Lessons Learned from Developing a Microservice Based Mobile Location-Based Crowdsourcing Platform

TL;DR

This paper presents QRowdsource, a microservices-based framework for mobile location-based crowdsourcing research, addressing the lack of real-world data and facilitating scalable experimentation.

Contribution

It introduces a novel microservices architecture for crowdsourcing experiments, detailing design, development, and lessons learned from implementation.

Findings

QRowdsource enables scalable, modular experimentation.

Microservices approach offers flexibility and ease of deployment.

Lessons learned inform future development of crowdsourcing platforms.

Abstract

Research in Mobile Location-Based Crowdsourcing is hindered by a marked lack of real-world data. The development of a standardized, lightweight, easily deployable, modular, composable, and most of all, scalable experimentation framework would go a long way in facilitating such research. Conveniently, these are all salient characteristics of systems developed using a microservices approach. We propose QRowdsource - a MLBC experimentation framework built using a distributed services architecture. In this paper, we discuss the design and development of QRowdsource, from the decomposition of functional components to the orchestration of services within the framework. We also take a look at how the advantages and disadvantages of using a microservices approach translate to our specific use case and deliberate over a number of lessons learned while developing the experimentation framework.