Enabling green mobile crowd sensing via optimized task scheduling on smartphones

Jing Wang, Jian Tang, Xiang Sheng, Guoliang Xue, Dejun Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as an effective polynomial-time heuristic algorithm, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve over 79% energy savings on average compared to a widely-used baseline approach, and moreover, the proposed heuristic algorithm produces close-to-optimal solutions.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Communications Conference, GLOBECOM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479959525
DOIs
StatePublished - Feb 23 2016
Event58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States
Duration: Dec 6 2015Dec 10 2015

Other

Other58th IEEE Global Communications Conference, GLOBECOM 2015
CountryUnited States
CitySan Diego
Period12/6/1512/10/15

Fingerprint

Smartphones
scheduling
Scheduling
Sensors
heuristics
Heuristic algorithms
energy
Polynomials
energy saving
energy consumption
programming
Linear programming
Energy conservation
Energy utilization
simulation
present
demand
time

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

Cite this

Wang, J., Tang, J., Sheng, X., Xue, G., & Yang, D. (2016). Enabling green mobile crowd sensing via optimized task scheduling on smartphones. In 2015 IEEE Global Communications Conference, GLOBECOM 2015 [7417136] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2014.7417136

Enabling green mobile crowd sensing via optimized task scheduling on smartphones. / Wang, Jing; Tang, Jian; Sheng, Xiang; Xue, Guoliang; Yang, Dejun.

2015 IEEE Global Communications Conference, GLOBECOM 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7417136.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, J, Tang, J, Sheng, X, Xue, G & Yang, D 2016, Enabling green mobile crowd sensing via optimized task scheduling on smartphones. in 2015 IEEE Global Communications Conference, GLOBECOM 2015., 7417136, Institute of Electrical and Electronics Engineers Inc., 58th IEEE Global Communications Conference, GLOBECOM 2015, San Diego, United States, 12/6/15. https://doi.org/10.1109/GLOCOM.2014.7417136
Wang J, Tang J, Sheng X, Xue G, Yang D. Enabling green mobile crowd sensing via optimized task scheduling on smartphones. In 2015 IEEE Global Communications Conference, GLOBECOM 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7417136 https://doi.org/10.1109/GLOCOM.2014.7417136
Wang, Jing ; Tang, Jian ; Sheng, Xiang ; Xue, Guoliang ; Yang, Dejun. / Enabling green mobile crowd sensing via optimized task scheduling on smartphones. 2015 IEEE Global Communications Conference, GLOBECOM 2015. Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{c92ad9cbcd5342ef8ddf9f73f512ac5c,
title = "Enabling green mobile crowd sensing via optimized task scheduling on smartphones",
abstract = "In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as an effective polynomial-time heuristic algorithm, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve over 79{\%} energy savings on average compared to a widely-used baseline approach, and moreover, the proposed heuristic algorithm produces close-to-optimal solutions.",
author = "Jing Wang and Jian Tang and Xiang Sheng and Guoliang Xue and Dejun Yang",
year = "2016",
month = "2",
day = "23",
doi = "10.1109/GLOCOM.2014.7417136",
language = "English (US)",
isbn = "9781479959525",
booktitle = "2015 IEEE Global Communications Conference, GLOBECOM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Enabling green mobile crowd sensing via optimized task scheduling on smartphones

AU - Wang, Jing

AU - Tang, Jian

AU - Sheng, Xiang

AU - Xue, Guoliang

AU - Yang, Dejun

PY - 2016/2/23

Y1 - 2016/2/23

N2 - In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as an effective polynomial-time heuristic algorithm, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve over 79% energy savings on average compared to a widely-used baseline approach, and moreover, the proposed heuristic algorithm produces close-to-optimal solutions.

AB - In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as an effective polynomial-time heuristic algorithm, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve over 79% energy savings on average compared to a widely-used baseline approach, and moreover, the proposed heuristic algorithm produces close-to-optimal solutions.

UR - http://www.scopus.com/inward/record.url?scp=84964902391&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964902391&partnerID=8YFLogxK

U2 - 10.1109/GLOCOM.2014.7417136

DO - 10.1109/GLOCOM.2014.7417136

M3 - Conference contribution

SN - 9781479959525

BT - 2015 IEEE Global Communications Conference, GLOBECOM 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -