mlsanityData to 7 Oct 2026

Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence

arXiv 2105.01798 ↗First posted 4 May 2021cs.DC cs.AIcited by 18

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Abstract (arXiv)

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 cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 132 of its 293 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

132 of 293 references.

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Citing papers per year: 2022: 1; 2023: 6; 2024: 6; 2025: 4; 2026: 1.

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  4. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  5. 2022Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarmscited by 5
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  10. 2023AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Methodcited by 1
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  12. 2025Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomerationcited by 1
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  4. 2024Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networkscited by 0
  5. 2025Fundamentals of Physical AIcited by 0
  6. 2026Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edgecited by 0