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

Tianyi Chen, Sergio Barbarossa, Xin Wang, Georgios B. Giannakis, Zhi Li Zhang

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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 a 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.

Original languageEnglish (US)
Article number8648462
Pages (from-to)778-796
Number of pages19
JournalProceedings of the IEEE
Volume107
Issue number4
DOIs
StatePublished - Apr 2019

Bibliographical note

Funding Information:
Manuscript received June 23, 2018; revised October 25, 2018; accepted January 25, 2019. Date of publication February 21, 2019; date of current version March 25, 2019. The work of T. Chen and G. B. Giannakis was supported by the National Science Foundation (NSF) under Grant 1509040, Grant 1508993, and Grant 1711471. The work of S. Barbarossa was supported by the H2020 Europe-Japan Project 5G-MiEdge under Grant 723171. The work of X. Wang was supported in part by the National Natural Science Foundation of China under Grant 61671154, in part by the National Key Research and Development Program of China under Grant 2017YFB0403402, and in part by the Innovation Program of Shanghai Municipal Science and Technology Commission under Grant 17510710400. The work of Z.-L. Zhang was supported in part by the U.S. Department of Defense (DoD) under Contract HDTRA1-14-1-0040 and in part by NSF under Grant 1411636 and Grant 1617729. (Corresponding author: Tianyi Chen.) T. Chen and G. B. Giannakis are with the Digital Technology Center, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: chen3827@umn.edu; georgios@umn.edu). S. Barbarossa is with the Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy (e-mail: sergio.barbarossa@uniroma1.it). X. Wang is with the Shanghai Institute for Advanced Communication and Data Science, Shanghai 200444, China, also with the Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China, and also with the Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China (e-mail: xwang11@fudan.edu.cn). Z.-L. Zhang is with the Department of Computer Science, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: zhang@cs.umn.edu).

Publisher Copyright:
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Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Internet of Things (IoT)
  • mobile edge computing (MEC)
  • network resource allocation
  • online learning
  • stochastic optimization

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