Enterprise AI Analysis
AI-enhanced routing and slicing strategy for QoS-aware mobile ad hoc networks
This research proposes an AI-enhanced routing and slicing framework for Mobile Ad Hoc Networks (MANETs) that couples Deep Reinforcement Learning (DRL) with adaptive Network Slicing (NS) to steer packets through latency-aware, slice-specific paths. The DRL agent observes local topology changes, queue states, and slice budgets, then selects next hops that jointly minimize end-to-end delay and maximize packet delivery ratio, while a fuzzy logic slicer reallocates bandwidth across slices in real time. Simulations over 100 to 300 nodes moving under the Random Waypoint model showed that, compared with Ad Hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and a standalone DRL router, the proposed scheme educed average delay by 37%, increased throughput by a factor of 1.8, and lifted packet delivery ratio by 22% at node speeds up to 20 m/s, without sacrificing energy efficiency or incurring excessive control overhead. These results confirm that integrating intelligent routing with agile slicing is a viable pathway to sustain application level QoS in highly dynamic MANETs.
Key Enterprise Impact
37%
Average Delay Reduction
1.8x
Throughput Increase
22%
Packet Delivery Ratio Lift
Sub-millisecond
Inference Latency
0.80mJ
Energy per Packet (mJ)
Deep Analysis & Enterprise Applications
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Overview Insights
This article proposes an AI-enhanced routing and slicing framework for Mobile Ad Hoc Networks (MANETs) that couples Deep Reinforcement Learning (DRL) with adaptive Network Slicing (NS) to steer packets through latency-aware, slice-specific paths. The DRL agent observes local topology changes, queue states, and slice budgets, then selects next hops that jointly minimize end-to-end delay and maximize packet delivery ratio, while a fuzzy logic slicer reallocates bandwidth across slices in real time.
DRL & Slicing Insights
The proposed AI-enhanced strategy integrates Deep Reinforcement Learning (DRL) for intelligent routing and fuzzy logic for adaptive network slicing. The DRL agent uses Proximal Policy Optimization (PPO) and observes real-time slice utilization, link quality, and node mobility to make routing decisions. The fuzzy slicer dynamically reallocates bandwidth by adjusting contention window sizes based on queue lengths and signal-to-noise ratios, anticipating demand with a GBRT model.
Performance Insights
Comparative simulations demonstrate significant performance improvements. The proposed scheme reduces average end-to-end delay by 37%, boosts throughput by a factor of 1.8, and lifts packet delivery ratio by 22% at node speeds up to 20 m/s, without sacrificing energy efficiency or incurring excessive control overhead. These gains are consistent across various node densities and traffic patterns, highlighting the robustness of the integrated approach.
Implementation Insights
The framework was trained in MATLAB using Proximal Policy Optimization and implemented slice control with native Communications System Toolbox functions. The DRL agent's inference latency is sub-millisecond on a 100 MHz Cortex M4 microcontroller, confirming computational viability for resource-constrained MANET nodes. Control information is piggybacked on route reply packets to minimize signaling overhead.
37%
Reduction in Average End-to-End Delay
AI-Enhanced Routing Agent Architecture
Observation
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Encoder
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Actor Critic Core (Policy & Value)
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Action
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MAC Queue
| Metric | Proposed AI Enhanced | AODV QoS | DRL Only |
|---|---|---|---|
| Median Latency @ 20 m/s (ms) | 93 | 180 | 140 |
| Aggregate Throughput (Mbit/s) | 6.8 | 3.9 | 5.6 |
| Mean Packet Delivery Ratio @ 20 m/s (%) | 93 | 78 | 85 |
| Control Overhead (kB/s) | 1.8 | 5.2 | 3 |
| Energy per Packet (mJ) | 0.80 | 1.30 | 1.00 |
1.8x
Increase in Network Throughput
Achieving Robust QoS in Dynamic MANETs
The integration of Deep Reinforcement Learning for proactive routing and fuzzy logic for adaptive network slicing allows the system to overcome the inherent challenges of highly mobile ad hoc networks. By continuously adapting to topology changes and traffic bursts, the AI-enhanced framework ensures sustained Quality of Service (QoS) across diverse application demands, from low-latency conversational traffic to high-reliability telemetry. This represents a significant leap from traditional reactive protocols.
Key Benefit: By leveraging AI, MANETs can achieve unprecedented levels of reliability and efficiency, critical for mission-critical and real-time applications where traditional methods fail.
Calculate Your Potential ROI
Estimate the financial and operational benefits of adopting AI-enhanced networking in your enterprise.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A structured approach to integrating AI-enhanced networking into your existing infrastructure.
Phase 01: Discovery & Assessment
Comprehensive analysis of current network infrastructure, traffic patterns, and QoS requirements. Identify critical applications and mobility profiles.
Phase 02: AI Model Customization & Training
Tailor DRL agents and fuzzy slicers to enterprise-specific needs. Utilize historical data for offline training and simulation with real-world scenarios.
Phase 03: Phased Deployment & Integration
Pilot deployment in a controlled environment, followed by gradual rollout across critical segments. Integrate with existing network management systems.
Phase 04: Continuous Learning & Optimization
Ongoing monitoring, performance tuning, and online learning to adapt to evolving network conditions and traffic demands. Ensure sustained QoS and efficiency.
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