Sybil-Resilient Publisher Selection Mechanism in Blockchain-Based MCS Systems

Abstract

In Blockchain-based Mobile CrowdSensing (BMCS) systems, publishers (data collectors) can exploit the ability to create multiple blockchain identities, enabling Sybil attacks. Selfish, malicious, and collusive Sybil behaviors undermine both reward and majority-based data validation mechanisms, discouraging honest participation and threatening system integrity. Existing solutions often fail to address these issues, particularly in environments dominated by selfish or malicious publishers. This article proposes a novel two-phase publisher selection mechanism to mitigate Sybil attacks in BMCS systems. Phase-I employs a modified Proof-of-Stake (PoS) mechanism with carefully calibrated parameters, including staked amount, coinage, reputation, and randomness. The strategic combination of staked amount and coinage increases the difficulty of Sybil attacks as the system scales over time. Phase-II introduces a lightweight, reputation-based Proof-of-Work (PoW) mechanism tailored for Mobile CrowdSensing (MCS) environments, where puzzle difficulty adjusts dynamically based on the publisher's reputation. Reputation and penalization mechanisms are central to the proposed mechanism, ensuring robust prevention of task domination, selfish behavior, and malicious activities while fostering honest participation. Comprehensive on-chain and off-chain simulations demonstrate the proposed mechanism's effectiveness in mitigating Sybil attacks, reducing their impact, and promoting fair participation.