as expected in many MWSN applications. In this paper, we have evaluated the performance of
Random Waypoint, Random Direction, Random Walk and Gauss Markov mobility models for four node density scenarios.
Each node density scenario is simulated for three node movement speeds.
A simulation based analysis of throughput, packet delivery function, end to end delay, residual energy and routing
overhead is performed. Comparison is done with static WSN scenario, in order to estimate the performance degradation
due to mobility environment.
KEYWORDS:Mobile Wireless Sensor Networks, Mobility Models, AODV, Performance
INTRODUCTION
The advances in Micro Electro Mechanical Systems (MEMS) and communication technologies have lead to
development of tiny, cheaper and battery operated sensor motes which can communicate over wireless links. Network of
such sensor motes known as Wireless Sensor Networks (WSN), have potential applications in diverse fields such as
process control, battlefield surveillance, environmental monitoring, monitoring for agriculture crops, habitat monitoring,
pollution monitoring etc. WSN have distinguished characteristics compared to cellular and Mobile Adhoc NETworks
(MANETs). WSN exhibit attributes like local node identity, application specific design, dense node deployment, self
configuring, many-to-one traffic, high data redundancy. WSN are also constrained by bandwidth, energy, computational
power and frequent topology changes. Moreover, WSN may be deployed in unattended or hazardous environments and as
result must be supported with energy efficient routing protocol to maximize network lifetime.
The network layer is responsible for routing sensed data to the data sink. Multi-hop short-range communication
strategy is better compared to long range communication as later is characterized by high energy requirement,
implementation complexity, channel fading and propagation effects. The many-to-one data traffic pattern and hop-by-hop
short range communication induce problems like congestion, delay, packet loss and energy consumption. Especially the
nodes closer to the sink are the most affected and drain quickly.
The routing protocol designs for WSNs need to consider energy constraints and data traffic pattern of the network.
The sensor nodes sense the data and use multi-hop relays to transmit the data for further analysis and monitoring. The
design of routing protocol is required to be robust and adjust the transmission path according to the constraints.[3]
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138 Mandar Karyakarte, Anil Tavildar& Rajesh Khanna
Many researchers [1], [2] have already proposed various different routing protocols for resource constrained WSN
with an assumption of stationary sinks and sensor nodes. For WSN applications like water body monitoring, wild-life
monitoring, monitoring inhalation levels of toxic gases etc require sensor nodes to be mobile and sometimes also be
integral part of phenomena to be monitored. The mobility of sensor nodes is induced due to external factors (eg wind flow,
water currents, mobility of sensor carrying hosts etc). Mobile Wireless Sensor Networks (MWSNs) being an ad-hoc
network, MANET routing protocols are primary choice for routing. The routing protocol such as ADOV, DSR, DSDV and
OLSR have been investigated on the MANETs in the past few years. The investigation of the performance of these
protocols on the MANETs has produced many useful results. However, very limited findings of how these Ad-hoc routing
protocols perform for MWSNs have been reported. In this paper, the performance of Ad-hoc On-demand Distance Vector
(AODV) [6], [7], one of the widely used MANET routing protocol, is investigated for MWSN by using various different
known mobility models.
The paper is organized as follows. The investigations of other researchers are briefly summarised in Section 2.
Section 3 briefly presents various mobility models. The performance metrics used for evaluation are discussed in Section 4.
The simulation scenario considered for the investigations is presented in Section 5. Results for various performanceparameters are presented and briefly discussed in Section 6 In the last section the conclusions are drawn on the basis of
investigations.
APPLICATION OF PANEL DATA ANALYSIS
RELATED WORK
The routing protocol for MANETs can be classified as on-demand reactive protocols such as Ad-hoc On-demand
Distance Vector (AODV) and table driven proactive protocols such as Destination Sequenced Distance Vector (DSDV).
Wei and Zou [10] have compared efficiency of AODV and DSDV for parameters like terrain, speed and number of nodes
on MANETs. Pileggi et al [9] have studied the impact of mobility on clustered WSN for finding connected and
unconnected nodes for various communication ranges. The work is limited due to assumptions like no packet loss network
and highly reliable links. Alamamou et al [11] have implemented Better Approach for Mobile Ad-hoc Networking
(BATMAN) and Optimal Link State Routing protocols on MSB 430 sensor platform for static WSNs.
The two protocols were evaluated using packet delivery ratio and routing overhead as performance metrics.
Garcia et al [12] have simulated MANET protocols AODV, DSR, OLSR over mobile WSN using OPNET. The metrics
used for comparison are throughput and delay. The fairness of the work is restricted due to non-realistic mobility pattern
considered for simulation. Raghuvanshi and Tiwari [13] have used Qualnet for measuring performance of AODV and
DYnamic MANET On-demand (DYMO) protocols over static WSN for parameters like throughput and delay.
APPLICATION OF PANEL DATA ANALYSIS
MOBILITY MODELS
Random Waypoint Mobility Model
The Random Waypoint Mobility Model (RWMM) includes pauses between changes in direction and/or speed
[14]. A Mobile node begins by staying in one location for a certain period of time (i.e pause). Once this time expires, the
mobile node chooses a random destination in the simulation area and a speed that is uniformly distributed between [min-
speed, max-speed]. The mobile node then travels toward the newly chosen destination at the selected speed. Upon arrival,
the mobile node pauses for a specified period of time, starting the process again. Figure 1a shows a mobility pattern for a
node in simulation.
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Effect of Mobility Models on Performance of Mobile Wireless Sensor Networks 139
Random Direction Mobility Model
Random Direction Mobility Model (RDMM) is designed to avoid concentration of mobile nodes (MNs) at centre
of the simulation area, as seen in the Random Waypoint model [14]. In this model, MNs choose a random direction in
which to travel similar to the Random Walk Mobility Model. An MN then travels to the border of the simulation area in
that direction. Once the simulation boundary is reached, the MN pauses for a specified time, chooses another angular
direction (between 0 to 180 degrees) and continues the process. Typical node mobility simulated using random direction
mobility model is shown in Figure 1b.
Random Walk Mobility Model
In Random Walk Mobility Model (RWkMM), a mobile node moves from its current location to a new location by
randomly choosing a direction and speed in which to travel [14]. The new speed and direction are both chosen from pre-
defined ranges, respectively [min-speed, max-speed] and [0, 2*pi] respectively based on uniform distribution. Each
movement in the RWkMM occurs in either a constant time interval tor a constant travelled distance d, at the end of
which a new direction and speed are calculated. Typical mobility simulated using the RWkMM is shown in Figure 1c
Gauss-Markov Mobility Model
Gauss-Markov Mobility Model (GMMM) was designed to adapt to different levels of randomness via tuning
parameters. Initially each mobile node is assigned a current speed and direction. At each fixed intervals of time n a
movement occurs by updating the speed and direction of each mobile node. Specifically, the value of speed and direction at
the nth instance is calculated based on the basis of the value of speed and direction at the (n-1)st
instance and a random
variable using the following equations:
,
And
Where and are the new speed and direction of the mobile node atthe time interval n, where 0 < alpha < 1, is
the tuning parameter used to vary the randomness s and d are constants representing the mean value of speed and direction
as n > infinity and and , are random variables from a Gaussian distribution. Totally random values(or
Brownian motion) are obtained by setting alpha = 0 and linear motion is obtained by setting alpha=1. Intermediate levels of
randomness are obtained by varying the value of alpha between 0 and 1. At each time interval the next location is
calculated on the basis of the current location, speed and direction of the movement. Specifically, at time interval n, a
mobile nodes position is given by the equations:
,
And
The Figure 1d shows simulation result for mobility of node using GMMM, where is chosen randomly based on
uniform distribution between the interval 0 to 1.
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140 Mandar Karyakarte, Anil Tavildar& Rajesh Khanna
From the Figure 1 it is observed that various mobility models, excepting GMMM, exhibit straight line movement
patterns. GMMM, however, results in truly random movement of nodes. Further it is noted that RWMM gives higher
probability of node concentration at the centre of simulation area. RDMM leads to higher concentration of nodes near the
boundary region of the simulation area. The RWkMM, provides fairly uniform distribution of nodes in the simulation area
Figure 1: Sample Node Movement for Mobility Models
PERFORMANCE METRICS
The response of AODV to various mobility models in MWSN is evaluated using following performance metrics.
Throughput
Throughput in kbps is measured as number of bits received by sink from various different sensor nodes. The
number of bits received is calculated using number of packets received at sink and size of each packet.
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Effect of Mobility Models on Performance of Mobile Wireless Sensor Networks 141
Where , and represent Number of packets received, Size of Packet and Simulation time respectively.
Higher the value of the metric indicates better performance.
Packet Delivery Function
Packet delivery function (pdf) is defined as ratio of number of data packets received at sink denoted by to total
number of data packets sent by various sensor nodes denoted by
Where n is number of sensor nodes. The metric value will range vary between 0 to 1, with higher value of pdf
indicating better performance.
Average End to End Delay
The average end to end delay measured in seconds, given as can be defined as average ofsum of transmission
delay of each packet received at sink. The transmission delay of each packet can be measured as difference between
time at which the ith
packet was received at sink and time at which the packet was sent at source node.
,
Smaller the value of average end to end delay indicates better performance.
Residual Energy
The sensor nodes consume energy for transmitting and receiving the data packets. The residual energy is
measured as difference between the initial energy of a node and energy consumed by node. The energy consumed
by each node denoted as can be measured as
,
The average residual energy per node measured in Joules is calculated as
where n represents number of sensor nodes. Higher the value of average residual energy, higher will be the
network lifetime.
Routing Overhead
Routing overhead is defined as ratio of total routing packets exchanged denoted by to total data packets
received at sink denoted by
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142 Mandar Karyakarte, Anil Tavildar& Rajesh Khanna
The metric value will range in 0 to 1. For better performance the of rovalue should be as small as possible.
SIMULATION SCENARIO
Wireless Sensor Network simulation is done using Network Simulator 2 (NS2). The simulations are carried over
area of 150 m x 150 m. for time period of 200 seconds. Each sensor node transmits the packet at an interval of 5 seconds.
The simulation is done with AODV routing protocol by using various mobility models discussed in Section 3. The sensor
node specifications considered in the simulation are given in table of Table 1. The sink is assumed to be stationary and
placed at the centre of simulation area. The sensor node deployment is considered random over the geographical area for
simulation. Considering the simulation area to be fixed, the total number nodes are varied. Four specific cases, one
representing low node density i.e 36 sensor nodes, two cases representing medium node density i.e 72 and 108 sensor
nodes and one case of high node density i.e 144 sensor nodes. For each of the case mentioned above, three speeds viz
0.5m/s, 1m/s and 1.5m/s are considered for node movement. The speeds are chosen based on some typical MWSN
applications such as water quality monitoring in closed environment (low speed), health care monitoring (medium speed)
or border security surveillance monitoring (higher speeds). The results calculated are based on average after 20 trials of
simulation. The node movements are defined as per the mobility models discussed in Section 3. All the simulation
scenarios are evaluated as per the performance metrics defined in Section 4.
Table 1: Specifications of Typical Sensor Node
Sr No. Parameter
CPThresh 10.0
CSThresh 1.559e 11
RXThresh 3.652e 10
Rb 2 _ 1e6Pt 0.2818
freq 914e6
RESULTS
Throughput
The results for throughput for various simulation scenarios are shown in Figure 2. For comparison sake we have
also included results of static WSN.
It is observed that, the throughput increases with decrease in speed. At a given node density, throughput is
maximum for GMMM and minimum for RDMM for all speeds. In RDMM, this may be attributed to nodes located
towards the boundary region. At a given speed, throughput increases with increase in node density, which is expected
result. The performance of RWMM and RWkMM is between the two extremes.
It is observed that for lower node density and low speeds the throughput for GMMM is comparable with static
WSN, whereas for all other mobility models some definite degradation is observed with respect to static WSN. This
degradation significantly increases with increase in speed of node movement.
Packet Delivery Function
The simulation result for packet delivery function is shown in Figure 3. It is observed that the pdf value is better
at lower speeds compared to pdf value at higher speeds, which is expected result. It is observed that, GMMM gives
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Effect of Mobility Models on Performance of Mobile Wireless Sensor Networks 143
higher pdf value and RDMM gives lower pdf compared to other mobility models. This may be due to true randomness
achieved in GMMM, which increases the probability of mobile nodes coming closer to sink. The performance of RWMM
and RWkMM lies in between the two extremes.
Comparing the performance with static WSN, it is noted that there is performance degradation in pdf value for
all the mobility models, at higher node density which is a expected result. However this degradation is marginal for
GMMM. In fact, for lower speeds and lower node densities, GMMM exhibits higher pdf value compared to static WSN.
This may be due to the true random node movement pattern in GMMM, as explained above.
Figure 2: Throughput
Figure 3: Packet Delivery Function
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144 Mandar Karyakarte, Anil Tavildar& Rajesh Khanna
Average End to End Delay
The results for average end to end delay are shown in Figure 4. It is observed that average end to end delay
increases with increase in number of nodes at all speeds. It is observed that GMMM shows lowest value of end to end
delay and RDMM shows highest value, compared to all other mobility models, which are expected results. The
performance of RWMM and RWkMM is in between the two extremes.
Comparing the performance of all mobility models with static WSN, there is considerable degradation in end to
end delay performance for mobility models RDMM, RWMM and RWkMM, which is expected. For GMMM, the
performance degradation is marginal.
Residual Energy
The residual energy is measured as average of residual energy of each node, the results for which are shown in
Figure 5. It is observed that average residual energy decreases with increase in node density for all mobility models at all
speeds, which is expected result.
The average residual energy for RDMM and RWMM is comparable for low and medium node densities, for all
speeds. But at higher node density RWMM shows better performance as compared to RDMM.
GMMM shows considerable apparent degradation for average residual energy compared to all other mobility
models. This may be due to requirement of calculating instantaneous speed and direction at each node for each step of
movement, which adds to energy consumption during simulations. In other mobility models speed is constant and new
direction is calculated only when the target destination is reached.
Compared to static WSN, GMMM shows a considerable apparent degradation in performance, which is expected
due to increased energy consumption for mobility management in GMMM simulations. This degradation is marginal for
RDMM and RWkMM. For RWMM, it is observed that average residual energy is more compared to static WSN, which
may be attributed to higher probability of nodes close to sinks, which considerably reduces demand on energy.
Routing Overhead
The results for routing overhead are shown in Figure 6. The routing overhead increases with increase in node
density which is expected result for all models at all speeds. RDMM shows higher routing overhead and GMMM shows
lowest routing overhead compared to other mobility models. For RDMM this may be attributed to node locations near the
boundary area. For GMMM, this may be attributed to true random movement of nodes in controlled manner. The
performance RWMM and RWkMM is in between the two extremes.
Comparing the performance with respect to static WSN, it is observed that routing overhead increases
considerably for RDMM and RWkMM for all node densities at all speeds. The performance of RWMM and RWkMM is
comparable to static WSN for high node density and shows degradation at low node density.
GMMM shows lowest routing overhead for low node densities and low speeds. The performance shows an
improvement compared to static WSN, which may be due to same true random node movement pattern in GMMM,
explained above.
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Effect of Mobility Models on Performance of Mobile Wireless Sensor Networks 145
Figure 4: Average End to End Delay
Figure 5: Average Residual Energy
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Figure 6: Routing Overhead
CONCLUSIONS
The performance for various mobility models have been compared for different typical WSN speeds and node
densities. It is observed that GMMM gives true random movement and exhibits better performance for throughput, packet
delivery function, average end to end delay and routing overhead. It also shows a higher degradation for average residual
energy. However, this is due to additional energy requirements for varying speed and direction at each step of movement.
However, this energy requirement is only for simulation purpose. In actual mobile WSN application, node movement is
due to inherent property of phenomenon being monitored. As such GMMM degradation may be comparable with other
mobility models in actual practice. In view of the above, it is suggested to use GMMM for simulating mobility as it
provides true random node movement
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