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Mobile crowd sensing (MCS) user selection balances coverage area and budget. The MIA algorithm optimizes user subsets for maximum and balanced urban sensing, enhancing data collection efficiency.

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Mobile Computing

Background:

  • Mobile crowd sensing (MCS) enables urban-scale data collection using smartphone sensors.
  • Existing MCS approaches face challenges in balancing coverage area and user selection within budget constraints.

Purpose of the Study:

  • To address the coverage-balancing user selection (CBUS) problem in MCS under a budget constraint.
  • To develop a novel sensing utility model that integrates coverage area and balance.
  • To propose an efficient algorithm for optimal user subset selection.

Main Methods:

  • Developed a coverage balance-based sensing utility model.
  • Formally defined the CBUS problem.
  • Designed a heuristic algorithm (MIA) employing the maximum independent set model.
  • Implemented a fast method for calculating union coverage area.

Main Results:

  • The MIA algorithm effectively selects user subsets for large and balanced coverage.
  • The proposed utility model accurately captures MCS requester needs.
  • Experimental results demonstrate the efficacy of MIA in improving coverage balance and total area.

Conclusions:

  • The MIA algorithm provides an effective solution for the NP-hard CBUS problem.
  • The proposed approach enhances MCS campaign efficiency by optimizing user selection.
  • The methodology is applicable to various MCS scenarios focused on coverage optimization.