Human-in-the-Loop Mobile Networks: A Survey of Recent Advancements

Lingjie Duan, Longbo Huang, Cedric Langbort, Alexey Pozdnukhov, Jean Walrand, Lin Zhang

Research output: Contribution to journalReview article

Abstract

Recent developments of smart devices and mobile applications have significantly increased the level at which human users interact with mobile systems. As a result, human activities, usage behavior, and perceived experience of users weigh increasingly on the performance of mobile networks, which has created new challenges for system operation in various aspects, such as increasing uncertainty, selfishness in operations, and complicated performance evaluation. On the other hand, the strong engagement of a large population of human users makes it possible to take advantage of the unique features of human behavior and to leverage the computing powers owned by users. Due to these emerging features of mobile networks, their design and evaluation require a hybrid view of human factor and information technology, and a paradigm shift is required for designing a new human-in-the-loop architecture by actively learning, adapting, and steering user behavior, so as to exploit the human factor in future ubiquitous mobile systems, and to greatly enhance system efficiency and provide superior quality-of-experience to users. The goal of this survey is to summarize recent results that focus on understanding and exploiting the human factor in mobile networks. In the tutorial, we summarize and discuss novelties of these formulations, adopted methodologies, and interesting results. We also point out some future research directions.

Original languageEnglish (US)
Article number7911263
Pages (from-to)813-831
Number of pages19
JournalIEEE Journal on Selected Areas in Communications
Volume35
Issue number4
DOIs
StatePublished - Apr 2017

Fingerprint

Human engineering
Wireless networks
Information technology

Keywords

  • Human-in-the-loop
  • crowdsensing
  • game theory
  • mobile networks
  • online learning
  • optimal control
  • prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Human-in-the-Loop Mobile Networks : A Survey of Recent Advancements. / Duan, Lingjie; Huang, Longbo; Langbort, Cedric; Pozdnukhov, Alexey; Walrand, Jean; Zhang, Lin.

In: IEEE Journal on Selected Areas in Communications, Vol. 35, No. 4, 7911263, 04.2017, p. 813-831.

Research output: Contribution to journalReview article

Duan, Lingjie ; Huang, Longbo ; Langbort, Cedric ; Pozdnukhov, Alexey ; Walrand, Jean ; Zhang, Lin. / Human-in-the-Loop Mobile Networks : A Survey of Recent Advancements. In: IEEE Journal on Selected Areas in Communications. 2017 ; Vol. 35, No. 4. pp. 813-831.
@article{5b261719f2f44b69bb757b17910d6c74,
title = "Human-in-the-Loop Mobile Networks: A Survey of Recent Advancements",
abstract = "Recent developments of smart devices and mobile applications have significantly increased the level at which human users interact with mobile systems. As a result, human activities, usage behavior, and perceived experience of users weigh increasingly on the performance of mobile networks, which has created new challenges for system operation in various aspects, such as increasing uncertainty, selfishness in operations, and complicated performance evaluation. On the other hand, the strong engagement of a large population of human users makes it possible to take advantage of the unique features of human behavior and to leverage the computing powers owned by users. Due to these emerging features of mobile networks, their design and evaluation require a hybrid view of human factor and information technology, and a paradigm shift is required for designing a new human-in-the-loop architecture by actively learning, adapting, and steering user behavior, so as to exploit the human factor in future ubiquitous mobile systems, and to greatly enhance system efficiency and provide superior quality-of-experience to users. The goal of this survey is to summarize recent results that focus on understanding and exploiting the human factor in mobile networks. In the tutorial, we summarize and discuss novelties of these formulations, adopted methodologies, and interesting results. We also point out some future research directions.",
keywords = "Human-in-the-loop, crowdsensing, game theory, mobile networks, online learning, optimal control, prediction",
author = "Lingjie Duan and Longbo Huang and Cedric Langbort and Alexey Pozdnukhov and Jean Walrand and Lin Zhang",
year = "2017",
month = "4",
doi = "10.1109/JSAC.2017.2695738",
language = "English (US)",
volume = "35",
pages = "813--831",
journal = "IEEE Journal on Selected Areas in Communications",
issn = "0733-8716",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Human-in-the-Loop Mobile Networks

T2 - A Survey of Recent Advancements

AU - Duan, Lingjie

AU - Huang, Longbo

AU - Langbort, Cedric

AU - Pozdnukhov, Alexey

AU - Walrand, Jean

AU - Zhang, Lin

PY - 2017/4

Y1 - 2017/4

N2 - Recent developments of smart devices and mobile applications have significantly increased the level at which human users interact with mobile systems. As a result, human activities, usage behavior, and perceived experience of users weigh increasingly on the performance of mobile networks, which has created new challenges for system operation in various aspects, such as increasing uncertainty, selfishness in operations, and complicated performance evaluation. On the other hand, the strong engagement of a large population of human users makes it possible to take advantage of the unique features of human behavior and to leverage the computing powers owned by users. Due to these emerging features of mobile networks, their design and evaluation require a hybrid view of human factor and information technology, and a paradigm shift is required for designing a new human-in-the-loop architecture by actively learning, adapting, and steering user behavior, so as to exploit the human factor in future ubiquitous mobile systems, and to greatly enhance system efficiency and provide superior quality-of-experience to users. The goal of this survey is to summarize recent results that focus on understanding and exploiting the human factor in mobile networks. In the tutorial, we summarize and discuss novelties of these formulations, adopted methodologies, and interesting results. We also point out some future research directions.

AB - Recent developments of smart devices and mobile applications have significantly increased the level at which human users interact with mobile systems. As a result, human activities, usage behavior, and perceived experience of users weigh increasingly on the performance of mobile networks, which has created new challenges for system operation in various aspects, such as increasing uncertainty, selfishness in operations, and complicated performance evaluation. On the other hand, the strong engagement of a large population of human users makes it possible to take advantage of the unique features of human behavior and to leverage the computing powers owned by users. Due to these emerging features of mobile networks, their design and evaluation require a hybrid view of human factor and information technology, and a paradigm shift is required for designing a new human-in-the-loop architecture by actively learning, adapting, and steering user behavior, so as to exploit the human factor in future ubiquitous mobile systems, and to greatly enhance system efficiency and provide superior quality-of-experience to users. The goal of this survey is to summarize recent results that focus on understanding and exploiting the human factor in mobile networks. In the tutorial, we summarize and discuss novelties of these formulations, adopted methodologies, and interesting results. We also point out some future research directions.

KW - Human-in-the-loop

KW - crowdsensing

KW - game theory

KW - mobile networks

KW - online learning

KW - optimal control

KW - prediction

UR - http://www.scopus.com/inward/record.url?scp=85019652763&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019652763&partnerID=8YFLogxK

U2 - 10.1109/JSAC.2017.2695738

DO - 10.1109/JSAC.2017.2695738

M3 - Review article

AN - SCOPUS:85019652763

VL - 35

SP - 813

EP - 831

JO - IEEE Journal on Selected Areas in Communications

JF - IEEE Journal on Selected Areas in Communications

SN - 0733-8716

IS - 4

M1 - 7911263

ER -