Performance Evaluation of ACO Based on
Demand Routing Algorithm for Mobile Ad
Hoc Networks
Rajeshwar Singh
Department of Electronics and Communication Engineering, Gateway Institute of Engineering and Technology, Sonipat, Haryana-131001 (India)
Dharmendra K Singh
Department of Electronics and Communication Engineering, BIT Sindri, Dhanbad, Jharkhand (India)
Lalan Kumar
IT Centre, Central Institute of Mining and Fuel Research (CIMFR) Dhanbad, Jharkhand (INDIA)
Abstract
Routing in Mobile Ad Hoc Networks (MANETs) is complex problem due to the dynamic topology, limited process and storing capability, bandwidth constraints, high bit error rate and lack of the central control. The design of efficient routing protocols is a fundamental problem in MANETs. Researchers have proposed a number of routing protocols in literature. Ants based routing is gaining more popularity because of its adaptive and dynamic nature. A number of Swarm Intelligence (SI) based, more specially Ant Colony Optimization (ACO) based routing algorithms are proposed by researchers. Each one is based on different characteristics and properties. In this paper, we propose an Ant Based on Demand Routing Algorithm named ABDRA for routing in MANETs. We simulate the proposed algorithm using NS-2 and compare the result with traditional AODV routing protocol. The proposed ABDRA is multi-path routing algorithm and improves the packet delivery ratio. Also, we compare end to end delay for AODV and ABDRA. The proposed ABDRA performs better for Mobile Ad Hoc Networks routing.
Keywords: Swarm Intelligence (SI), Ant Colony Optimization (ACO), AODV, ABDRA, MANET.
1. INTRODUCTION
Mobile Ad Hoc Networks (MANETs) [1, 2] are built up of a collection of mobile nodes which have no fixed infrastructure. The nodes communicate through wireless network and there is no central control for the nodes in the network. Routing is the task of directing data packets from a source node i.e. transmitter to a given destination node i.e. receiver. This task is particularly complex due to the dynamic topology, limited process and storing capability, bandwidth constraints, high bit error rate and lack of the central control. It is very challenging for the researchers and engineers to develop and implement a routing algorithm to accomplish the task of routing in changing topology of Mobile Ad Hoc Networks. Ants routing resembles basic mechanisms from distributed Swarm Intelligence (SI) in biological systems and turns out to become an appealing solution when routing becomes a crucial problem in a complex network scenario, where traditional routing techniques either fail completely or at least face intractable complexity. Ants based routing is gaining more popularity because of its adaptive and dynamic nature [3,6,7].
The Multicast for Ad hoc Network with Swarm Intelligence (MANSI) protocol provides multicast support for ad hoc networks. Within a multicast group, each member launches a forward ant in order to find an existing forwarding node where it can be used to establish connectivity to the group with lower cost [16].
The Multicast for Ad hoc Network with hybrid Swarm Intelligence (MANHSI) utilizes small control packets equivalent to ants in the physical world. These packets, traveling like biological ants, deposit control information at nodes they visit similar to the way ants laying pheromone trails on the path [17].
Ants Hybrid Routing (ACO-AHR) Hybrid routing algorithm [18]. It introduces the service agents to reduce expense of network. It uses two artificial ant agents that is, forward ant agent which travels from source to destination and finds out information about quality of the path. Backward ant agent travels from destination to source and collects information about pheromone deposited. There are other types of agents which are called service agents (sagents). Improved Ant Colony Optimization algorithm for mobile ad hoc network (PACONET) is a reactive algorithm. It is a very dynamic algorithm which takes into account the mobility, route maintenance, Link Failure-Handling [19].
Position based on Ant colony routing algorithm for MANETS. (POSANT) is reactive i.e., route is established only when there are some data to send. It is position based routing means each node has information about its own position, position of its neighbors and position of its destination node [20]. Also, Swarm Intelligence: An emerging solution for Quality of Service (QoS) provisioning in Mobile Ad Hoc Networks is described in Singh et al.[21].
Probabilistic Ant Routing (PAR) is proposed for routing in MANETs [22]. Each node maintains a list of neighbors according to HELLO MESSAGE received. Forward ant agents (FANT) are probabilistic and explore the network to collect network traffic information. They are routed on normal priority queue. If route to destination is available as present node, the FANT is uni-casted otherwise it is broadcasted.
In this paper a new ant-based algorithm named Ants Based on Demand Routing Algorithm ABDRA is presented that combines many features of both AODV [4,5] and DSR [4,5]. This routing algorithm has features to establish multiple routes from source to destination and updates table as per local and global updates available. It reduces the route discovery time and hence able to manage the network topology change very effectively. Further, this paper is organized as follows: Section 2 describes the basic routing algorithm in detail; Section 3 describes the Experimental setup and environment considered. Section 4 provides performance metrics and result analysis with graphs. Section 5 presents conclusion of this paper.
2. DESCRIPTION OF ROUTING ALGORITHMS
Mobile Ad Hoc Network with V nodes connecting with E links are represented by weighted digraph as:
G = ( V, E )
In the proposed Ant Based on Demand Routing Algorithm ABDRA, two ants are used. One FANT, which is created at Source S and moves to destination D. The other is BANT, which is created at Destination and follows the part of FANT and updates the route table. The FANT available at node i, follows the path through node j by probability formulae:
, =
∑
,
,
− − −
= 0 otherwise.
Where, Ni is neighbor node set of node i.
ρ ( i, j ) is pheromone strength on link e ( i, j )
, =
− − −
, ⃪ − , + ∆, − − −
And∆, =
,
− − − −
where ∆ , ! "#!! $% &'(
α is tunable parameter and utilized to calculate allowed delay D ( i, j ) and q ϵ ( 0, 1 ) is pheromone evaporation parameter.
When FANT reaches to Destination D, it is destroyed and BANT is created and routed through the path of FANT. During this process, pheromone parameter ρ(i,j) is again updated by formulae:
,
⃪ − , + ∆
ᴧ, *, +
,
⃪ − , , #- − − − − − − − .
R is reverse route, which FANT passes from source to destination
∆
ᴧ, = / 012 3 4, 5 − − − − − − − − − 6
β is tunable parameter used to influence bandwidth B ( i, j ), min B ( i, j ) is minimum bandwidth of the link to maintained the desired quality of service.
Now we can define the probabilities P1 and P2, of two paths through mobile nodes i and j, from same source S to same destination D as:
P1= (S, i1, i2, i3,….., im,D) and
P2= (S, j1, j2, j3,….., jm,D)
Let us consider, intermediate node sets are
I = { im | 1≤ m ≤ |V|} and
J = { jn | 1≤ n ≤ |V|}
If I∩J = Ф then P1 and P2 are two similar routes.
Route Discovery - When node S sends a packet to destination node D. Node searches the destination route in the routing table. If it is not available in the routing table, it creates a FANT with source address and broadcasts to all adjacent nodes.
I. When an intermediate node i receives the FANT and checks destination address of FANT, if destination address is not same: (a) node i adds own address and time FANT arrived to i, (b) node i adds the source address as destination address for routing BANT, into routing table and computes pheromone value according to formulae shown above (c) the bandwidth is considered for the link between nodes. (d) Hop count is also updated. (e) node i broadcasts FANT to neighbor again, if did not receive the route of destination.
II. When node i receives duplicate FANT i.e with same sequence number and source address, the above steps are followed. If sequence number is less than or equal to max sequence number and route record of FANT includes address of present node, then the FANT is discarded. Otherwise node updates the max sequence number by new value and executes the steps shown in I.
III. When destination of FANT is same as that of i, route is discovered. FANT is discarded after retrieving relevant information from FANT, and BANT is sent on to the path followed by FANT. Again the pheromone table is updated as per BANT equation defined above. When BANT reaches to the source S all the pheromone tables are updated.
3. Experimental Setup
The NS-2.29 experimental setup is used for performance evaluation of the AODV and ABDRA routing protocols. It measures the ability of protocols to adapt to the dynamic network topology changes while continuing to successfully deliver data packets from source to their destination. In order to measure this ability, different scenarios are generated by varying the pause time of nodes. We use following scenario generation commands for generating scenario file for 50 nodes:
./setdest –v 1 –n 50 –p 20.0 –M 10.0 -t 1000 -x 1500 -y 300; ./setdest –v 1 –n 50 –p 50.0 –M 10.0 -t 1000 -x 1500 -y 300; ./setdest –v 1 –n 50 –p 100.0 –M 10.0 -t 1000 -x 1500 -y 300; ./setdest –v 1 –n 50 –p 300.0 –M 10.0 -t 1000 -x 1500 -y 300;
Similarly, for connection pattern generation we use, cbrgen.tcl file. By using command “ns cbrgen.tcl -type cbr -nn 50 -seed 1.0 -mc 16 –rate 4.0;” the connection pattern is generated.
The trace file is created by each run and is analyzed using a variety of scripts, particularly one called file *.tr that counts the number of successfully delivered packets and the length of the paths taken by the packets, as well as additional information about the internal functioning of each scripts executed. This trace file is further analyzed with AWK file and Microsoft Excel is used to produce the graphs.
In order to measure the performance of proposed Ant Based on Demand Routing Algorithm ABDRA effectively, we compare the performance of ABDRA with AODV for varying pause time. Nodes we have considered are 50 and they move within an area of 1500m x 300 m using Random Waypoint Model. The maximum speed of nodes we have considered in simulation is 10m/s. The channel capacity is 2 Mbps and transmission range of each node is taken as 250m. Traffic Type is 20 CBR (continuous bit rate) traffic sources each send 4 packets per second with a packet size of 64 bytes. Packet Delivery Fraction and End to End Delay is considered as evaluation parameter for this simulation. The simulation time was taken to be of 1000 seconds. Also, we have considered nodes with Omni-Antenna and Two Ray Ground Radio Propagation method. Simulation parameters are appended in Table-1.
SIMULATION PARAMETERS
Parameter Value
Simulator NS-2.29
Protocols studied AODV and ABDRA Simulation time 1000 sec Simulation area 1500 X 300 Transmission range 250 m Node movement model Random Way Point
Traffic type CBR (UDP)
Data payload 64 Bytes / packet Table: 1. Simulation Parameters
4. PERFORMANCE METRICS AND RESULT ANALYSIS
In this paper we have considered End to End Delay in milli second and Packet Delivery Fraction (PDF) in percentage for evaluation of AODV and ABDRA routing protocols.
4.1 End to End Delay:
Figure: 1.
4.2 Packet Delivery Fraction (PDF).
It is the ratio of the data packets delivered to the destination to those generated by the sources.
Packet Delivery Fraction (PDF) = Total Packets Delivered to destination / Total Packets Generated. Mathematically, it can be expressed as:
Where, P is the fraction of successfully delivered packets, C is the total number of flow or connections, f is the unique flow id serving as index, Rf is the count of packets received from flow f and N
transmitted to f. The Packet Delivery Fraction with varying pause time for AODV and ABDRA is calculated. The comparison between AODV and ABDRA is shown in Figure.2
Figure: 2. 0 20 40 60 80 100 120 140 20
End to End Delay Vs Pause Time
E n d to E n d D e la y (m s) 96.5 97 97.5 98 98.5 99 20
Packet Delivery Fraction Vs Pause Time
Pa ck e t D e li v e ry F ra cti o n ( % )
Figure: 1. End to End Delay versus Pause Time.
4.2 Packet Delivery Fraction (PDF).
It is the ratio of the data packets delivered to the destination to those generated by the sources.
Packet Delivery Fraction (PDF) = Total Packets Delivered to destination / Total Packets Generated. Mathematically, it can be expressed as:
∑
==
e f f fN
R
c
P
11
P is the fraction of successfully delivered packets, C is the total number of flow or connections, f is the is the count of packets received from flow f and Nf is the count of packets Packet Delivery Fraction with varying pause time for AODV and ABDRA is calculated. The comparison between AODV and ABDRA is shown in Figure.2
Figure: 2. Packet Delivery Fraction versus Pause Time.
50 100 300
Pause Time in Sec.
End to End Delay Vs Pause Time
AODV
ABDRA
50 100 300
Pause Time in Sec.
Packet Delivery Fraction Vs Pause Time
ABDRA
AODV
Packet Delivery Fraction (PDF) = Total Packets Delivered to destination / Total Packets Generated.
P is the fraction of successfully delivered packets, C is the total number of flow or connections, f is the is the count of packets Packet Delivery Fraction with varying pause time for AODV and ABDRA is calculated.
ABDRA
5. CONCLUSION
In this paper, we have evaluated the performance of ACO based, named as Ant Based-on-Demand Routing Algorithm ABDRA with traditional routing protocol AODV for Mobile Ad Hoc Networks using NS-2.29 event simulator.The proposed ABDRA decreases the average end-to-end delay and the times of congestion happening effectively by on demand creating the multiple routes of link disjoint paths. This is because the proposed algorithm uses local updating rule and global updating rule to update the pheromone of link and the probability route table. Also, selects route and balances traffic flows considering bandwidth and probability from routing table. Experimental results show that ABDRA performs better in both the performance matrices i.e. for Packet Delivery Fraction as well as End to End delay.
It has been found that the overall performance of proposed ACO based ABDRA routing protocol for performance matrices, Packet Delivery Fraction as well as End to End delay is better than that of traditional AODV routing protocol. In our experimental evaluation, we have taken up comparison of ABDRA and AODV protocols with varying pause time with fixed number of nodes to 50. Further, evaluation of the proposed ACO Based on Demand Routing Algorithm (ABDRA), need to be done by varying the number of mobile nodes.
ACKNOWLEDGEMENT
We are thankful to CMU Monarch Project Group for providing Wireless and Mobility Extensions to NS-2 Simulator.
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AUTHORSBIOGRAPHY
Rajeshwar Singhreceived his AMIE (India) degree from the Institution of Engineers, India, in year 1992, Master of Engineering (Digital Systems) from Motilal Nehru Regional Engineering College, Allahabad, U.P in year 2002. He has also received Master of Business Administration (MBA) in Information Technology from MD University, Rohtak, Haryana, in year 2009. He is presently working as Dean at Gateway Institute of Engineering and Technology, Sonipat, Haryana. He has rich experience of industry and teaching of UG and PG Courses. His more than 30 research papers are published in National and International Journals and conferences of repute. His area of interest is Swarm Intelligence Based Routing in Communication Systems and Embedded Systems Design.
Dr. Dharmendra K Singh is presently working as Head, Department of Electronics and Communication Engineering at BIT Sindri, Dhanbad, Jharkhand. He has more than 20 years of teaching experience at various levels. He has published more than 35 papers in national and international journals / conferences of repute. He has supervised a number of M.Tech. and Ph.D. research scholars, and presently there are five research scholars working under him. His current research interests include Swarm Intelligence based Optimization, intellectual information technology, Mobile Ad hoc Network Routing, Coding theory, cryptography, Photonic Crystal Fibers, e-Governance and Educational Planning. He is member and convener of various computerization programs of BIT Sindri, Vinoba Bhave University and Ranchi University, Jharkhand (India).
