XGate: Explainable Reinforcement Learning for Transparent and Trustworthy API Traffic Management in IoT Sensor...

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• The proliferation of Internet of Things (IoT) devices and their APIs complicates sensor network traffic management.
• Existing traffic management solutions often lack transparency, hindering effective control in large-scale deployments.

Purpose of the Study:

• To introduce XGate, a novel explainable reinforcement learning framework for transparent API traffic management in sensor networks.
• To address the need for balancing optimal routing decisions with interpretability for network administrators.

Main Methods:

• Integration of transformer-based attention mechanisms with counterfactual reasoning for explainable decisions.
• Development of a reinforcement learning framework tailored for sensor network API traffic.
• Evaluation through extensive experimentation on large-scale sensor API traffic datasets and user studies.

Main Results:

• XGate achieved 23.7% lower latency and 18.5% higher throughput compared to state-of-the-art black-box methods.
• User studies indicated a 67% improvement in operator trust and a 41% reduction in intervention time.
• Theoretical analysis confirmed probabilistic guarantees on explanation fidelity and computational efficiency.

Conclusions:

• XGate offers a significant advancement in trustworthy AI for IoT infrastructure, providing transparent decision-making.
• The framework enhances performance in dynamic sensor network environments without sacrificing interpretability.
• XGate empowers network administrators with understandable insights into complex traffic management decisions.