AI-Native Distributed Edge Intelligence for Resource-Aware Ultra-Low-Power IoT Networking

Abstract

– As ultra-low-power Internet of Things (IoT) networks continue to expand into dense, mission-critical environments, there is a growing need for communication frameworks that intelligently manage power, bandwidth, and computation without relying on centralized processing. However, existing ultra-low-power IoT networking frameworks still depend on static routing policies, centralized intelligence, excessive communication overhead, and computationally expensive learning mechanisms that cannot efficiently adapt to dynamic channel conditions, node failures, and stringent energy constraints in dense deployments. To overcome these constraints, this paper introduces NEURAL-MESH (Nano-Energy Edge-Unified Resource-Adaptive AI-Driven Learning-Enabled Multi-tier Edge Self-optimizing Hierarchy), a novel AI-native framework that embeds cooperative micro-learning modules within the network stack, enabling nodes to perform on-device behavioral prediction, ultra-lightweight federated feature fusion, and hierarchical mesh-level adaptation. Unlike traditional distributed learning, NEURAL-MESH employs a hybrid micro-inference and nano-aggregation mechanism where each node independently executes compressed temporal-spatial predictors while selectively contributing minimal learned signatures to nearby cluster heads for real-time optimization of channel selection, wake-up cycles, and routing paths. Experimental results across heterogeneous low-power platforms indicate that NEURAL-MESH reduces node-level energy consumption by 42%, increases routing stability by 29%, and cuts latency by 21% compared to existing AI-assisted IoT protocols. These findings validate that embedding hierarchical micro-cooperative intelligence directly inside the communication mesh significantly enhances adaptability without increasing computational load. The proposed method, NEURAL-MESH establishes a unique and scalable pathway for future ultra-low-power IoT, demonstrating how nano-scale collaborative learning can unlock sustainable, self-optimizing networks. The proposed NEURAL-MESH framework demonstrates superior performance, with an average energy consumption of 25 mJ, a packet delivery ratio of 95%, an end-to-end latency of 70 ms, and a routing stability index of 0.85.