Towards scalable indoor positioning systems (IPS): User-centric challenges, methods, and recommendations for user-friendly crowd-powered framework

Crowd-powered Indoor Positioning Systems (IPS) offer a cost-efficient and scalable alternative to traditional site-survey-based methods for generating the offline prerequisites of ubiquitous, measurement-driven IPS. However, the widespread adoption of such paradigms depends on resolving critical user-centric challenges that span all layers of the crowd-powered architecture. This survey provides a systematic investigation of these challenges, including user participation schemes, incentive mechanisms, privacy and security threats, and the impact of data collection and localization on user devices. To the best of our knowledge, this is the first in-depth review that examines these issues and their implications for data quality, reliability, and scalability, with a specific emphasis on user-friendliness. It maps these challenges across the architectural layers of crowd-powered IPS, reviews prior studies to analyze the user’s role and assigned tasks in active, opportunistic, and passive participation schemes, emphasizing the objectives of these tasks and the trade-offs associated with each scheme. Next, it distinguishes incentive mechanisms in crowd-powered IPS from those in other domains, highlighting how intrinsic and extrinsic motivations can be aligned with IPS-specific objectives. It then surveys the mathematical models employed in current incentive mechanisms, along with their goals and limitations. Subsequently, it reviews the privacy and security risks, the preservation techniques proposed in existing literature, and their shortcomings. In addition, the survey discusses the adverse impacts of data collection and localization on user devices, identifying potential user burdens and associated mitigation strategies. Finally, it outlines a roadmap of recommendations for developing user-friendly, sustainable, and scalable IPS.