Performance evaluation of EDCA and agent based congestion control routing in mobile adhoc network

Performance evaluation of EDCA and agent based congestion control routing in mobile adhoc network

Performance evaluation of EDCA and agent based congestion control routing in mobile adhoc network

N.VIDHYA,

M.E Communication systems, Department of electronics and communication,

Saranathan college of Engineering, Tiruchirapalli, India.

Vidhyasaro238@gmail. Com

1. RUNMOZHI,

   Associate professor,

   Department of electronics and communication, Saranathan college of Engineering, Tiruchirapalli,India.

   arunmozhi-ece@saranathan.ac.in

   Abstract-MANET is a system of autonomous mobile nodes that communicate over wireless links without any preinstalled infrastructure. In MANET when there is too much of data traffic at a node then the channel access in the network slows down and it leads to congestion. It degrades quality of service and also leads to delay and packet loss. In proposed system congestion in a network is controlled by mobile agent. The Network Simulator (NS-2) was used for simulation.

   Keywords: congestion; MANET; AODV; Routing.

   1. Introduction

      A mobile ad-hoc network (MANET) is a self-configuring infrastructure less network of mobile devices connected by wireless. Each device in a MANET is free to move independently in any direction, and will therefore change its links to other devices frequently. Each node should be capable of communicating with other nodes that are located within its transmission range. Each must forward traffic unrelated to its own use, and therefore be a router. In ad-hoc network nodes that are in the radio range of each other can directly communicate otherwise intermediate nodes are used to route the packet in between source and destination.

      A. Congestion in mobile ad-hoc networks

      In Ad-hoc networks, since there is no fixed infrastructure there are no separate network elements called routers and hence the mobile nodes themselves act as the routers.Congestion occurs when the resource demand exceeds the capacity of a network. During congestion, the network throughput decreases and packet drop will increases. In this work took congestion control in a network is achieved by using mobile agent.

   2. PROPOSED SYSTEM

      1. IEEE 802.11E

         IEEE 802.11e MAC standard was proposed as an enhancement to the legacy IEEE 802.11 DCF in order to support quality of service in WLAN. It introduces two new access methods Hybrid Coordination channel access (HCCA) and the Enhanced Distributed Coordination Function (EDCF), renamed in latest 802.11e draft to EDCA (Enhanced Distributed Channel Access). The IEEE 802.11e EDCA

         mechanism provides differentiated, distributed access to the wireless medium .Original IEEE 802.11 treats all packets as equals and not differentiating time sensitive traffic. EDCA classifies the packets into four different classes voice(VO), video (VI), best effort (BE) and background (BK) and assigns each of these traffic types to four Access Categories.

         EDCA defines four access categories (ACs) namely AC_VO for voice, AC_VI for video , AC_BE for best effort and AC_BK for background classes of traffic to provide priority among different traffic types. EDCA provides differentiated and distributed access to the wireless medium. Each AC achieves differentiated channel access by varying the amount of time, a node would sense the channel to be idle and the length of the contention window during a back off. Each frame from the higher layer carries its user Priority(UP).Eight User Priorities(UP) identical to IEEE 802.11D priority tags are defined which can be mapped to any four Access Categories. After receiving a frame ,the MAC layer maps it into one of the four ACs .Each AC is associated with one backoff entity and some AC specific parameters called the EDCA parameter set composed of Arbitrary Inter-Frame

         Space Number (AIFSN[AC]), minimum contention window (CWmin[AC]), and maximum contention window (CWmax[AC]).

         The Channel is idle for a contention period (CP) before a node can transmit data . If the channel is idle for the whole CP, the station can transmit immediately after the CP. So with multiple stations contending for one radio channel, the shorter the CP, the higher the chance is to access the wireless medium. The CP consists of Arbitration Inter-frame Space (AIFS) and a random backoff time. The AIFS is a distinct value for each AC. After the idle duration of AIFS, the contention entity generates a random backoff period for an additional deferral time before transmitting. Basically, the smaller AIFSN[AC], CWmin[AC], and max[AC], the shorter the channel access delay for the corresponding priority. However, the probability of collisions increases when operating with smaller CWmin[AC].

      2. Agent based congestion control routing

      In MANET obstruction occurs due to packet loss and it can be reduced by congestion control. In the proposed system the mobile agent based congestion control technique is

      used. The mobile agent moves through the network and it select a less loaded neighbor node as its next hop and update the routing table. The mobile agent estimates the queue length and the channel contention of each path. Then total congestion metric is applied to the routing protocol to select the minimum congested route in the network.

      Queue length estimation:

      Where is the queue length.

      is the arrival rate. is the service rate.

      Queue length distribution:

      The channel occupation due to MAC contention is

      Step (4): Then the Mobile agent1 calculates the TCM1 of that patp and similarly Mobile agent2 calculates the TCM2 of patp ,Mobile agent3 calculates the TCM3 of patpand Mobile agent4 calculates the TCM4 of path4.

      Step (5): Now the destination D sends the total congestion metrics TCM1, TCM2, TCM3 and TCM4 of the patp, patp,patp and path4 to the source.

      Step (6): TCM is set to a threshold value. The path with TCM less than the threshold value are the active path and the path with TCM greater than the threshold value are the non active path.

      Step (7): If the TCM is above the threshold then the traffic is distributed.

      .

   3. IMPLEMENTATION

      The wireless network scenario was generated using Tcl script and it was simulated using NS-2. The simulation was carried for 20,40,60,80,100 number of nodes, for the data rate 64,128,256,512 packets/sec and for the node mobility 10, 20, 30,40,50m/s. The simulation area is 1000m×1000m, the simulation time is 50sec and the mobility model is random way point. The simulation parameters are listed in Table1.The

      performance was evaluated by calculating performance

      Total Congestion Metric:

      The Total Congestion Metric (TCM) can be estimated from the obtained queue length and the channel contention.

      The agent based congestion routing Architecture can be explained from the following figure:

      Step (1): The source S checks the number of available one hop neighbours and clones the Mobile Agent (MA) to that Neighbours.

      metrics (i.e. packet delivery ratio, throughput, packet drop, routing over head and end to end delay)

      TABLE-1 SIMULATION PARAMETERS

      SIMULATION PARAMETER	VALUES
      Routing protocol	AODV
      Number of nodes	20,40,60,80,100
      Area	1000m×1000m
      Maximum speed	10,20,30,40,50 M/S
      Packet rate	64,128,256,512 packets/sec
      Mobility model	Random way point
      Simulation time	50 sec

      Step (2): The Mobile agent estimate the queue length and

      channel contention of patp, path 2, path 3,path 4 as given in the figure1. TCM (total congestion metric) is the sum of queue length and channel contention of each path.

      Step (3): The Mobile agent1 moves towards the destination D in a hop-by-hop manner in the patp and Mobile agent2 in patp, Mobile agent3 in patp and Mobile agent4 in path4 respectively.

      0.2

      A. Performance Metrics

      Throughput: It is defined as the average number of packets successfully received per unit time. This measurement used to know how fast packets can send through the network.

      Packet delivery fraction (PDF): It is defined as the ratio of number of data packets received to the number of data packets sent.

      End-to-end delay: It is defined as the total time taken by each packet to reach the destination. Average end-to-end delay is the ratio of total time delay for all the received packets to the number of packets received.

      Routing Overhead: It is defined as the total number of routing packets transmitted during the simulation.

      Packet drop: It is defined as the failure of one or more transmitted packets to arrive at their destination.

   4. Simulation results and analysis

0.15

0.1

0.05

0

1

0.98

0.96

0.94

0.92

0.9

10 20 30 40 50

Fig 2.delay Vs mobility

EDCA

congestion controlled EDCA

EDCA

1. Performance of EDCA at Various mobility:

   0.88 congestion

   The performances of EDCA are measured in terms of packet delivery ratio, throughput and average end-end delay , packet drop and routing overhead.

   To analyze the performance of EDCA, the number of nodes is 20, data rate is 64 packets/sec and mobility is varied

   0.86

   0.84

   10 20 30 40 50

   controlled EDCA

   as 10,20,30,40,50 m/s. Figures 1, 2 ,3,4 and 5 shows the performance of EDCA in terms of various mobility.

   Fig 3.packet delivery ratio Vs mobility

   60

   50

   40 EDCA

   30

   20

   congestion

   0.12

   0.1

   0.08

   0.06

   0.04

   0.02

   0

   EDCA

   congestion controlled EDCA

   10

   0

   10 20 30 40 50

   controlled EDCA

   10 20 30 40 50

   Fig 4.packet drop Vs mobility

   Fig 1.throughput Vs mobility

   2

   1.5

   1

   EDCA

   1

   0.98

   0.96

   0.94

   EDCA

   0.5 congestion

   controlled

   0.92

   0.9

   congestion

   0

   10 20 30 40 50

   EDCA

   0.88

   20 40 60 80 100

   controlled EDCA

   Fig 5.routing over head Vs mobility

2. Performance of EDCA at various number of nodes:

To analyze the performance of EDCA, the mobility is 10, data rate is 64 packets/sec and number of nodes is varied as 20,40,60,80,100. Figures 6,7,8,9 and 10 shows the performance of EDCA in terms of various numbers of node.

Fig 8. packet delivery ratio Vs number of nodes

60

50

40 EDCA

30

20

0.1

0.08

0.06

0.04

EDCA

10

0

20 40 60 80 100

congestion

controlled EDCA

0.02

0

20 40 60 80 100

congestion controlled EDCA

Fig 6. throughput Vs number of nodes

Fig 9. packet drop Vs number of nodes

0.06

0.05

0.04 EDCA

0.03

0.02

2.5

2

1.5

1

EDCA

0.01

0

20 40 60 80 100

congestion

controlled EDCA

0.5

0

20 40 60 80 100

congestion controlled EDCA

Fig 7. packet delay Vs number of nodes

fig 10. routing over head Vs number of nodes

1. Performance of EDCA at various datarate:

   To analyze the performance of EDCA, the mobility is 10, number of nodes is 20 and datarate is varied as 64,128,256,512 and 1024. Figures 11,12,13,14 and 15

   shows the performance of EDCA in terms of various datarate.

   100

   80

   1

   0.8

   0.6

   0.4

   0.2

   0

   EDCA

   congestion controlled EDCA

   60

   40

   20

   0

   64 128 256 512

   Fig 11. throughput Vs datarate

   EDCA

   congestion controlled EDCA

   1.6

   1.4

   1.2

   1

   0.8

   0.6

   64 128 256 512

   Fig 14. packet drop Vs datarate

   EDCA

   0.06

   0.4 congestion

   0.05

   0.04

   EDCA

   0.2

   0

   64 128 256 512

   controlled EDCA

   0.03

   0.02

   0.01

   0

   64 128 256 512

   congestion controlled EDCA

   Fig 15. routing over head Vs datarate

   V.CONCLUSION

   From the above analysis the performance of congestion

   1.2

   1

   Fig 12. packet delay Vs data rate

   controlled EDCA is evauated. In this technique, the Mobile agent estimates the queue length and channel contention of each path. Based on the TCM value the traffic is distributed.

   Thus the proposed technique attains high delivery ratio and throughput with reduced delay when compared with the EDCA.

   0.8 EDCA

   0.6

   0.4

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   0.2

   0

   64 128 256 512

   congestion controlled EDCA

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