Leveraging GPS-less sensing scheduling for green mobile crowd sensing

Xiang Sheng, Jian Tang, Xuejie Xiao, Guoliang Xue

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


In this paper, we consider leveraging GPS-less energy-efficient sensing scheduling for mobile crowd sensing. We present a probabilistic model for sensing coverage without accurate location information (provided by GPS), based on which we formally define the Energy-constrained Maximum Coverage Sensing Scheduling (E-MCSS) problem for maximum coverage and the Fair Maximum Coverage Sensing Scheduling (F-MCSS) problem for fairness. Assuming that moving trajectories of mobile users are known beforehand, we present a (1-1/e)-approximation algorithm and a 1/2-approximation algorithm to solve the E-MCSS and F-MCSS problems in polynomial time, respectively, which can serve as benchmarks for performance evaluation. Under realistic assumptions, we present a GPS-less energy-efficient protocol for sensing scheduling based on the proposed algorithms. We developed an Android-based mobile crowd sensing system, on which we implemented the proposed protocol. Simulation results and experimental results (from a field test) are presented to validate and justify effectiveness of the proposed algorithms and protocol.

Original languageEnglish (US)
Article number6847102
Pages (from-to)328-336
Number of pages9
JournalIEEE Internet of Things Journal
Issue number4
StatePublished - Aug 1 2014


  • Collaborative sensing
  • energy-efficiency
  • mobile crowd sensing
  • scheduling

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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