Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Abstract

Among the emerging concepts in the context of IoT, crowdsourcing, and crowdsensing are known as two critical building blocks on the intersection point of things and human-based techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this paper, we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence, we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.