Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course provides an in-depth exploration of UAV networks and the routing protocols essential for efficient communication and operation. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course provides an in-depth exploration of UAV networks and the routing protocols essential for efficient communication and operation.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
UAV Network Topologies
• Single-UAV, swarm and multi-tier network architectures
• Air-to-air and air-to-ground link characteristics and their asymmetry
• Backhaul options and integration with terrestrial and satellite networks
Wireless Propagation for Aerial Nodes
• Line-of-sight probability and altitude-dependent path loss
• Doppler, antenna orientation and body blockage from the airframe
• Interference management in shared spectrum
Protocols for Highly Dynamic Topologies
• Why MANET protocols degrade under UAV mobility
• Position-based and predictive routing using flight plans
• Delay-tolerant approaches for intermittently connected swarms
Swarm Control and Resource Allocation
• Trajectory planning that jointly serves mission and connectivity
• Energy-aware operation and the flight-time constraint on everything
• Task allocation and consensus in a distributed swarm
Simulation, Regulation and Applications
• Simulation with NS-3 or OMNeT++ and validating against flight data
• Airspace regulation, remote identification and BVLOS constraints
• Applications in coverage, sensing and emergency communications
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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