Hybrid AI Modal for Edge Computing in 5G

Abstract

Growing next generation technologies include autonomous driving, smart healthcare systems, and augmented reality provide massive amounts of data that need to be consistently and fast handled. Expectations of ultra-low latency and tremendous bandwidth have surged sharply with the deployment of 5G networks. Although conventional cloud computing provides a lot of processing capability, its inherent delay from centralized architectures makes it difficult to fulfill the real-time needs of these applications. Moving computation closer to data sources gives edge computing a potential answer. The edge does, however, have several drawbacks as well: limited processing resources, changing device circumstances, and higher security hazards. We offer a hybrid AI-driven architecture specifically for 5G edge settings in order to handle these difficulties. The approach deliberately combines lightweight machine learning and deep learning modules to dynamically allocate tasks across edge, fog, and cloud tiers. It combines important technologies like a trust-aware approach to filter unreliable edge nodes, reinforcement learning for intelligent job offloading, and federated learning for privacy protection. Here we created and simulated the whole architecture with MATLAB. Early-exit logic in a modular hybrid AI system with RL-based offloading agent, trust score evaluation, and federated model aggregation forms part of our approach. To see system latency, bandwidth usage, and performance changes under dynamic traffic, we also created waveform graphs. Simulation results showed that the proposed hybrid model lay a strong basis for next-generation intelligent edge systems in real-world 5G deployments since it outperformed both edge-only and cloud only configurations in terms of response time, scalability, and resilience to adversarial conditions.

Introduction

With the rise of real-time, data-intensive applications (e.g., autonomous vehicles, remote health monitoring), traditional cloud computing is too slow and bandwidth-hungry for fast decision-making. Edge computing offers faster local processing, but edge devices often lack the resources to run complex AI models.

???? Proposed Solution: Hybrid AI Framework

This study proposes a hybrid AI architecture that smartly distributes tasks between edge, fog, and cloud layers based on context, resources, and urgency. It integrates:

• Lightweight ML models at the edge for fast decisions

• Deeper models in the fog/cloud for complex tasks

• Reinforcement Learning (RL) for intelligent task offloading

• Federated Learning (FL) for privacy-preserving distributed training

• Trust scoring to filter unreliable devices in FL

• Early-exit mechanisms for adaptive inference to save energy

????? System Architecture

Three-layer hybrid design:

Also includes:

• RL Agent: Learns to route tasks dynamically

• FL Engine: Enables collaborative learning without raw data sharing

• Trust Module: Filters out malicious or low-quality nodes

???? Research Methodology

• Platform: MATLAB R2023a (chosen for realistic signal behavior modeling)

• Simulation Inputs:

  • ECG waveforms (healthcare)

  • Sine/square signals (industrial sensors)

  • 28x28 grayscale images (surveillance footage)

Key Evaluation Metrics:

• Latency

• Accuracy

• Energy consumption

• Bandwidth usage

• % of Early exits

???? Key Algorithms & Formulas

1. Early Exit: Stops inference early if confidence is ≥ 85%

2. Q-Learning (for offloading decisions):

   • State = [Bandwidth, Battery, CPU load, Confidence]

   • Action = {Local, Fog, Cloud}

3. FedAvg: Aggregates models from multiple edge devices

4. Trust Score: Dynamically evaluates nodes for secure FL

5. Energy Estimation: E = Power × Time

???? Results

MATLAB simulations under changing 5G conditions (e.g., bandwidth drops, device failures) showed the proposed system achieved:

• Lower latency than full cloud-based systems

• Better energy efficiency for edge devices

• Improved task accuracy via intelligent offloading

• Robustness to malicious nodes using trust-aware FL

???? Identified Gaps in Current Research

• Weak adaptability in static AI architectures

• Inadequate trust handling in FL

• Lack of coordination across edge-fog-cloud layers

• Little focus on energy-efficient AI models

• Minimal attention to scalability, ethics, and transparency

???? Motivation

With the surge of real-time AI demands (e.g., emergency health alerts, autonomous actions), fast, secure, and scalable AI systems are critical. Cloud-alone or edge-alone solutions are not enough. This research aims to build a real-world-ready system that’s fast, intelligent, privacy-conscious, and energy-efficient.

? Key Contributions

1. Hybrid AI architecture for real-time 5G applications

2. RL-based task routing to reduce latency and cloud load

3. Trust-aware FL to safeguard against bad data/models

4. Early-exit inference for edge resource savings

5. MATLAB simulation validation under dynamic network conditions

Conclusion

Following a thorough investigation on the junction of artificial intelligence and edge computing under 5G architecture, this work has produced and validated a hybrid AI framework meant to satisfy the always rising needs of real-time, resource efficient, and safe edge intelligence. The proposed system integrates machine learning, deep learning, reinforcement learning, and federated learning into one coherent and dynamic decision-making engine using a layered approach distributed across Edge, Fog, and Cloud. Early-exit inference lowers response time at the edge layer by conserving energy, so enabling practical use for real-time tasks in smart environments, health monitoring, and surveillance. During federated learning rounds, fog nodes provided mediators for more intricate inference, model aggregation, and trust evaluation, so adding scalability and resilience to the system. Though only used for deeper inference or long-term learning, the cloud layer guaranteed that the global model stayed thorough and current, so supporting knowledge transfer between edge clusters. MATLAB\'s extensive simulations revealed the system\'s latency, accuracy, energy economy, and bandwidth optimization performance. Dynamic task routing depending on real-time metrics was mostly dependent on the reinforcement learning agent, so enhancing system adaptability over load conditions and network volatility. Preserving model integrity while supporting data privacy by federated learning with trust-aware filtering addressed ethical and technical issues. Still unresolved issues are cold-start nodes, fog layer overloads under dense edge deployment, and convergence latency in federated training. Particularly when considering complex adversarial attacks or insider manipulation of trust scores, security is always changing. These spaces provide rich ground for more improvement.

References

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Copyright

Copyright © 2025 Amit ., Dr. Amandeep , Khushi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.