Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
Emna Baccour, Naram Mhaisen, Alaa Awad Abdellatif, Aiman Erbad, Amr, Mohamed, Mounir Hamdi, Mohsen Guizani

TL;DR
This survey reviews recent resource-efficient distributed AI techniques for pervasive IoT systems, highlighting challenges, solutions, and future research directions to enable scalable, low-overhead AI applications across ubiquitous devices.
Contribution
It provides a comprehensive overview of resource-efficient distributed AI methods tailored for pervasive IoT environments, including communication strategies and system architectures.
Findings
Survey of communication-efficient distributed AI techniques
Analysis of challenges in resource-constrained pervasive systems
Discussion of future research directions
Abstract
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise…
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