Enabling green mobile crowd sensing via optimized task scheduling on smartphones

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

Research output: Contribution to journalConference Articlepeer-review

3 Scopus citations


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)
Article number7417136
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2015
Event58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States
Duration: Dec 6 2015Dec 10 2015

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


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