Iterative Spatial Crowdsourcing in Peer-to-Peer Opportunistic Networks

Abstract

Spatial crowdsourcing is a potentially powerful method for incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. This paper proposes and investigates task assignments and recruitment in iterative spatial crowdsourcing processes to find regions of particular interest among a collection of regions. We consider cases where associations between regions can be exploited to reduce costs and increase efficiency in crowdsourcing. We describe five approaches, incorporated into crowdsourcing algorithms, for reducing the cost (the number of queries required) and increasing the efficiency (reducing the number of rounds of querying required) in using such spatial crowdsourcing. We demonstrate the performance improvements gained using these approaches based on simulation scenarios. The findings show the interplay and relationships among our proposed approaches using a range of metrics including responses, energy consumption, costs, and time usage. These metrics are demonstrated via a range of scenarios, showing that our proposed approaches can lead to improved performance over randomly choosing regions for inquiry.