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.

中文翻译：

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适用于物联网应用的普及型AI：资源高效的分布式人工智能

从推荐系统到机器人控制和军事监视，人工智能（AI）在各种物联网（IoT）应用程序和服务中均取得了重大突破。这是由于更容易访问感官数据以及产生Zettabytes（ZB）实时数据流的无处不在的/无处不在的设备的巨大规模。使用这样的数据流设计准确的模型，以预测未来的见解并革新决策过程，将普及的系统作为一种值得改善的生活质量的有价值的范例进行了开创。普适计算和人工智能的融合，普适性AI将无处不在的物联网系统的作用从主要的数据收集扩展到执行分布式计算，并提供了一种有希望的替代集中式学习的方法，提出各种挑战。在这种情况下，应该在物联网设备（例如，智能手机，智能车辆）和基础设施（例如，边缘节点和基站）之间构想明智的合作和资源调度，以避免通信和计算开销并确保最佳性能。在本文中，我们对为克服普及型AI系统中的这些资源挑战而开发的最新技术进行了全面调查。具体来说，我们首先概述普适计算，其架构及其与人工智能的交集。然后，我们回顾了在无处不在的系统中运行的AI的背景，应用程序和性能指标，尤其是深度学习（DL）和在线学习。接下来，我们将对通讯效率高的技术进行深入的文献综述，从算法和系统角度来看，涵盖物联网设备，边缘设备和云服务器的组合的分布式推理，培训和在线学习任务。最后，我们讨论了我们的未来愿景和研究挑战。