Reinforcement Learning

Simulation and Analysis of IoT LoRaWAN Networks Under ns-3

Low-power wide-area network (LPWAN). These networks use low-power radio signals, such as those in the sub-1 GHz range, to send small amounts of data over a large area. LPWANs are […]

Creating Random Road Network, Traffic Flows, Signals in SUMO

SUMO has different tools for creating and customizing road networks and traffic flows and traffic signals. In this article, we will use some of those tools available in SUMO to […]

Implementation of Spring Mobility Model for ns-3 and Visualizing it in 3D along with Circle Mobility

Spring Mobility Spring mobility is nothing but making a node to move in a path similar to that of an expanded circular coil spring. From the top or bottom point […]

Implementation of Circle Mobility Model for ns-3 and Visualizing it in 3D

Mobility Models of ns-3. The default ns-3 installation will contain the following mobility models. MobilityModel Subclasses ConstantPosition ConstantVelocity ConstantAcceleration GaussMarkov Hierarchical RandomDirection2D RandomWalk2D RandomWaypoint SteadyStateRandomWaypoint Waypoint PositionAllocator Position allocators […]

3D Aquatic Animal Tracking Underwater Network Simulation (UWSN) Under ns-3

Aquatic Animal Tracking: Tracking marine animals can be extremely tricky due to GPS signals not functioning well underwater[1]. Underwater acoustic communication is a technique of sending and receiving messages below […]

RL-FEC – A Self-Contained Reinforcement Learning Code of FEC for ns-3

Forward Erasure Correction (FEC) Packet-level forward erasure correction is effective for achieving low-latency transmissions in non-terrestrial networks (NTNs), which often contain lossy links with long propagation delay[1]. In [1] the […]
ns-3, ns-3 Installation, Research, Restricted Articles