Framework Title (Strong & Publishable)

Edge-Assisted Multi-Agent Deep Reinforcement Learning for Intelligent Anti-Jamming in Tactical Military IoT Networks

Core Idea

Design a hierarchical AI-driven anti-jamming framework where:

Resource-constrained IoT sensor nodes act as lightweight agents
Edge nodes (e.g., mobile command vehicles / UAV edge servers) perform heavy AI computation
A central controller aggregates intelligence when available

This balances:

Runs Multi-Agent Deep Reinforcement Learning (MADRL)
Learns optimal:
- Channel selection
- Power allocation
- Frequency hopping patterns
- Transmission scheduling

Include:

Multi-Agent DRL specifically optimized for resource-constrained IoT
Edge-assisted distributed learning to reduce computation burden
Federated anti-jamming learning to avoid centralized vulnerability
Energy-aware reward function design
Joint optimization of:
- Throughput
- Energy consumption
- Latency
- Anti-jamming resilience

$R = T E L + J$

Requirements: 10000