Layered blockchain-based mobile crowdsensing architecture: exploring privacy and scalability challenges across layers

Abstract

Blockchain technology has emerged as a transformative solution for addressing the limitations of traditional Mobile CrowdSensing (MCS) systems, which rely on centralized architectures. Despite its promise, the integration of blockchain into MCS introduces challenges related to privacy, scalability, and system efficiency. This paper presents a comprehensive layered architecture for enhancing blockchain-based MCS systems (BMCS), focusing on two critical dimensions: privacy and scalability. By categorizing challenges and proposed mitigation strategies, the study explores privacy risks arising from blockchain transparency and evaluates privacy-preserving mechanisms, including zero-knowledge proofs, multiparty computation, and homomorphic encryption, to protect sensitive data in decentralized environments. Scalability constraints, such as limited transaction throughput and resource intensity, are presented with targeted solutions that reduce on-chain loads and improve performance. The findings contribute actionable insights to advance BMCS systems, charting a path for resilient and scalable decentralized ecosystems.