Published October 9, 2020 | Version camera ready
Journal article Open

A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing with Diverse Sensing Costs

  • 1. National University of Defense Technology, University of Amsterdam
  • 2. National University of Defense Technology
  • 3. University of Amsterdam


The Internet of Things (IoT) and mobile techniques

enable real-time sensing for urban computing systems. By recruiting

only a small number of users to sense data from selected

subareas (namely cells), Sparse Mobile Crowdsensing (MCS)

emerges as an effective paradigm to reduce sensing costs for

monitoring the overall status of a large-scale area. The current

Sparse MCS solutions reduce the sensing subareas (by selecting

the most informative cells) based on the assumption that each

sample has the same cost, which is not always realistic in realworld,

as the cost of sensing in a subarea can be diverse due to

many factors, e.g. condition of the device, location, and routing

distance. To address this issue, we proposed a new cell selection

approach consisting of three steps (information modeling, cost

estimation, and cost-quality beneficial cell selection) to further

reduce the total costs and improve the task quality. Specifically,

we discussed the properties of the optimization goals and modeled

the cell selection problem as a solvable bi-objective optimization

problem under certain assumptions and approximation. Then,

we presented two selection strategies, i.e. Pareto Optimization

Selection (POS) and Generalized Cost-Benefit Greedy (GCBGREEDY)

Selection along with our proposed cell selection

algorithm. Finally, the superiority of our cell selection approach

is assessed through four real-life urban monitoring datasets

(Parking, Flow, Traffic, and Humidity) and three cost maps (i.i.d

with dynamic cost map, monotonic with dynamic cost map and

spatial correlated cost map). Results show that our proposed

selection strategies POS and GCB-GREEDY can save up to

15.2% and 15.02% sample costs and reduce the inference errors

to a maximum of 16.8% (15.5%) compared to the baseline-

Query by Committee (QBC) in a sensing cycle. The findings

show important implications in Sparse Mobile Crowdsensing for

urban context properties.



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