Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 1, February 2014

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Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 1, February 2014
T...
Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 1, February 2014
N...
Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 1, February 2014
N...
Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 1, February 2014
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Performance analysis of DCF protocol for different node density and traffic load conditions using GloMoSim

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Performance analysis of DCF protocol for different node density and traffic load conditions using GloMoSim

  1. 1. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 1, February 2014 Performance analysis of DCF protocol for different node density and traffic load conditions using GloMoSim By Ankit Rajpal, P.K. Hazra, Nikhil Kumar Rajput Department of Computer Science, Deen Dayal Upadhyaya College, University of Delhi, Delhi, India Department of Computer Science, University of Delhi, Delhi, India Department of Computer Science, Ramanujan College, University of Delhi, Delhi, India ankit30sep@gmail.com, pradyot.k.hazra@gmail.com, nikhilrajput@gmail.com ABSTRACT This paper presents a study of IEEE 802.11 DCF MAC layer access mechanism, using GloMoSim (GLobal MObile information system SIMulator). A performance evaluation of the IEEE 802.11 DCF with the help of several simulation scenarios in GloMoSim has been done. Variation of throughput and end to end delay with respect to number of stations has been carried out. Packet delivery ratio with changing mobility speed and number of CBR pairs has also been studied. The changing pattern of aggregated throughput and average end to end delay with number of CBR pairs has also been studied. Besides some common basic analysis, we have considered CBR pairs for the performance analysis of DCF protocol. Keywords DCF; WLAN; Simulation; GloMoSim; CBR 1. INTRODUCTION IEEE 802.11 is a standard for wireless LANs which specifies standards for both medium access control and physical layer [1, 2]. Distributed Coordination Function (DCF) and Point Coordination Function (PCF) are the two mechanisms for medium access of IEEE 802.11. The DCF access method is used for Ad-Hoc wireless networks while the PCF access method is used for infrastructure wireless networks. Performance analysis of both of these mechanisms has been carried out by several researchers. Bianchi [3, 4] provided a brief analysis of DCF protocol. Chatzimisios et. al. [5] studied the DCF protocol in presence of transmission errors. Zeng et al. [6] analyzed DCF in imperfect channels. Most of the research done on DCF in literature has focused on CBR (Constant Bit Rate) traffic. In this paper, we provide a brief analysis of DCF protocol by carrying out simulations in GloMoSim [7] to observe the variation of throughput with respect to packet size and CBR pairs, variation of packet delivery ratio and average-end-to-end delay taking into consideration the CBR pairs. The mode of access is RTS/CTS for all simulation scenarios. 2. DISTRIBUTED COORDINATION FUNCTION (DCF) IEEE 802.11 DCF protocol is a Carrier Sense Multiple Access (CSMA) based protocol. In DCF, to monitor the channel activity a station uses a new packet. The station transmits if the channel is idle for a period of time equal to a distributed inter-frame space (DIFS). If the channel is busy during the DIFS or immediately after it, the station continues to monitor the channel till it finds it idle for a DIFS. In this case, the station generates a random back-off interval before transmitting. This is the Collision Avoidance feature of the DCF to minimize the collision probability of packets being transmitted by other stations. Further to avoid channel capture, a station must wait for a random back-off time between two consecutive packet transmissions, even if the medium is sensed idle. DCF uses a discrete-time back-off scale. The time period following an idle DIFS is slotted, and a station is permitted to transmit only at the starting point of each time slot. The time slot size,  , is set equal to the time required at any station to detect the transmission of a packet from some other station. DCF employs an exponential back-off scheme. For each packet transmission, the back-off time is chosen uniformly in the range (0, w-1). Here w is the contention window which depends on the number of transmissions failed for a packet. For first attempt of transmission, w is set equal to minimum contention window CWmin. After each unsuccessful transmission, w is doubled to a maximum value CWmax = 2mCWmin. As long as the channel is sensed idle, the back-off time counter is decremented when a transmission is detected on the channel, and it is reactivated when the channel is sensed idle again for more than a DIFS. The station transmits when the back-off time becomes zero. 3. SIMULATION AND ANALYSIS 3.1 Variation of throughput with respect to the number of stations In the config.in file of GloMoSim, the following parameters are used as shown in Table I. 1
  2. 2. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 1, February 2014 TABLE I MODE OF ACCESS MEDIUM DCF using RTS/CTS scheme SIMULATION-TIME 100M NUMBER-OF-NODES VARIED FROM 10 TO 100 IN STEPS OF 10 NODE-PLACEMENT UNIFORM MOBILITY NONE MAC-PROTOCOL 802.11 NETWORK-PROTOCOL IP ROUTING-PROTOCOL BELLMANFORD The average end-to-end delay is the total delay from the time application at the sender side generates a packet to the time the application at the receiver side receives it. It includes all types of delays that occur during transmission like transmission delay, propagation delay and any queuing delays. As the number of stations contending for the medium access increase, these delays also increase which account for the increase in average end-to-end delay with an increase in the number of nodes as depicted above. 3.3 Variation of throughput with respect to packet size In APP.CONF FILE, the packet size was varied for the CBR source as: CBR 0 1 250 24 1S 0S 150S Initially the WLAN aggregated throughput increases as more and more number of nodes start transmitting, it reaches a maximum level and stays there for further increase in the number of nodes i.e. becomes independent of the number of nodes. This behaviour is in compliance with the ideal results expected in the RTS/CTS case. CBR 0 1 250 32 1S 0S 150S 3.2 Variation of average end-to-end delay with respect to the number of stations CBR 0 1 250 512 1S 0S 150S In the config.in file of GloMoSim, the following parameters are used as shown in Table II. TABLE II MODE OF ACCESS MEDIUM DCF using RTS/CTS scheme SIMULATION-TIME 100M NUMBER-OF-NODES IP ROUTING-PROTOCOL CBR 0 1 250 2048 1S 0S 150S For each specific packet length, the simulation was repeated several times and the average throughput was recorded as shown later. We also noticed that in GloMoSim, the minimum allowable packet size is 24 bytes while the maximum size is 2048 bytes. In the config.in file of GloMoSim, the following parameters are used as shown in Table III. 802.11 NETWORK-PROTOCOL CBR 0 1 250 1024 1S 0S 150S NONE MAC-PROTOCOL CBR 0 1 250 256 1S 0S 150S UNIFORM MOBILITY CBR 0 1 250 128 1S 0S 150S VARIED FROM 10 TO 40 IN STEPS OF 10 NODE-PLACEMENT CBR 0 1 250 64 1S 0S 150S BELLMANFORD TABLE III MODE OF MEDIUM ACCESS DCF using RTS/CTS scheme SIMULATION-TIME 100M 2
  3. 3. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 1, February 2014 NUMBER-OF-NODES 30 NODE-PLACEMENT RANDOM MOBILITY RANDOM-WAYPOINT MOBILITY-WP-PAUSE 3S MOBILITY-WP-MIN-SPEED 0 MOBILITY-WP-MAX-SPEED 10 MAC-PROTOCOL 802.11 NETWORK-PROTOCOL IP ROUTING-PROTOCOL DSR Throughput of a node increases as the packet size increases because of decrease in the RTS/CTS overhead. 3.4 Variation of packet delivery ratio with respect to mobility speed In the config.in file of GloMoSim, the following parameters are used as shown in Table IV. TABLE IV The packet delivery ratio here can be defined as the ratio of total number of packets received by CBR client to the total number of packets sent by the corresponding CBR server. For the above simulation, the CBR traffic has been routed using the DSR protocol and random waypoint mobility model has been used where pause time is 3 seconds. From the above figure, we observe that there is a sharp decrease in the number of data packets delivered to the destination (indicated by the packet delivery ratio) as the mobility of nodes comprising the Wireless LAN increases. The shape of the graph is such because the data packet delivery ratio for the DSR protocol decreases as node speed increases since it is more difficult for the protocol to find a stable route to the destination (the random waypoint model produces a dynamic network topology at a high speed and short pause time). At low speeds, paths may exist for longer amounts of time, allowing CBR packets to be transmitted in their entirety. At high speeds these connections exist for shorter periods favoring shorter transmissions. The faster nodes move, the more frequently link breaks occur. 3.5 Variation of packet delivery ratio with respect to number of CBR pairs MODE OF MEDIUM ACCESS DCF using RTS/CTS scheme In the config.in file of GloMoSim, the following parameters are used as shown in Table V. SIMULATION-TIME 100M NUMBER-OF-NODES 30 In APP.CONF FILE, we start by taking 2 CBR pairs (connections) by specifying: NODE-PLACEMENT RANDOM MOBILITY RANDOM-WAYPOINT MOBILITY-WP-PAUSE 3S MOBILITY-WP-MIN-SPEED 0 MOBILITY-WP-MAX-SPEED 5 TO 20 IN STEPS OF 5 MAC-PROTOCOL 802.11 MODE OF MEDIUM ACCESS DCF using RTS/CTS scheme NETWORK-PROTOCOL IP SIMULATION-TIME 100M ROUTING-PROTOCOL DSR NUMBER-OF-NODES 30 CBR 0 1 250 512 1S 0S 150S CBR 2 3 250 512 1S 0S 150S And keep on increasing the number of connections. TABLE V 3
  4. 4. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 1, February 2014 NODE-PLACEMENT RANDOM MOBILITY RANDOM-WAYPOINT MOBILITY-WP-PAUSE 3S MOBILITY-WP-MIN-SPEED 0 MOBILITY-WP-MAX-SPEED 10 MAC-PROTOCOL 802.11 NETWORK-PROTOCOL IP ROUTING-PROTOCOL DSR The aggregated WLAN throughput increases as more and more stations start transmitting; this is so because of the use of RTS/CTS medium access mechanism. As more and more senders inject their packets onto the WLAN, more bits are transmitted per second, leading to increase in throughput. 3.7 Variation of average end-to-end delay with respect to number of CBR pairs Using the same settings of parameters in part E, we have obtained the following results. The number of CBR pairs is truly representative of the actual load on the network. Thus we studied the variation of packet delivery ratio w.r.t. the offered load (Number of CBR pairs). The above graph shows that when the number of connections (number of senders or number of CBR pairs) is increased, the packet delivery ratio decreases. This is so because, as the number of senders increase, the contention for medium access also increases and so does the network congestion, the combined effect is to have fewer successful transmissions. Despite using RTS/CTS collisions may occur in this scenario because of the hidden terminal problem which arises when nodes go out of range of each other as a result of random node placement and random way-point mobility model. 3.6 Variation of aggregated throughput with respect to number of CBR pairs Using the same settings of parameters in part E, we have obtained the following results. The average end-to-end delay increases as the number of CBR connections (network load) is increased. This is so because IEEE 802.11 uses binary exponential back-off scheme. The MAC layer delay increases quickly for packet retries. 4. CONCLUSION We studied the DCF protocol (specifically the RTS/CTS medium access mechanism) using simulations in GloMoSim. We can conclude that in the absence of hidden terminals, for the RTS/CTS mechanism, the aggregate throughput of the WLAN initially increases as the number of nodes is increased. Later, Throughput attains a maximum value and becomes independent of any further increase in the number of stations in the network. The average endto-end delay increases monotonically for an increase in the number of nodes for the RTS/CTS access method. As we keep on increasing the packet size, the throughput of WLAN increases on account of decreased RTS/CTS overhead. With an increase in the number of CBR connections (number of active nodes) in the network, the packet delivery ratio shows a gradual decrease as a result of increased channel contention and network congestion. An increasing number of active nodes in the network cause the throughput to increase 4
  5. 5. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 1, February 2014 monotonically. The curve for average end-to-end delay shows a rapid rise with the increase in the number of active connections due to heightened media access delay. 5. References B. P. Crow, I. Widjaja, J. G. Kim, and P. T. Sakai, "IEEE 802.11 wireless local area networks", IEEE Commun. Mag., pp.116 -126 1997 [2] H. AhleHagh, WR. Michalson and D. Finkel, "Statistical Characteristics of Wireless Network Traffic and Its Impact on Ad Hoc Network Performance," In Proceedings of the 2003 Applied Telecommunication Symposium, 2003. [3] G. Bianchi, "Performance analysis of the IEEE 802.11 distributed coordination function", IEEE J. Sel. Areas Commun., vol. 18, no. 3, pp.535 -547 2000 . [1] G. Bianchi and I. Tinnirello "Remarks on IEEE 802.11 DCF performance analysis", IEEE Commun. Lett., vol. 9, no. 8, pp.765 -767 2005 [5] P. Chatzimisios, A. C. Boucouvalas, and V. Vitsas, "Performance analysis of IEEE 802.11 DCF in presence of transmission errors," in Proc. of IEEE ICC, June 2004, pp. 38543858. [6] Y. Zheng , K. Lu and D. W. Fang "Performance analysis of IEEE 802.11 DCF in imperfect channels", IEEE Trans. Veh. Technol., vol. 55, no. 5, pp.1648 -1656 2006 [7] L. Bajaj, M. Takai, R. Ahuja, K. Tang, R. Bagrodia, and M. Gerla, GlomoSim: A scalable network simulation environment, 1997 :Univ. California [4] 5

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