How to Implement Network Capacity Improvement in NS2

To implement a network capacity improvement using NS2 (Network Simulator 2), we can leverage several methods like congestion control, adaptive routing, load balancing, frequency reuse, and maximizing transmission power, between others. The significant aim of improving the network capacity that to enhance the network’s ability to manage increased traffic loads without experiencing packet loss or excessive delay. In this model, we provide simple technique for enhancing the network capacity using NS2:

Step-by-Step Implementation:

  1. Optimize Routing Protocols

One way to enhance the network capacity is by improving the routing protocol. We can execute the adaptive routing algorithms, which choose the paths including the least congestion, so spreading traffic more evenly and preventing bottlenecks.

For example, using AODV (Ad hoc On-Demand Distance Vector) routing or DSR (Dynamic Source Routing) protocols can support balance the load over several routes.

Example of AODV in TCL script:

# Use AODV routing protocol

set val(rp) AODV

We can fine-tune the AODV protocol metrics to minimise the routing overhead or append the congestion-aware routing mechanisms.

  1. Implement Load Balancing

Load balancing delivers traffic evenly over numerous paths or access points to prevent the congestion in particular areas of the network. This method can significantly improve the network capacity.

Example: Dynamic Load Balancing

We can modify the transmission queue size or assign traffic to various routes according to their present load.

# Set interface queue size

$ns_ queue-limit $node_(0) 50 ;# Increase queue size to handle more packets

Otherwise, execute the algorithms, which observe network congestion and actively shift traffic away from the congested nodes.

proc dynamic_load_balancing {node congestion_threshold} {

set load [get_queue_length $node]

if { $load > $congestion_threshold } {

# Change route or reduce transmission rate

change_route $node

puts “Node $node experiencing congestion, changing route.”

}

}

# Example usage

dynamic_load_balancing $node_(1) 40 ;# Apply load balancing if queue length exceeds 40

  1. Implement Frequency Reuse and Channel Assignment

In wireless networks, frequency reuse can maximize capacity by permitting several nodes to transfer on the similar frequency however including spatial separation to increase interference. We can set up the channel allocation and reuse to enhance capacity.

# Define wireless channels for frequency reuse

set chan1 [new Channel/WirelessChannel]

set chan2 [new Channel/WirelessChannel]

# Assign channels to nodes

$node_(1) set channel_ $chan1

$node_(2) set channel_ $chan2

By enhancing channel assignment and reuse strategies, we can develop the network’s spectral efficiency, so the capacity is increasing.

  1. Improve MAC Layer Protocols

Altering the MAC layer protocol, like changing the contention window size or transmission backoff time that can be minimized packet collisions and improve the network capacity.

We can execute the changes to the 802.11 MAC protocol or use other contention-based protocols such as TDMA (Time Division Multiple Access) to improve capacity.

# Example of adjusting contention window for 802.11 MAC

$node_(1) set CWMin_ 15 ;# Set minimum contention window size

$node_(1) set CWMax_ 1023 ;# Set maximum contention window size

It supports to minimise the packet collisions, especially in the dense network environment. Thus enhancing the throughput and capacity.

  1. Implement Congestion Control Mechanisms

Congestion control is critical for network capacity improvement. Executing the congestion avoidance methods, like RED (Random Early Detection), which can be avoided the packet loss and decrease delays within high-load scenarios.

Example of RED (Random Early Detection):

# Enable RED queue management for congestion control

set redQueue [new Queue/RED]

$ns_ queue-limit $node_(1) 50 ;# Set queue limit for RED

$redQueue set thresh_ 10 ;# RED threshold

$redQueue set maxthresh_ 30

$node_(1) attach $redQueue

We can use the RED or other Active Queue Management (AQM) methods can support maintain optimal throughput by controlling packet drop rates during the network approaches congestion.

  1. Increase Transmission Power

Using the increasing transmission power, nodes can communicate across the longer distances that may minimize the number of hops among the nodes. It reduces the congestion in intermediate nodes, enhancing overall network capacity.

# Increase transmission power for a node

$node_(1) set transmit_power_ 0.8 ;# Increase transmission power to 0.8 watts

But, we must balance this method wisely to prevent the interference among the nodes that would reduce network performance.

  1. Adaptive Rate Control

Also, we can execute adaptive rate control that alters the data transmission rate depends on the network conditions. It make certain that the network can manage differing levels of traffic without becoming overloaded.

For instance, we use TCP Vegas or TCP NewReno to adaptively handle the congestion window and then prevent overloading the network.

# Enable TCP NewReno for better congestion control

Agent/TCP set tcp_reno_ 1

Agent/TCP set windowSize_ 32 ;# Set TCP window size

It permits the network to actively modify to differing the conditions and enhances the network’s ability to manage the increased traffic, boosting capacity.

  1. Node Placement and Density Optimization

Tactical node placement and modifying the node density can enhance the network’s overall capacity. In wireless networks, setting the nodes more evenly or maximising the number of access points can lead to enhanced spatial reuse and better network throughput.

For instance, a grid-based or circular node placement approach could deliver the better coverage and decrease interference that increases network capacity.

# Grid-based node placement

for {set i 0} {$i < $num_nodes} {incr i} {

set X_ [expr ($i % 10) * 100]

set Y_ [expr int($i / 10) * 100]

$node_($i) set X_ $X_

$node_($i) set Y_ $Y_

}

  1. Use Multipath Routing

Multipath routing can be utilised to enhance the network capacity by distributing traffic over various routes. It supports to prevent the congestion and maximize the use of obtainable resources.

Example: Implementing Multipath Routing

In the TCL script, we can configure a multipath routing algorithm like AOMDV (Ad hoc On-Demand Multipath Distance Vector):

# Use AOMDV routing protocol for multipath routing

set val(rp) AOMDV

Multipath routing allows more effective use of the network’s resources and decreases congestion, leading to enhanced capacity.

  1. Analyse and Monitor Network Capacity

To estimate the efficiency of these approaches, observe the network performance by inspecting throughput, latency, and packet loss. We can change the trace file to record these metrics:

# Trace file for network performance analysis

set tracefile [open “capacity_trace.tr” w]

$ns_ trace-all $tracefile

We can use the tools such as AWK or Perl scripts to estimate the trace file and compute the network’s throughput that is an important indicator of its capacity.

Example TCL Script for Network Capacity Improvement

# Set up simulation

set ns [new Simulator]

# Define network parameters

set val(chan) Channel/WirelessChannel

set val(prop) Propagation/TwoRayGround

set val(netif) Phy/WirelessPhy

set val(mac) Mac/802_11

set val(ifq) Queue/DropTail/PriQueue

set val(ll) LL

set val(ant) Antenna/OmniAntenna

set val(x) 1000

set val(y) 1000

set val(rp) AODV ;# Routing protocol

# Create nodes

for {set i 0} {$i < 10} {incr i} {

set node_($i) [$ns node]

}

# Increase transmission power

$node_(1) set transmit_power_ 0.8

# Enable RED for congestion control

set redQueue [new Queue/RED]

$redQueue set thresh_ 10

$redQueue set maxthresh_ 30

$node_(1) attach $redQueue

# Simulation start

$ns run

  1. Post-Simulation Analysis

We use the trace files after the simulation, to evaluate the metrics like:

  • Throughput: Total data effectively transmitted in a given time.
  • Delay: Time taken for a packet to traverse the network.
  • Packet Delivery Ratio (PDR): The ratio of packets are effectively delivered to the end.
  • Packet Loss: Number of packets lost because of the congestion or interference.

By examining these metrics, we can compute the improvements in network capacity.

Summary

To enhance the network capacity within NS2, we can combine numerous strategies such as:

  1. Enhancing routing protocols such as multipath routing.
  2. Executing the load balancing.
  3. Handling the channel assignment and also frequency reuse.
  4. Improving MAC layer protocols like adjusting contention window.
  5. Executing congestion control such as RED.
  6. Maximising the transmission power.
  7. Adaptive rate control and congestion avoidance like TCP Vegas.
  8. Node placement optimization.
  9. Utilizing multipath routing algorithms such as AOMDV.

As demonstrated, we described the implementation process for Network Capacity Improvement and optimization of network capacity that were executed in the simulation platform NS2. More valuable details will be delivered in various manual.

In relation to your projects, we emphasize contemporary research methodologies to ensure optimal results. For superior implementation outcomes in Network Capacity Improvement for your projects, please reach out to ns2project.com. Our developers offer exceptional project support, focusing on network parameters, congestion control, adaptive routing, load balancing, frequency reuse, and the enhancement of transmission power tailored to your specific project needs. We invite you to explore the best topics and ideas with us.