Towards energy-efficient task scheduling on smartphones in mobile crowd sensing systems

Jing Wang, Jian Tang, Guoliang Xue, Dejun Yang

Research output: Contribution to journalArticlepeer-review

49 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 two effective polynomial-time heuristic algorithms, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve significant energy savings, compared to a widely-used baseline approach; moreover, the proposed heuristic algorithms produce close-to-optimal solutions.

Original languageEnglish (US)
Pages (from-to)100-109
Number of pages10
JournalComputer Networks
StatePublished - Mar 14 2017


  • Energy efficiency
  • Mobile crowd sensing
  • Smartphones
  • Task scheduling

ASJC Scopus subject areas

  • Computer Networks and Communications


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