2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
Applying OLSR Routing in FANETs Kuldeep Singh 1 ,Anil Kumar Verma 2 Research Scholar, 2Associate Professor 1,2 Computer Science and Engineering Department,Thapar University, Patiala, India. 1
[email protected],
[email protected] 1
Abstract— MANETs are getting exposure due to their versatile applications in the last few years. New networking paradigms like VANETs and FANETs have evolved by using the concept of MANETs. FANETs provide a distinguished approach to tackle with the situations like emergency, natural disaster, military battle fields, UAVs etc. Due to the high mobility in FANETs nodes and rapid topology change, it is a big challenge for researcher to apply routing in FANETs. Mobility models also play a very important role in optimizing the performance of routing protocol in FANET. The research presented aims to apply OLSR routing protocol in FANETs and study of OLSR under different mobility models to optimize the performance of OLSR in FANETs. Keywords: FANET, MAVs, Mobility Models, OLSR.
I.
INTRODUCTION
Flying adhoc network (FANET) represents a particularly new class of adhoc networks. FANET is allow to send information quickly and accurately in a situation, where generic adhoc networks are not capable to do so. At the time of natural disaster like flooding, earthquakes and even in military battlefield FANET can perform better than other form of mobile adhoc networks [1]. FANET uses a group of homogenous flying agents called MAVs(Micro Air Vehicle ) communicates with each other locally, and also interacts with their environment to get some sort of information. FANETs do not support central control system [2]. In FANET position of MAVs changes rapidly and because of this, there are frequent changes in topology.
Figure 1. Flying Adhoc Network
Due to FANTE’s versatile applications this field required attention of researchers, but till now no significant work has ISBN No. 978-1-4799-3914-5/14/$31.00 ©2014 IEEE
been done. High mobility of nodes in FANET is a very big issue, so here we have applied and analyzed OLSR in FANETs. Also try to find a mobility models through which performance of OLSR can be improved for FANET. OLSR (Optimized Link State Roting) OLSR is proactive routing protocol which is designed especially for adhoc networks [3]. OLSR is a proactive routing protocol. In proactive protocol every node maintains one or more tables representing the entire topology of the network. Because of the proactive nature, this protocol has a benefit of having routes quickly available whenever required. To maintain the up-to-date routing information, all the nodes declared and broadcast their topology information in the whole network [4]. OLSR provides following features: • It reduces the size of control packets by declaring a node as a multi-point-relay (MPR) selector to each and every nodes of its neighbour. • By using those MPRs which were selected, it reduces to scatter its messages to the whole network. OLSR use MPR through which it is able to reduce the whole network traffic and also reduces the flooding in the network which is arises when every node transmits data to each other to send the message to the exact destination. Hence, this routing protocol is best fit for large and dense adhoc networks [5, 6]. II.
OVERVIEW OF MOBILITY MODELS
Mobility models define the pattern of moving nodes. Mobility models consider how node’s position, velocity and acceleration changes with time. To optimize the protocol performance, Mobility models play a remarkable role. It is desirable for mobility models to emulate the movement pattern of nodes targeted real life applications and scenario in a reasonable way [7]. When evaluating FANET protocols, it is very important to select the proper underlying mobility model. We have analyzed those mobility models through which we can take all the aspects of real time applications. Previous work repeatedly demonstrated that using different mobility models, the performance of a protocol can vary significantly [8, 9, 10]. We have taken group as well as entity mobility models which are discuses below in detail. A. RandomWaypoint Mobility Model The Random Waypoint Mobility Model involves pause times before changes in direction and speed of a mobile node. A 1212
2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) mobile node begins by staying in one location for some period of time i.e. a pause time. Once this time ends, the mobile nodes choose a random destination in the simulation area and a speed that is uniformly distributed between min-speed and max-speed. The mobile node then travels toward the newly chosen destination at the selected speed. B. Manhattan Grid Mobility Model In the Manhattan Grid model nodes move only on prespecified paths, means there is a path defined already for movement of nodes. As fig. 2 defined the paths on the simulation area is an example of manhattan grid mobility model:
Figure 2. Manhattan Grid path
C. Reference Point Group Mobility Model The reference point group mobility (RPGM) model is a group mobility model. This represents the random motion of a group of mobile nodes and also describes random motion of each individual mobile node within the group. The movements of group nodes are dependent on the path traversed by the center for the group [11]. The motion of the group center fully specifies the movement of its corresponding group of mobile nodes including their direction and speed. Individual mobile nodes randomly move about their own pre-defined reference points, whose movements depend on the group movement [11]. D. Pursue Mobility Model The pursue mobility model attempts to represent mobility nodes tracking a particular target. The pursue mobility model consists of a single update equation for the new position of each mobile node:
an open source simulator. NS-2 is an application level simulator, written in C++ and it uses OTCL (object oriented extension of tool command language) interpreter as a frontend. NS-2 has support for both wired networks and wireless networks. NS-2 can simulate difference types of network protocol like TCP, UDP and multicast routing [13, 14]. B. Simulation Parameter TABLE 1. SIMULATION PARAMETERS
Parameter Simulator
NS-2(Version-2.35)
Channel Type
Channel/Wireless Channel
Protocol
OLSR
Simulation Duration
900s
Number of Nodes per 20 Simulation Mobility Models Random Waypoint, Manhattan Grid , RPGM, Pursue MAC Layer Protocol 802.11 Traffic Type
CBR
Data Payload
512 bytes/packet
Max of CBR Connections Node Speed
200
III.
• Packet Delivery Ratio: Packet delivery ratio defines
the ratio of sent and delivered packets, means how many packets were sent and how many actually delivered to the destination.
Packet delivery ratio = (Total number of packet sent/ Total number of packet received)
• End to End Delay: End to End tells that the average time taken by a packet to arrive at the destination, it also involves delay in queue and delay caused by the route discovery. End to End delay= (arrive time –send time)/ number of connections
IMPLEMENTATION
A. Simulation Plateform NS-2 simulator is used for evaluating the performance of OLSR with different mobility models [12]. NS-2 simulator is
5,10,20,30,40,50 (m/sec)
C. Performance Parameter In this paper, three performance parameters (Packet Delivery Ratio, End to End Delay and Average Throughput) are considered for analyzing the different mobility models for OLSR. By comparing these parameters, we can identify a better mobility model in OLSR for FANETs.
New position = (old position + acceleration [target -old position] +Random vector) Where random vector is a random offset for each MN and acceleration (target-old position) is information on the movement of the mobile nodes being pursued. The random vector value is obtained via an entity mobility model. The degree of randomness for each MN is limited in order to maintain effective tracking of the MN being pursued [11].
Value
•
Throughput: Throughput shows the bandwidth of the protocol.
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2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) The results obtained from trace files using AWK scripts are shown below. IV.
RESULTS AND ANALYSIS
A. Result Tables After performing the simulation, Table-2 shows results of packet delivery ratio, Table-3 shows results of Average throughput and Table-4 shows results of end to end delay. Table 2. Speed vs. Packet Delivery Ratio.
Mobility Models OLSR with Random Waypoint OLSR with Manhattan Grid OLSR with RPGM OLSR with Pursue
Speed(m/sec) 5
10
20
30
40
50
0.998
0.995
0.988
0.986
0.983
0.901
0.992
0.991
0.989
0.998
0.991
0.987
0.997
0.961
0.927
0.946
0.897
0.995
1
1
0.996
1
0.997
1
Figure 3. Speed vs. Packet Delivery Ratio.
Table 3. Speed vs. Average Throughput
Mobility Models
Speed(m/sec) 5
10
20
30
40
50
Random
64.03
63.6
63.54
63.42
62.95
57.44
OLSR with Manhattan Grid
63.03
63.93
63.23
63.42
63.54
63.51
OLSR with RPGM
64.04
61.04
59.44
61.49
57.62
63.57
OLSR with Pursue
64.61
64.61
64.61
64.61
64.61
64.61
OLSR with Waypoint
Figure 4. Speed vs. throughput
Table 4. Speed vs. End to End Delay
Mobility Models OLSR with Random Waypoint OLSR with Manhattan Grid OLSR with RPGM OLSR with Pursue
Speed(m/sec) 5 8.920
10 8.989
20 7.322
30 11.571
40 11.911
50 10.614
12.897
8.921
17.494
10.657
12.300
12.411
9.953
6.713
6.093
8.236
6.624
7.563
6.234
6.233
6.234
6.233
6.333
6.533
B. Result Graphs Figure 5. Speed vs. End to End Delay
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2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) A. Simulation Analysis In this Simulation, OLSR protocol has been applied under different mobility models (Random Waypoint, ManhattanGrid, RPGM and Pursue) in FANETs. For all the simulations, same type of traffic and number of nodes (20) has been used. In this simulation, different speed groups (5, 10, 20, 30, 40 and 50) are used to check the performance of protocol under a fix simulation area 400X600. As shown in fig. 3, it is observed that OLSR with Pursue mobility model performs better in terms of packet delivery ratio. Pursue mobility models provide almost 100 percent packet delivery ratio (PDF) even at a high speed of nodes, whereas PDF of other mobility models vary from 90 to 99 percent. From fig. 4, it can be clearly observed that speed of node in the FANET does not affect the throughput of OLSR under Pursue mobility model, and which is better than the other three mobility models. Fig. 5 shows that, End to End delay of OLSR with pursue mobility models is low and it is almost the consistent, whereas the other mobility models (random waypoint, Manhattan grid and RPGM) have high End to End delay, Which leads to the poor performance of OLSR for FANET. V.
[7]
[8]
[9]
[10]
[11] [12] [13]
[14]
Jeff Boleng Tracy Camp Vanessa Davies,” A Survey of Mobility Models for Ad Hoc Network Research”, Dept. of Math. and Computer Sciences Colorado School of Mines, Golden, CO , 10th September, 2002. F. Bai, N. Sadagopan, and A. Helmy, “Important: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks,” in Proc. of IEEE INFOCOM 2003, (San Francisco, CA), March/April 2003. T.Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,” Wireless Communications & Mobile Computing (WCMC) Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483–502, 2002 A. Jardosh, E. M. Belding-Royer, K. C. Almeroth, and S. Suri, “Real world environment models for mobile ad hoc networks,” IEEE Journal on Special Areas in Communications -Special Issue on Wireless Ad hoc Networks, 2005. Fan Bai and Ahmed Helmy, “A Survey of mobility models in wireless adhoc network”, University of Southern California (USA),2004. ZhibinWu,Rutgres University, Available: http://www.winlab.rutgres.edu/~zhibinwu/html/network_simulator _2.html ,October,2007 Issariyakul, Teerawat, Hossain, Ekram, Introduction to Network Simulators[online] Available http://www.springer.com/engineering/signals/book/978-03871759-3,2009. Francisco J.Ros, OLSR, Available: http://masimum.inf.um.es/fjrm/development/um-olsr/, September, 2013.
CONCLUSION
In this research, OLSR routing protocol is applied and analyzed under different mobility model in FANETs in terms of packet delivery ratio, average throughput and end to end delay with varying speed of nodes. Simulation results show that performance of OLSR varies depending on the different node speed and mobility models. Under the pursue mobility model, OLSR performs better for FANETs, in comparison of Manhattan, Random Waypoint and RPGM. From the results, it can be clearly seen that performance of OLSR can be optimized by using the pursue mobility model in FANETs. REFERENCES [1] [2] [3]
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