Investigating the impact of Group Mobility Models

over the On-Demand Routing Protocol in MANETsMuhammad Shoaib

1, Nasru Minallah

2

1,2University of Engineering & Technology, Peshawar

Peshawar, Pakistan

Sadiq Shah4

National University of Science and Technology

Islamabad, Pakistan

Shahzad Rizwan3

Comsats Institute of Information Technology

Attock, Pakistan

Hameed Hussain5

Comsats Institute of Information Technology

Islamabad, Pakistan

Abstract—Due to the high mobility and frequently changing

network topology, the choice of suitable mobility model and

routing protocol has a significant impact on the performance of

Ad-hoc networks, when deployed in unmanageable and hostile

mobility environments. In this paper, the performance of various

Group mobility models such as RPGM, Column, Nomadic and

Pursue are evaluated and examined over the On-demand routing

protocol (DSR) in the realistic scenarios using performance

metrics like packet delivery ratio, average delay and normalized

routing load. For the implementation, Linux (Fedora) Operating

System is considered as a platform for the research work that is

reliable and compatible with Network Simulator (NS-2) and the

mobility scenario generation tool BENCHManet. The

recommendations of the research will provide better

understanding of the On-demand routing protocol, Mobility

Models and their use in the real world applications such as rescue

and relief operations, military operations, scanning and

searching , tracking and surveillance operations and many more.

Keywords—MANET; DSR; RPGM; NS-2; BENCHManet.

I. INTRODUCTION

In the past few years, the growth and development in the field of portable and mobile communication has increased sharply. The nature of computation has changed from personal computing to ubiquitous computing due to the need of such wireless and portable devices (i.e. laptop, smart phones and wearable devices).In Mobile Ad-hoc Networks the group of mobile and wireless nodes communicate with each other in a decentralized manner and without any permanent communication structure [1].The Wireless Mobile Ad-hoc Networks are impermanent network, which could be installed in no-time, anywhere, anytime. The nodes operate and communicate in decentralized fashion as a host as well as a router [2]. The Ad-hoc Network has numerous and countless applications [3]; such as rescue and relief operations, military tactical operations, scanning and searching, tracking and surveillance operations etc.

Regardless of the numerous applications, MANETs are exposed to several challenges and issues [4].In the wireless Ad-hoc network, mobile nodes has limited bandwidth and battery power hence need a routing protocol which could produce a low overhead, so in most of the situations the on-demand routing protocol provide better results[5,6], rather than pro-active routing protocol. In the on-demand routing protocol [7]

the routes are discovered when needed, so the choice of suitable routing protocol has a significant impact on the performance of Ad-hoc networks.

Such networks also have another issue of frequently changing topology and uncertainty with high mobility situations. The mobility model [8] designed for movement patters must closely match the real time situation to produce better results. Such mobility models could be divided into two groups. In the entity mobility model, the nodes are independent of the movement of other mobile nodes, whereas in group mobility model, the mobile nodes are dependent on the movement of whole group.

The related work of Harris Simaremare et al. [9] evaluated the performance of modified AODV using RWP and RPGM models. Chrisy Samara et al. [10] worked on the performance comparison of three MANET routing protocols AODV, DSDV and OLSR using the real life scenarios. Arindrajit Pal et al. [11] studied the impact of traffic patterns over the on-demand routing protocols AODV and DSR for the RPGM model. Fahim and Nauman [12] analyzed the effect of MANET routing protocol AODV, DSR, DYMO, OLSR and DSDV against the three mobility models RWP, RPGM and CMM. K. Amjad [13] examined the impact of group leader’s mobility using the RPGM model over the Dynamic Source Routing (DSR) Protocol in MANETs. S. R. Biradar et al. [14] analyzed the performance evaluation of on-demand routing protocols AODV and DSR using Group mobility model. Geetha Jayakumar and Gopinath Ganapathi [15] evaluated the performance comparison of reactive routing protocol AODV and DSR over the RWP and RPGM models.

The intention of the research is to evaluate the impact of group mobility models[16,17] such as RPGM, Column, Nomadic and Pursue over the on-demand routing protocol i.e. Dynamic Source Routing Protocol[18,19] in the realistic environment scenarios using performance metrics like packet delivery ratio (PDR), average delay (AD) and normalized routing load (NRL).

The rest of the paper is managed as; Section II describes the overall methodology and system design. Section III depicts the simulation environment based on realistic scenarios. Section IV contains the results and detailed discussion. Section V comprises of conclusion and future work.

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II. METHODOLOGY AND SYSTEM DESIGN

A. Experimental Setup

The main aim of the research work was to evaluate the impact of four different group mobility models under the on-demand routing protocol in the realistic conditions. For generating the realistic environment scenarios Bench-MANET [20] was used where as for simulations, Network Simulator (NS-2) version-2.29 [21] was considered under the platform of Linux (Fedora) operating system. Four different scenarios were carried out using mobility models such as RPGM, Column, Nomadic and Pursue under DSR protocol.

1) Road Accident due to the land sliding in the Hilly Area,

50 nodes with area 900x900m2

2) Soldiers performing Military Drill in the Parade Area, 100

nodes with area 1000x1000m2,

3) Students touring Zoo-Park with the tour guide, 60 nodes

with area 700x700m2,

4) Army Personnel tracking the terrorist in the nearby Sea-

Port Area, 30 nodes with area 800x800m2 respectively.

The Wireless physical IEEE 802.11b was considered with Omni-directional antenna under transmission range of 250m. For radio propagation Two Ray Ground model was employed, where as the Constant Bit Rate (CBR) traffic was generated using cbrgen.tcl script. CMUPri model was used for queuing with

a buffer size of 50. The goal was to evaluate the group mobility impact on realistic scenario and to realize the effect of changing number of groups and nodes within the specific scenario. Fig. 1 shows the procedural flow for simulation in NS-2 Environment.

Fig. 1. Block Diagram for Simulation in NS-2 Environment.

B. Performance Metrices

In this research study the following metrics are used to examine the effect of group mobility over the On-demand routing protocol under the realistic scenarios.

1) Packet Delivery Ratio (PDR): It is the ratio of total

number of packets received by the destination node to the

packets sent by the source node. It is measured in percentage.

2) Average Delay (AD): It is the time required for the

packet to be transmitted from the source node to the

destination node. It is measured in millisecond or seconds.

3) Normalized Routing Load (NRL): It is the ratio of the

total number of routing packets or bytes (i.e. control) sent by

the source to the data packet received by the destination. It is

measured in bytes or packets.Some of the parameters used in the simulation are given in

Table 1.

TABLE.I

Parameters Value

Area(m2) 900x900,1000x1000,700x700,800x800

No. of Nodes 50,20-100,20-60,30

No. of Groups 1-5-10,1-5-10,1-2-5,1-2-5

Speed(m/sec) 1-2,1-2,1-2,1-15

Pause time(sec) 50-300,0,60,0

No. of connections 30,20,30,20

Sending rate(pkts/sec) 5,3,7,3

Simulation time(sec) 900,900,2000,900

Mobility Model RPGM,CMM,NCMM,PMM

Routing Protocol DSR

III. REALISTIC ENVIRONMENT SCENARIOS

A. Road Accident due to land sliding in the Hilly Area

In the first case study a real rescue operation environment of road accident in the hilly area is analyzed and modeled using RPGM mobility model. The rescue operation was performed after the heavy rain which caused land sliding and results in tragic road accident. The area of the disaster zone is surrounded by the terrain size of 900x900m

2. The rescue units covering the

catastrophe area is around 50 in numbers. Due to the sharp turns and narrow passage the rescue activity was restricted to 1-2m/sec mobility speed with the variable pause time between 50 to 300secs. The number of rescue groups was set to 1, 5 and 10 in the present scenario.

B. Soldiers performing military drill in the Parade Area

In this case study the military march scenario is modeled and examined using the Column mobility model. The dimension of the military exercise scenario is bounded by 1000x1000m

2 with the maximum number of 100 marching

soldiers (mobile nodes).During the military exercise the soldiers in each group would follows the marching pattern of group leader with the velocity of 1-2m/sec. For the various military drills the group formation were divided into 1, 5 and 10.

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C. Students touring Zoo-Park with the tour guide

In the third case study the Nomadic community mobility model is used to model the behavior of students that are touring the zoo-park. The park is surrounded by an area of 700x700m

2

with maximum number of 60 students and touring guide. The touring guide led the students and briefs them with the pause time of 60 seconds and with the human speed of 1-2m/sec throughout the park. The scenario is organized in 1, 2 and 5 groups respectively according to the number of mobile nodes.

D. Army personnel tracking the Terrorist in the nearby Sea-

Port Area

In the fourth and last case study the realistic scenario of tracking the terrorist is performed using Pursue mobility model. The military team members pursuing the terrorist near the sea port. The tracking operation is performed through the army personnel with military vehicle units as well, in the nearby sea port surroundings. The area is surrounded by the terrain of size 800x800m

2. Around 30 army personnel are

taking part in this special operation with a variable speed of 1-15m/sec. In order to assault the terrorist the operation units are grouped into 1, 2 and 5.

IV. RESULTS AND DISCUSSION

A. Scenario1: Road Accident due to land sliding in the Hilly

Area

The scenario depicts the impact of RPGM mobility model over the on-demand routing protocol. The figures 2, 3 and 4 shows the effect of increasing pause time on the performance of DSR protocol for 50 mobile nodes with in 900x900m

2 area

divided into three groups i.e. 1, 5 and 10 under rescue operation considered on road accident in the hilly area.

1) Impact of Packet Delivery Ratio(PDR):

Fig. 2. packet delivery ratio vs. pause time

The Fig. 2 demonstrates the impact of packet delivery with different number of groups i.e. 1, 5 and 10 under different pause times. In single group mobility the PDR tends to remain stable, due to the availability of all the nodes within the single group also the area size is irrelevant of different group sizes. In group5, degradation in PDR is observed at higher pause times i.e. 150 seconds till 250 sec. The reason behind is the increase in number of groups and decrease in nodes per group. In case of 10 groups, PDR tends to be irregular at certain pause times due to less number of nodes per group and the probability level of inter-group communication has increased during the entire simulation time. The variation in the PDR is due to the random

movement of groups, nodes within the groups and long pause intervals.

2) Impact of the Average Delay(AD):

Fig. 3. average delay vs. pause time

The Fig. 3 shows the effect of average delay with different pause time. In case of group 1, the average delay remains constant and stable due to full node density in single group and low packet drop ratio. In group 5, the number of nodes per group decreases and causes more packet delay in case of inter-group communication. At maximum of group 10, the average delay fluctuates due to both possibilities of inter and intra-group communication and increase in average hop count as the groups sparse apart in the large area and causes delay to increase. The reason behind variation in average delay is mainly due to sparse groups, random group’s movements and large area with high pause time and low mobility speed.

3) Impact of the Normalized Routing Load(NRL):

Fig. 4. normalized routing load vs. pause time

The Fig. 4 depicts the effect of increasing pause time over the normalized routing load. In single group, the protocol load remains low and stable due to the full node density in the single group. In case of 5 groups, the protocol load tends to increase beyond 200 sec, due to higher delay, packet drop and more static network behavior. While in 10 groups, the protocol load fluctuates at the start and then increases sharply with pause time, due to high percentage of inter-group communication, sparseness of groups in the large area and high route discoveries. The increase in protocol load cause more bandwidth consumption and reduces the overall routing performance of a routing protocol i.e. DSR.

B. Scenario2: Soliders performing militry drill in the Parade

Area

The second scenario describe the impact of Column mobility model over the on-demand routing protocol (DSR).

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The figures 5, 6 and 7 shows the effect of increasing number of nodes from 20 to maximum of 100 on the performance of DSR protocol within the area size of 1000x1000 m

2 divided

into three groups i.e. 1, 5 and 10 under military march scenario.

1) Impact of Packet Delivery Ratio(PDR):

Fig. 5. packet delivery ratio vs. no. of nodes

The Fig. 5 shows the packet delivery ratio of DSR protocol in military exercise scenario using column mobility model. In single group, the PDR is high and stable due to node’s following the single group mobility, while in group 5 and 10, the PDR is low at the start but keeps on increasing as the number of nodes increases. The reason of good packet delivery ratio with network size is more intra-group communication than inter-group communication and the probability of finding target node lies in the same group increases with increase in number of nodes per group.

2) Impact of Average Delay(AD):

Fig. 6. average delay vs. no. of nodes

The Fig. 6 displays the average delay with the increase in

number of nodes from 20 to 100. In group 1, all the nodes are

in single group so causing increase in number of hop distance

from source to destination and more link breakages which

increases the delay. While in group 5, the average delay is

lower than single group, as due to the less number of nodes per

group. Similarly in case of group 10, there is lesser number of

nodes per group as compared to group 1 and 5, causing fewer

hops to reach a destination so results in low average delay.

3) Impact of Normalized Routing Load(NRL):

Fig. 7. normalized routing load vs. no. of nodes

The Fig. 7 demonstrates the normalized routing load of DSR protocol using column mobility model. It showed that the protocol load tends to increase as packet drop ratio increases. In group 1, the protocol load effect is low and constant. However, increasing number of groups to 5 and 10 tends to raise the protocol load at start due to more routing traffic and less data traffic but as number of nodes per group increase in case of group 5 and 10, the protocol load decline due to more intra-group communication and low routing or control packets.

C. Scenario3: Students touring Zoo-Park with the tour guide

The third scenario depicts the effect of Nomadic mobility model over the reactive routing protocol (DSR). The figures 8, 9 and 10 shows the effect of increasing number of nodes from 20 to maximum of 60 on the performance of DSR protocol within the area size of 700x700 m

2 divided into three groups

i.e. 1, 2 and 5 under Zoo-Park scenario.

1) Impact of Packet Delivery Ratio(PDR):

Fig. 8. packet delivery ratio vs. no of nodes

The Fig. 8 shows the impact of packet delivery ratio with the increase in number of nodes. In case of single group, the PDR is observed highest than all, due to the single group mobility. In case of group 2 and 5, increase in number of groups, decreases PDR at the beginning as due to the more inter-group communication and link breakages but as the nodes per group increases the packet delivery ratio also increases. The aggressive cache mechanism deteriorates due to random movement of groups and nodes within the groups.

2) Impact of Average Delay(AD):

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Fig. 9. average delay vs. no. of nodes

The graph shown in fig. 9 describes the average delay of DSR using nomadic mobility model. The single and two groups’ scenario revealed low and stable average delay in seconds due to high concentration of intra-group communication and less probability of inter-group communication. Whereas in group 5, the probability of inter-group communication is higher with aggressive route discoverers, increases the average delay.

3) Impact of Normalized Routing Load(NRL):

Fig. 10. normalized routing load vs. no. of nodes

The Fig. 10 describes the normalized routing load of nomadic mobility model scenario for various group sizes. In group 1, the protocol load is minimum due to single group mobility. In case of group 2 and 5, the protocol load increases due to the increase in the groups, inter-group communication and more route discoveries. DSR showed good behavior in case of protocol load for medium size network, due to its reactive nature and route cache mechanism.

D. Scenario4: Army personnel tracking the Terrorist in the

nearby Sea-Port Area

The fourth and the last scenario describe the impact of Pursue mobility model over the on-demand routing protocol (DSR). The figures 11,12 and 13 shows the effect of increasing speed from 1m/sec to the maximum of 15 m/sec for 30 mobile nodes within the area of 800x800 m

2 divided into three groups

i.e. 1, 2 and 5 under tracking operation in the sea-port area.

1) Impact of Packet Delivery Ratio(PDR):

Fig. 11. packet delivery ratio vs. speed

The Fig. 11 clearly shows the effect of varying speed on the PDR using pursue mobility model. In case of group1, the mobility speed has minimum effect due to single group mobility and intra-group communication. However, the effect of increasing mobility speed has some adverse effect on group 2 and 5 than that of group 1, due to link or routing path disconnections which causes packet loss during inter-group communication. DSR performed well over different mobility speeds and group sizes due to small network and ability to track the target node due to co-operative working concept and behavior of pursue mobility model.

2) Impact of Average Delay(AD):

Fig. 12. average delay vs. speed

In fig. 12, the average delay of DSR increases at different group sizes using low to high mobility speeds. In case of single group, average delay remains stable due to the intra-group communication and single group mobility. However, by increasing group size to 2 and 5, the average delay tends to rise with increase in mobility speed. Increasing group size causes decrease in nodes per group and more probability of inter-group communication but more average delay is due to packet drop and less utilization of cache mechanism.

3) Impact of Normalized Routing Load(NRL):

Fig. 13. normalized routing load vs. speed

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The Fig. 13 shows the effect of normalized routing load with respect to mobility speed for DSR protocol. The result shows that with the increase in number of groups, the protocol load also increases with high mobility speeds. In single group, protocol load remains low due to single group mobility. In group 2 and 5, increase in speed causes route or link breakages which needs more control traffic to reconstruct the routing paths. At extreme speed, the protocol load using group 5 is highest than all as shown in the figure above.

V. CONCLUSION AND FUTURE WORK

In this research paper the performance of on-demand routing protocol(DSR) is tested under various group mobility models such as RPGM, Column, Nomadic and Pursue in terms of packet delivery ratio, average delay and normalized routing load under realistic environment scenarios. As discussed and examined in results and discussion section, the performance of dynamic source routing protocol is greatly affected by the choice of suitable mobility model and scenario being employed.

The results illustrated that, in the Rescue Operation scenario, with the increase in pause time DSR performed well in terms of packet delivery ratio, average delay and normalized routing load in the single group. However, increasing group size brings degradation and certain fluctuations in these metrics at high pause time. In case of Military March scenario, DSR showed excellent results in terms of packet delivery ratio and normalized routing load with the increase in number of nodes and nodes per group. However, slight fluctuation has been seen in the average delay at different points. In case of Students touring Zoo Park scenario, DSR expressed good and stable results for packet delivery ratio even using large network and group size. However, sharp increase in average delay and normalized routing load value is observed only at large network and group size. In last scenario i.e. Army Personnel tracking terrorists, the DSR has performed well in-terms of packet delivery ratio but showed higher delay and normalized routing load only at higher group size and mobility speed.

In the future, we are interested in proposing a robust on-demand routing protocol (i.e. optimized version of DSR) for deployment of different realistic scenarios such as Urban City Area Network, Combat Operation Network and Mesh Network etc.

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