AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A framework is proposed for collaborative decision-making learning to select secure, load-balanced routing paths, leveraging multiple-level distributed computation to achieve trustworthy communication.
Simulation analyses conducted in NS-3 demonstrate that the proposed integrated, multi-tier optimization framework achieves superior performance in terms of Packet Delivery Ratio (PDR), energy conservation, end-to-end latency, and resilience against malicious routing attacks compared to existing baseline protocols.
This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA) to ensure that the chain and reward functions align with the Quality of Service (QoS).
The findings indicate that in decentralized wireless environments, HBbGBBTMN significantly enhances network efficiency, security, and dependability.
The study proposes the hybrid optimized harmony search enhanced distributed energy-efficient clustering algorithm that optimizes route selection, detects faults, and calculates probability values to identify reliable communication paths, thereby enhancing fault tolerance (FT) and energy consumption (EC) efficiency.
WSNs (wireless sensor networks) in hostile environments not only have limited energy resource but also have the existence of malicious node behaviour which significantly degrades both the routing reliability and network lifetime quality. To overcome this challenge, this study presents a trust-energy reinforced routing...
The rapid expansion of Internet of things (IoT) devices generates increasing service demands that require reliable computation in regions with limited fixed infrastructure. Uncrewed aerial vehicle (UAV)-based mobile edge computing (MEC) systems provide a flexible solution by relocating computational resources close to...
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