An Incentive-Compatible Crowdsensing Framework for Verifiable Accessible Routes

Navigating urban environments remains a profound challenge for wheelchair users due to the dynamic and often obstructive nature of accessible pathways. Current navigation systems are typically limited by static data, a lack of real-time validation, and insufficient incentives for sustained community contribution. This paper presents ConnectedWheels, a novel ecosystem that synergizes IoT-driven crowdsensing, a robust application-layer Proof-of-Reputation consensus mechanism, and a blockchain-anchored token economy to create a dynamic, trustworthy, and scalable platform for accessible urban mobility. The system utilizes a mobile application to passively collect real-time sensor data (GPS, accelerometer, gyroscope) from users, characterizing traversed paths as accessible and assessing their quality via an on-device machine learning model. These crowdsensed data streams are combined with manually logged Points of Interest (POIs), all of which are validated through a novel reputation-weighted voting system. Contributions and accurate validations are rewarded with a native utility token, fostering a sustainable, incentive-driven community. All data transactions and token distributions are immutably recorded on a blockchain, ensuring transparency, integrity, and auditability. An implementation was tested in a controlled environment, demonstrating truth discovery, efficient data handling and the feasibility of the integrated solution. The results confirm ConnectedWheels as a viable, next-generation solution for transforming urban mobility through verifiable, community-powered intelligence.