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AI-enhanced routing and slicing strategy for QoS-aware mobile ad hoc networks
Venkatesan C1, Shaha Al-Otaibi2, Balaji Vijayan V3
1Department of Electronics and Communication Engineering, HKBK College of Engineering, Opposite Manyata Tech Park, Nagawara, Bengaluru, 560045. Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India. venkatesanc.ec@hkbk.edu.in.
Scientific Reports
|January 28, 2026
Summary
This study introduces an AI framework using Deep Reinforcement Learning and Network Slicing for Mobile Ad Hoc Networks (MANETs). It significantly reduces delay and boosts throughput and packet delivery, improving Quality of Service (QoS) in dynamic environments.
Area of Science:
- Computer Science
- Telecommunications Engineering
- Artificial Intelligence
Background:
- Mobile Ad Hoc Networks (MANETs) face challenges in maintaining Quality of Service (QoS) due to node mobility, interference, and traffic variations.
- Existing routing protocols struggle to adapt to the dynamic nature of MANETs, leading to performance degradation.
Purpose of the Study:
- To propose an AI-enhanced routing and network slicing framework for MANETs.
- To improve packet delivery, reduce latency, and enhance throughput in dynamic MANET environments.
Main Methods:
- Coupling Deep Reinforcement Learning (DRL) for intelligent routing with adaptive Network Slicing (NS).
- Utilizing a DRL agent trained with Proximal Policy Optimization to select optimal next hops based on network conditions.
- Implementing a fuzzy logic-based network slicer for real-time bandwidth reallocation across network slices.
Main Results:
- The proposed framework reduced average delay by 37% and increased throughput by 1.8 times compared to traditional protocols (AODV, DSR) and a standalone DRL router.
- Packet delivery ratio improved by 22% at node speeds up to 20 m/s.
- The scheme maintained energy efficiency and low control overhead.
Conclusions:
- Integrating intelligent routing (DRL) with agile network slicing (NS) is an effective strategy for sustaining application-level QoS in highly dynamic MANETs.
- The AI-enhanced framework offers a viable solution for overcoming performance limitations in current MANETs.
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