How to Implement Network Bio Inspired Routing in NS2

To implement the Bio Inspired Routing in Network Simulator 2 (NS2), we have to build routing protocols that are inspired by natural systems including activities of ants, bees, swarms or neural networks. These algorithms are commonly decentralized, adaptive, and efficient, constructing them appropriately for dynamic and resource-constrained networks like mobile ad-hoc networks (MANETs) or wireless sensor networks (WSNs). The given procedure will help you implementation this in ns2:

Common Bio-Inspired Routing Protocols:

  1. Ant Colony Optimization (ACO): Replicates the foraging activities of ants to discover optimal routes amongst nodes.
  2. Bee-inspired Routing: Imitates how bees interact to discover the most efficient routes.
  3. Swarm Intelligence: Depends on the collective characteristics of decentralized, self-organized systems.
  4. Genetic Algorithms: Inspired by natural selection and evolution to enhance routing decisions.

Step-by-Step Guide to Implementing Bio-Inspired Routing in NS2

  1. Understand Bio-Inspired Algorithms

Bio-inspired routing protocols depend on:

  • Decentralized decision-making: Routing decisions are made locally by each node according to the information received from other nodes.
  • Adaptive behavior: Routes are dynamically adjusted based on current network conditions.
  • Heuristic methods: Instead of calculating the absolute best route, these algorithms tend to discover the best route by exploring and studying over time.

For this instance, we’ll concentrates on Ant Colony Optimization (ACO) as it is one of the most often executed bio-inspired routing protocols.

  1. Create the Basic Structure for ACO Routing

In ACO, ants are used as agents that explore the network, leaving behind pheromones on the path they take. Over time, more efficient paths aggregate more pheromones, guiding future ants (and data packets) sideways optimal routes.

Define the Ant Agent:

  • Ants behave like small control packets (exploratory agents) that traverse the network, gathering details about routes.
  • Pheromones indicates the quality of a path. They are deposited by ants and decay over time.

Example C++ Code for ACO Ant Agent:

class AntAgent : public Agent {

public:

AntAgent();

void recv(Packet* p);  // Override the receive function to process incoming ants

protected:

void forwardAnt(Packet* p);     // Forward the ant to the next node

void leavePheromone(Packet* p); // Leave pheromone on the current path

double pheromoneDecayRate;      // Decay rate of pheromone over time

};

// Constructor to initialize pheromone properties

AntAgent::AntAgent() : Agent(PT_UDP), pheromoneDecayRate(0.05) {}

// Function to process received ant agents

void AntAgent::recv(Packet* p) {

leavePheromone(p);  // Leave pheromone on the current path

// Check if the ant has reached the destination

hdr_ip* iph = HDR_IP(p);

if (iph->dst() == node_->address()) {

// Ant has reached the destination, reverse it to go back to the source

iph->set_direction(HDR_IP(p)->dir_reverse());

forwardAnt(p);  // Forward the ant back to the source

} else {

forwardAnt(p);  // Continue forwarding the ant

}

}

// Function to leave pheromone on the path

void AntAgent::leavePheromone(Packet* p) {

hdr_ip* iph = HDR_IP(p);

double currentPheromone = getPheromone(iph->src(), iph->dst());

setPheromone(iph->src(), iph->dst(), currentPheromone + (1.0 – pheromoneDecayRate));  // Update pheromone

}

// Forward the ant to the next node

void AntAgent::forwardAnt(Packet* p) {

// Use pheromone information to choose the next hop

int nextHop = getNextHopUsingPheromone();

sendto(p, nextHop);

}

This code states the AntAgent, which travel through the network, leaving pheromones on each path and forwarding itself in terms of the pheromone focusing on neighboring nodes.

  1. Maintain Pheromone Tables at Each Node

Each node needs to uphold a pheromone table that finds the pheromone levels for various paths. The table is used to guide ants and data packets to the optimal path based on the number of pheromone.

Example of Pheromone Table:

class PheromoneTable {

public:

PheromoneTable();

void updatePheromone(nsaddr_t src, nsaddr_t dst, double value);  // Update pheromone for a path

double getPheromone(nsaddr_t src, nsaddr_t dst);                 // Get pheromone value for a path

protected:

map<pair<nsaddr_t, nsaddr_t>, double> pheromoneTable;  // Pheromone table (src, dst) -> pheromone level

};

// Constructor to initialize the pheromone table

PheromoneTable::PheromoneTable() {}

// Update pheromone levels for a path

void PheromoneTable::updatePheromone(nsaddr_t src, nsaddr_t dst, double value) {

pheromoneTable[make_pair(src, dst)] = value;

}

// Get pheromone level for a given path

double PheromoneTable::getPheromone(nsaddr_t src, nsaddr_t dst) {

return pheromoneTable[make_pair(src, dst)];

}

Each node will uphold a pheromone table that is updated by the AntAgent as ants traverse the network.

  1. Ant Packet Forwarding Based on Pheromone Levels

Ants will use pheromone levels to make routing decisions. The higher the pheromone focusing on a path, the more likely it is to be chosen.

Example of Pheromone-Based Forwarding:

int AntAgent::getNextHopUsingPheromone() {

// Get pheromone levels for all neighbors

vector<int> neighbors = getNeighbors();

int bestNextHop = -1;

double maxPheromone = -1;

for (int neighbor : neighbors) {

double pheromone = getPheromone(node_->address(), neighbor);

if (pheromone > maxPheromone) {

maxPheromone = pheromone;

bestNextHop = neighbor;

}

}

return bestNextHop;  // Return the neighbor with the highest pheromone concentration

}

Here, the AntAgent picks the next hop based on the pheromone levels allied with its neighboring nodes. The path with the highest pheromone focusing is picked.

  1. Simulate Data Packet Forwarding

Once pheromone trails are accomplished by the ants, the original data packets can be dispatched based on these pheromone trails. Data packets will follow the paths with the highest pheromone concentrations.

Data Packet Forwarding:

void DataAgent::recv(Packet* p) {

// Forward data packet based on pheromone concentrations

int nextHop = getNextHopUsingPheromone();

sendto(p, nextHop);

}

  1. Implement Pheromone Decay

Over time, pheromone levels should wane to prevent old, suboptimal routes from being preferred indefinitely.

Pheromone Decay Function:

void PheromoneTable::decayPheromone() {

for (auto& entry : pheromoneTable) {

entry.second *= (1.0 – pheromoneDecayRate);  // Decay pheromone over time

}

}

Pheromone decay makes sure that paths are frequently updated based on current network conditions.

  1. Simulate the Bio-Inspired Routing in NS2

Once the AntAgent and DataAgent are executed and pheromone tables are configured, you can replicate the network using Tcl scripts. Ant agents will traverse the network to search routes, and data packets will be forwarded depends on the pheromone trails applied by the ants.

Example Tcl Script for Bio-Inspired Routing Simulation:

# Create a simulator instance

set ns [new Simulator]

# Create nodes

set node1 [$ns node]

set node2 [$ns node]

set node3 [$ns node]

# Attach ant agents to discover routes

set antAgent1 [new Agent/AntAgent]

set antAgent2 [new Agent/AntAgent]

$ns attach-agent $node1 $antAgent1

$ns attach-agent $node2 $antAgent2

# Attach data agents for actual packet forwarding

set dataAgent1 [new Agent/DataAgent]

set dataAgent2 [new Agent/DataAgent]

$ns attach-agent $node1 $dataAgent1

$ns attach-agent $node2 $dataAgent2

# Start the simulation by sending ants to discover routes

$ns at 1.0 “$antAgent1 sendAnt”

$ns at 1.5 “$antAgent2 sendAnt”

# Run the simulation

$ns run

  1. Evaluate the Performance

After the simulation, analyze the performance of the bio-inspired routing algorithm using metrics includes:

  • Packet Delivery Ratio (PDR): Compute how effectively the routing protocol sends packets.
  • Average Latency: Assess the delay experienced by packets.
  • Path Optimality: Verify if the selected paths are optimal or close to optimal according to the hop count and delay.
  • Energy Efficiency: For wireless sensor networks, validate the energy utilization allied with bio-inspired routing.

In conclusion, you can get to know more about how to approach the implementation of Bio Inspired Routing in the simulated network using ns2 tool to find the optimal path to deliver the packets across the network by executing Pheromone Decay into the simulation.

Contact ns2project.com for tailored Network Bio Inspired Routing in NS2 implementation guidance and project ideas.