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Crowdsourcing has become a popular method for involving humans in socially-aware computational processes. This paper proposes and investigates algorithms for finding regions of interest using mobile crowdsourcing. The algorithms are iterative, using cycles of crowd-querying and feedback till specified targets are found, each time adjusting the query according to the feedback using heuristics. We describe three (computationally simple) heuristics, incorporated into crowdsourcing algorithms, to reducing the costs (the number of questions required) and increasing the efficiency (or reducing the number of rounds required) in using such crowdsourcing: (i) using additional questions in each round in the expectation of failures, (ii) using neighbourhood associations in the case where regions of interest are clustered, and (iii) modelling regions of interest via spatial point processes. We demonstrate the improved performance of using these heuristics using a range of stylised scenarios. Our research suggests that finding in the city is not as difficult as it can be, especially for phenomena that exhibit some degree of clustering.

Heuristics for spatial finding using iterative mobile crowdsourcing