S-AI-IoT: A Sparse Artificial Intelligence Architecture with Hormonal Orchestration, Parsimonious Agent Activation, and Symbolic Memory for Adaptive, Secure, and Explainable Internet of Things Systems

Volume 17, Number 3

S-AI-IoT: A Sparse Artificial Intelligence Architecture with Hormonal Orchestration, Parsimonious Agent Activation, and Symbolic Memory for Adaptive, Secure, and Explainable Internet of Things Systems

Authors

Said Slaoui, Mohammed V University, Morocco

Abstract

The Internet of Things is undergoing a paradigm shift from passive data collection infrastructures toward ecosystems of autonomous, resource-constrained distributed computing entities. Existing IoT intelligence frameworks — whether rule-based, deep reinforcement learning-based, federated, or bio-inspired— share six structural failures: activation indiscriminateness, opacity, non-stationarity instability, federated learning overhead, absent symbolic memory*, and absence of reproducible evaluation infrastructure**. No existing approach addresses all six simultaneously while satisfying the energy, connectivity, security, explainability, and scalability constraints of operational IoT deployments.This article introduces S-AI-IoT, a formally grounded, intrinsically parsimonious, hormonally regulated, and natively explainable IoT intelligence framework that is the first to integrate these four properties by architectural design.S-AI-IoT extends the Sparse Artificial Intelligence (S-AI) paradigm through four original contributions. First, a seven-layer bio-inspired modular architecture deployed across node, gateway, and cloud tiers, with formally guaranteed local hormonal stability at each tier, enabling coherent autonomous operation under connectivity loss.Second, five canonical IoT hormones — Sensorin, Connectin, Energexin, Resiliencin, and Normin — whose reaction-diffusion dynamics on the IoT communication graph implement a unified continuous signaling layer jointly encoding sensor quality, connectivity, energy, resilience, and security compliance. Third, a primal-dual parsimonious orchestration mechanism selecting the minimal sufficient agent subset at each decision instant, with Lyapunov stability guarantees — formally established in Article II (Theorem 6.1) under the deployability condition 𝜆𝑘 > 𝐷𝑘𝜆𝑚𝑎𝑥(𝐿 ∗ )+ ∑𝑚≠𝑘 𝛾𝑘𝑚 + 𝜌𝑘 ∥ 𝜒 ∗ ∥∞, verifiable a priori from network parameters alone — and Euler-Maruyama discretization.Fourth, a distributed symbolic engram memory enabling contextual recall, cross-episode behavioral acceleration, and intrinsic explainability at negligible additional cost over the normal decision cycle.A comparative analysis demonstrates that S-AI-IoT is the only framework among those surveyed to simultaneously address all six identified structural dimensions.This article is the first of a three-part series: Article II develops the complete formal mathematical specification and algorithmic implementation; Article III presents the experimental evaluation on the SAI-UT+ IoT testbench across ten operational scenarios with full ablation study.

Keywords

Internet of Things, sparse artificial intelligence, hormonal orchestration, activation parsimony, reactiondiffusion dynamics, bio-inspired architecture, symbolic memory, engram, explainable AI, federated learning, multi-agent systems, edge computing, energy harvesting, duty cycle management, intrinsic explainability, homeostatic regulation, neuroendocrine systems, S-AI-IoT, S-AI-ROBOTICS, constrained computing