Learning and Management for Internet of Things: Accounting for Adaptivity and Scalability

Abstract

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on. automation, and thus intelligence toward the vision of realtime IoT. However, despite the popularity of IoT, several critical challenges must be addressed before embracing its full potential [5], [86]. To this end, we highlight three key challenges that are arguably expected to be at the epicenter of emerging IoT research fields. Fig. 1:Internet of Everything[3]. Extremeheterogeneity. The computational and communication capacities of connected devices differ due to differences in hardware (e.g., CPU frequency), communication protocol (e.g., ZigBee, WiFi), and energy availability (e.g., battery level) [103]. The tasks carried out on various devices are often considerably diverse, e.g., motion sensors monitor human behavior in a smart home [60], while cameras are responsible for recognizing a suspicious behavior in a crowded environment, or, vehicle plates in a parking garage.Unpredictable dynamics. Unlike many existing communication, computing and networking platforms, the IoT dynamics can stem from multiple sources, where adaptivity is not only critical but also essential in designing hardware and management protocols. Such sources entail human-in-the-loop dynamics in addition to physical objects [60], demand response in energy systems [40], and intelligent automotive applications [59]. In these applications, IoT dynamics are intertwined with or even partially determined by human behavior [34], [69], [73] -as such, high degree of adaptivity in the algorithm and hardware design is needed.Scalability at the core. IoT entails an intelligent network infrastructure with...

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