Citation

Hamrouni A, Ghazzai H, Frikha M, Massoud Y. IEEE Trans. Comput. Soc. Syst. 2020; 7(2): 477-491.

Copyright

(Copyright © 2020, Institute of Electrical and Electronics Engineers, Inc.)

DOI

10.1109/TCSS.2020.2967585

PMID

unavailable

Abstract

The widespread use of advanced mobile devices has led to the emergence of a new class of mobile crowdsourcing called spatial mobile crowdsourcing (SMCS). The main feature of SMCS is the presence of spatial tasks that require workers to be physically present at a particular location for task fulfillment. These tasks usually take advantage of the built-in sensors in mobile devices by requesting environment sensing services. Because cameras are becoming the most common way for visual logging techniques and sensing in our daily lives, we propose, in this article, a photo-based SMCS framework for event reporting. The proposed framework allows event report requesters to solicit photos of ongoing events and keep track of any updates. We propose a full architecture in which we solve the SMCS recruitment problem using different fairness strategies in the presence of multiple events and reporters. Then, once submissions are received and before forwarding final responses to event requesters, we proceed with a data processing phase for data quality monitoring. In short, our event reporting platform helps requesters recruit ideal reporters, select highly relevant data from an evolving picture stream, and receive accurate responses. This solution mainly incorporates: 1) a strategic and generic recruitment algorithm for recruiting and scheduling suitable reporters to events; 2) a deep learning model that eliminates false submissions and ensures photo's credibility; and 3) an A-tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Experiment results investigate the performances of the proposed recruitment approach and show that our algorithm outperforms two other benchmarking approaches. Also, we conduct simulations to evaluate the strategies of the proposed recruitment algorithm, given different fairness levels among events. Data quality simulation results show effectiveness in reducing false submissions and delivering high-quality responses. Finally, framework implementation for real-world applications is provided.

Keywords

Cameras; Crowdsourcing; Data quality; deep learning; event reporting; Mobile handsets; Monitoring; recruitment; Recruitment; Sensors; spatial mobile crowdsourcing (SMCS); Task analysis