Average Packet Delay Versus Cbr

Published Date: 02 Nov 2017

Accordingly, with the help of three of the parameters & four performance metrics I have generated 12 graphs, which in turn will evaluate the AODV protocol for QoS as well as Non-QoS. The AODV protocol with QoS provisions will be henceforth mentioned as QAODV.

It is clear for the figure 8.1 that for data rates above 600 kbps, the average delay suffered by packets is very less for QAODV in comparison with AODV. For low data rates delay suffered is approximately similar for both AODV and QAODV. The reason behind better performance of AODV is that it blocks the packet at source itself as soon as QoS criteria of path is lost which results in less contention in common intermediate sub-paths of different flows.

Figure 8.1. Average Packet Delay versus CBR

It is clear for the figure 8.2 that for each Pause time, the average packet delay suffered by QAODV is approximately 40-60 ms less than that suffered by AODV. For both AODV and QAODV, minimum delay is achieved when pause time of the nodes is 6 seconds.

Figure 8.2. Average Packet Delay versus Pause Time

It is clear for the figure 8.3 that for each value of speed, the average packet delay suffered by QAODV is very less than that suffered by AODV. For both AODV and QAODV, minimum delay is achieved when moving speed of the nodes is 4 m/s.

Figure 8.3. Average Packet Delay versus Speed of Nodes

As shown in Figure 8.4, the overhead of using QAODV is higher than the overhead of AODV at each data rate. To provide QoS to flows, QAODV uses special signaling packets/ information elements like QOS-LOST and QoS Bandwidth Extension, which results in high overhead of using it. The overhead values of both AODV and QAODV decrease with the increase in traffic data rate. It is difficult to explain the reason behind huge increase in the overhead value of QAODV when traffic data rate is 1200kbps.

Figure 8.4 : NOL versus CBR

The overhead of using QAODV is higher than the overhead of AODV at each pause time value, as can be seen from Figure 8.5. To provide QoS to flows, QAODV uses special signaling packets/ information elements like QOS-LOST and QoS Bandwidth Extension which results in high overhead of using it. The overhead values of AODV are approximately same at different pause time values. It is difficult to explain the reason behind huge increase in the overhead value of QODV when pause time is set to 12 seconds.

Figure 8.5 : NOL versus Pause Time

Figure 8.6 shows relationship NOL versus Speed of Nodes The overhead of using QAODV is higher than the overhead of AODV at each moving speed value. To provide QoS to flows, QAODV uses special signaling packets/ information elements like QOS-LOST and QoS Bandwidth Extension which results in high overhead of using it. The overhead values of AODV are approximately same at different speed values.

Figure 8.6 : NOL versus Speed of Nodes

At every data rate value, the PDR obtained by AODV is higher than that obtained by QAODV, as can be seen from Figure 8.7. The PDR value for AODV remains approximately same with increase in traffic data rate whereas the PDR value of QAODV decreases with the increase in traffic data rate. When the QoS (bandwidth) demand is high it is difficult to find QoS satisfying path for the flows. Therefore QAODV blocks the packets at source itself which results in decrease in the PDR value with increase in the data rate.

Figure 8.7. Packet Delivery Ratio versus CBR

Here, as shown in Figure 8.8, the data rate is set to 2000kbs and PDR value of QAODV is less than that of AODV at every pause time value.

Figure 8.8. Packet Delivery Ratio versus Pause Time

Whereas, for the data rate set to 2000kbs, as can be observed from Figure 8.9, PDR value of QAODV is less than that of AODV at every speed value.

Figure 8.9. Packet Delivery Ratio versus Speed of Nodes

At low data rates throughput achieved by QAODV is approximately similar to that achieved by AODV, as can be seen from Figure 8.10. When the QoS (bandwidth) demand is high it is difficult to find QoS satisfying path for the flows. Therefore QAODV blocks the packets at source itself with results in decrease in the throughput at the data rates higher than 1200 kbps.

Figure 8.10 : Throughput versus CBR

Here, as shown in Figure 8.11, the data rate is set to 2000kbs and QAODV’s throughput is less than AODV’s throughput. For both AODV and QAODV, Throughput achieved is highest when pause time is 4 seconds.

Figure 8.11 : Throughput versus Pause Time

Figure 8.12 : Throughput versus Speed of Nodes

As shown in Figure 8.12 above, the data rate is set to 2000kbs and QAODV’s throughput is less than AODV’s throughput. For both AODV and QAODV, Throughput achieved is highest when speed is 4 m/s. There is sudden decrease in throughput for both AODV and QAODV when speed is 8 m/s. Throughput achieved by AODV and QAODV does not vary much for the speed values greater than 12m/s.

8.2 Conclusion

In this thesis, I presented the QoS (Quality of Service) enabled AODV protocol. Firstly, I have simulated the basic protocol using NS2. Then using Gnuplot, the twelve graphs are generated with three varying scenarios for simulation used are 1) Speed of Nodes, 2) Traffic Rate, 3) Pause Time or Mobility & the performance metrics used are 1) PDR, 2) NOL, 3) Average packet delay, 4) Throughput. Then, the QoS of basic protocol is improved & again graphs are generated. And, ultimately the comparison of the Non-QoS and QoS-enabled protocol is carried out. The result shows the improvement in routing of data from source to destination.

By observing the graphs generated, following points can be concluded :

1) Average Packet Delay is reduced in QAODV whereas it is more in basic AODV protocol.

2) As I have made use of Hello Messages to read the bandwidth, the Network Overhead Load is increased to some extend in QAODV as compared to AODV.

3) Average throughput and Packet Delivery Ratio of QAODV are moderately same as AODV Protocol.

Reduced Average Packet Delay in case of QAODV indicate that this approach is suitable for modern and futuristic networks. Whenever streaming of multimedia based data such as video, audio and text is performed, traffic will be more and network becomes congested. It is observed that network congestion is the dominant reason for packet loss, longer delay and delay jitter in streaming video. The primary goal of a protocol is to increase the overall utility of the network by granting priority to higher-value or more performance-sensitive flows. QAODV protocol is found to cope up with this situation better as compared to AODV protocols although there is marginal increase in Network Overhead Load with Average throughput and Packet Delivery Ratio of QAODV are almost same.

Achievement of reduced Packet Delay of this new QAODV is very significant. This is because, wireless networks of future will need such approach, which will reduce delay in transmission. This reduced delay will transpire to very important parameter for networks handling real time traffic like video calling.

According to a survey by Cisco, mobile data in 2010 was triple the volume of the entire global Internet traffic in 2000. The growth rate in the previous year was 159%, which is 10% higher than anticipated in 2009. This rapid growth in mobile data is forecast to continue for the next five years with an average annual growth of 92%. There are several reasons why mobile traffic has grown so quickly. Firstly, mobile video, which requires high bit rates, is considered to lead to the increase of mobile traffic. It is reported that mobile video reached as high as 49.8% of total mobile traffic in 2010 and will account for two thirds of mobile traffic by 2015. Moreover, Internet gaming, which consumes, on average, 63 PB per month in 2009, also results in a growth in mobile traffic and it is expected to achieve an annual growth of 37% in the coming five years. Last but not the least, Voice over IP (VoIP) which includes phone-based VoIP services direct from or transported by a third party to a service provider, and software-based internet VoIP such as Skype, leads to the expansion of mobile traffic. Many of those applications described above are real-time applications which demand certain guarantees for performance metrics like Average Packet Delay for acceptable operation. Hence achievement of reduced Average Packet Delay of this new QAODV is very crucial for Ad hoc networks.

8.3 Application Areas

The Ad hoc wireless networks offer unique benefits and versatility for

certain environments and certain applications. No preexisting fixed infrastructure, including base stations, being prerequisite, they can be created and used "anytime, anywhere". Second, such networks could be intrinsically fault-resilient, for they do not operate under the limitations of a fixed topology. Indeed, since all nodes are allowed to be mobile, the composition of such networks is necessarily time-varying. Addition and deletion of nodes occur only by interactions with other nodes; no other agency is involved. Such perceived advantages elicited immediate interest in the early days among military, police, and rescue agencies in the use of such networks, especially under disorganized or hostile environments, including isolated scenes of natural disaster and armed conflict. In recent days, home or small-office networking and collaborative computing with laptop computers in a small area (e.g., a conference or classroom, single building, convention center) have emerged as other major areas of potential application.

8.4 Future Scope

At the dawn of the 21st century, computational and data management infrastructure based on supercomputers has become a first-class tool for science and engineering research. So far, the performance analysis has been targeted to NS-2 as simulation and analysis tool for my research. However, I know significance of Computational Science in engineering research as I have done MS, an interdisciplinary graduate program, in Computational Science & Engineering (CSE) from North Carolina A&T State University in United States of America with GPA 4.0.

Some of the most difficult problems in science are being addressed by the integration of computation and science -- computational science. In, current decade, computational science promises to enable fundamental advances in diverse fields of knowledge by means of data analysis and visualization. Hence, I am quite hopeful that, wireless network research will be revolutionized due to use of supercomputers. It must be noted that, current research in wireless networks is based on its two dimensional topology. Massive computational data processing, storage and visualization (especially 3-D visualization) capabilities will generate a sort of information ecosystem that will provide a new level of understanding and deeper insight for evolution of wireless Ad hoc networks.

I would like to use the deeper insight thus generated for extending my research in realization of nano-scale Ad hoc networks. Recently occurred developments in nano-scale electronics allow functioning of current wireless technologies in nano-scale environments. Carbon nanotubes can be used for the realization of a nano-scale communication. Hence, the realization of nano-scale ad hoc networks becomes one of the most challenging subjects for such applications. Carbon nanotube-based nano-scale Ad hoc Networks (CANETs) can be perceived as the down-scaled version of traditional wireless ad hoc networks without downgrading its main functionalities. Particularly, I would like to work in the field of nano-sensor networks as one of the early applications of nanotechnology is in the field of nano-sensors. Moreover, a nano-sensor is not necessarily a device merely reduced in size to a few nanometers, but a device that makes use of the unique properties of and nano particles to detect and measure new types of events in the nano-scale.