To simulate FANET (Flying Ad-hoc Networks) projects utilizing NS2 (Network Simulator 2) that encompasses modelling the communication among unmanned aerial vehicles (UAVs), which form an ad-hoc network. FANETs are typified by their highly dynamic topology, mobility, and the requirement for efficient routing protocols to make reliable communication. Even though NS2 does not natively support UAVs or FANETs, we can replicate these networks by setting up mobility models and applying ad-hoc network routing protocols such as AODV, DSDV, or DSR.
The following is a step-by-step instruction to replicate FANET projects using NS2:
Steps to Simulate FANET Projects Using NS2
- Install NS2:
Make sure NS2 is installed and working on the system. We can download and install NS2 from the NS2 website.
- Define Network Topology for UAVs:
Each UAV is denoted as a wireless node in NS2, and we can set up a dynamic topology for the UAVs. UAVs communicate with each other via ad-hoc links, same to MANET (Mobile Ad-hoc Networks).
Example of defining UAV nodes:
set ns [new Simulator]
# Create UAV nodes (representing each UAV in the network)
for {set i 0} {$i < 5} {incr i} {
set uav($i) [$ns node]
}
# Define communication links (wireless ad-hoc communication)
$ns node-config -adhocRouting AODV -llType LL -macType Mac/802_11 -ifqType Queue/DropTail/PriQueue \
-ifqLen 50 -antType Antenna/OmniAntenna -propType Propagation/TwoRayGround \
-phyType Phy/WirelessPhy -channelType Channel/WirelessChannel
- Set Up Mobility Models for UAVs:
UAVs are mobile, thus it’s necessary to describe mobility models, which reflect the movement patterns of the UAVs in 3D space. The network simulator NS2 supports mobility models, and we can utilize tools such as setdest or generate mobility traces to replicate UAV movements.
Example of configuring random waypoint mobility for UAVs:
# Generate random waypoint mobility for each UAV
setdest -n 5 -p 0.0 -s 20 -t 100 -x 1000 -y 1000 > mobility.tcl
source mobility.tcl
- -n 5: Number of UAVs (nodes)
- -p 0.0: Pause time between movements
- -s 20: Maximum speed (20 m/s)
- -t 100: Simulation time (100 seconds)
- -x, -y: Size of the simulation area (1000m x 1000m)
- Implement Routing Protocols for FANET:
FANETs depends on ad-hoc routing protocols like AODV (Ad-hoc On-demand Distance Vector), DSR (Dynamic Source Routing), or OLSR (Optimized Link State Routing) for data communication among UAVs. The simulation environment NS2 supports these routing protocols for wireless ad-hoc networks.
Example of setting up AODV routing protocol for UAV communication:
# Configure AODV routing protocol for the UAV network
$ns node-config -adhocRouting AODV
# Define links between UAVs (handled automatically by AODV based on proximity)
for {set i 0} {$i < 4} {incr i} {
$ns duplex-link $uav($i) $uav([expr $i+1]) 11Mb 10ms DropTail
}
- Simulate Communication Between UAVs:
We can replicate distinct kinds of communication among UAVs, like control messages, video streaming, or sensor data transmission. We can set up UDP or TCP traffic among the UAVs based on the type of data.
Example of setting up UDP communication between UAVs:
# Attach UDP agents to UAV nodes to simulate data transmission
set udp1 [new Agent/UDP]
set null1 [new Agent/Null]
$ns attach-agent $uav(0) $udp1
$ns attach-agent $uav(1) $null1
$ns connect $udp1 $null1
# Set up CBR traffic to simulate constant data flow (e.g., sensor data)
set cbr1 [new Application/Traffic/CBR]
$cbr1 set packetSize_ 512
$cbr1 set interval_ 0.1 ;# Generate 10 packets per second
$cbr1 attach-agent $udp1
- UDP is generally used for real-time data transmission.
- TCP can be utilized if reliable data transmission is needed, like for file transfer or large data streams.
- Simulate 3D Mobility (Optional):
Since FANETs operate in 3D space, replicating mobility in the Z-dimension is necessary. NS2 does not natively support 3D mobility, however we can manually inseret Z-coordinates or use custom mobility scripts.
Example of adding a Z-dimension to UAV movement:
# Create nodes and assign random Z-coordinates for 3D mobility
for {set i 0} {$i < 5} {incr i} {
set uav($i) [$ns node]
$uav($i) set X_ [expr rand() * 1000]
$uav($i) set Y_ [expr rand() * 1000]
$uav($i) set Z_ [expr rand() * 500] ;# Random altitude between 0 and 500 meters
}
# Update mobility model to include Z-dimension
source mobility_with_z.tcl
Otherwise, we can utilize a Mobility Generator such as BonnMotion to generate 3D mobility traces, which contain altitude for UAVs.
- Simulate Different FANET Scenarios:
We can expand the simulation to contain distinct FANET scenarios:
- Network formation and repair: Replicate how UAVs form a network and manage link failures or node departures.
- Collision avoidance: Execute basic collision avoidance algorithms by changing mobility models.
- Energy consumption: Model energy constraints in UAVs and enhance the network to minimize energy consumption.
- Load balancing: Distribute data evenly between UAVs to prevent overloading any single node.
- Run the Simulation:
After configuring the nodes, mobility, and routing protocols then we can run the simulation using NS2.
Example of running the simulation:
ns fanet_simulation.tcl
Visualize the simulation using NAM (Network Animator):
nam fanet_simulation.nam
- Analyze Results:
We can utilize the trace file (*.tr) generated by NS2 to investigate key performance parameters for FANETs:
- Packet delivery ratio: Compute how many data packets are effectively delivered among UAVs.
- End-to-end delay: Assess the delay in communication among UAVs.
- Throughput: Calculate the amount of data effectively sent over the network.
- Mobility effects: Investigate how UAV mobility influences network performance, particularly when UAVs move out of range.
We can be utilised AWK or Python scripts to process the trace files and extract these prameters.
- Advanced Features (Optional):
- Dynamic topology changes: Replicate UAVs joining and exit the network, triggering changes in topology.
- Cooperative routing: Execute cooperative routing protocols in which UAVs support each other in relaying data.
- Energy efficiency: Mimic energy consumption models for UAVs to learn network performance under limited power.
- Multi-tier FANET: Replicate a multi-tier FANET architecture where a few UAVs perform as relay nodes even though others perform sensing or communication tasks.
We carefully explained the systematic approach used for FANET Projects, which was simulated and analyse its results using NS2 simulator. Further informations will be delivered regarding this topic rely on your requirements.
We will work on FANET simulation with the ns2 tool related to your project. Additionally, we can provide you with great project topics in your area of interest. Please send us all the details about your project, and we will assist you with a thorough explanation.
