15.Effect Full

7/29/2019 15.Effect Full

1/13

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]


2/13

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.


3/13

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.


4/13


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.


5/13


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


6/13


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


7/13


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


8/13


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.


9/13


Figure 4: Average End to End Delay

Figure 5: Average Residual Energy


10/13


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

REFERENCES

1. I. Akyildiz, W. Su and Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey,ComputerNetworks Journal 38 (2002) 393422

2. K. Akkaya, M. Younis, A Survey Of Routing Protocols In Wireless Sensor Networks, Ad Hoc Network Journal 3(3) (2005) 325-349

3. WIRELESS SENSOR NETWORKS: Technology, Protocols and Applications, John Wiley and Sons, 2007.4. V. Govindaswamy, W. L. Blackstone, G. Balasekaran, Survey of recent position based routing mobile ad-hoc

network protocols, in: UKSim 13th International Conference on Modelling and Simulation, 2011, pp. 467471.

5. C. E. Perkins, P. Bhagwat, Highly dynamic destination-sequenced distance-vector routing (dsdv) formobilecomputers, Computer Communications (1994) 234244.

6. C. E. Perkins, E. M. Royer, Ad-hoc on-demand distance vector routing, in: Second IEEE Workshop on MobileComputer Systems and Applications, 1999, pp. 90100.


11/13


7. C. E. Perkins, E. M. Roye, S. Das, http://www3.ietf.org/proceedings/05mar/RFCs/rfc3561.txtAd hoc on-demanddistance vector (aodv) routing, Tech. rep., IETF RFC 3561 (July

2003).http://www3.ietf.org/proceedings/05mar/RFCs/rfc3561.txt

8. Mohamad Nazim Jambli, Kartinah Zen, Halikul Lenando, Alan Tully, Performance Evaluation of AODV RoutingProtocol for Mobile Wireless Sensor Network, 2011 7th International Conference on IT in Asia (CITA).

9. Salvatore F. Pileggi, Carlos Fernandez-Llatas, Teresa Meneu, Evaluating Mobility Impact on Wireless SensorNetwork, 2011 UKSim 13th International Conference on Modelling and Simulation.

10. Qingting Wei, Hong Zou, Efficiency Evaluation and Comparison of Routing Protocols in MANETs, InternationalSymposium on Information Science and Engineering, 2008.

11. Abd Al Basset Almamou, Raphaela Wrede, Pardeep Kumar, Houda Labiod, Jochen Schiller,PerformanceEvaluation of Routing Protocols in a Real-World WSN, IEEE, 2009 .

12. Miguel Garcia, Hugo Coll, Diana Bri, Jaime Lloret, Using MANET protocols in Wireless Sensor and ActorNetworks, The Second International Conference on Sensor Technologies and Applications, 2008.

13. A S Raghuvanshi, S Tiwari, DYMO as routing protocol for IEEE-802.15.4 Enabled Wireless Sensor Networks,IEEE 978-1-4244-9730-0/10, 2010.

14. Fan Bai, Ahmed Helmy, A Survey Of Mobility Models in Wireless Adhoc Networks, Kluwer AcademicPublishers, 2004.


12/13


13/13

Documents

15.Effect Full