Journal of Supercomputing (15730484)81(7)
Mobile Crowd Sensing (MCS)-based spectrum monitoring emerges to check the status of the spectrum for dynamic spectrum access. For privacy-preserving purposes, spectrum sensing reports may be sent anonymously. However, anonymous submission of reports increases the probability of fake reports by malicious participants. Also, it is necessary to assign a fair reward to encourage the honest participants, which needs to take into account participant’s reputation. In this research, a method is presented for MCS-based spectrum monitoring which uses Hyperledger Fabric and Identity Mixer (Idemix). This framework overcomes security challenges such as providing anonymity of the participants, identifying malicious participants, detecting intentional and unintentional incorrect reports, and providing a secure protocol to reward participants. An intuitive evaluation of the security features of the proposed method confirms that the proposed method withstands key threats, such as de-anonymization, participant misbehavior, privacy-compromising collusion among system entities, and reputation manipulation attack. Also, numerical evaluations show that the proposed method is superior compared to the similar centralized method in terms of delay when the number of participants is sufficiently large. Specifically, it achieves an average improvement of approximately 39% in scenarios involving 1000 to 2000 participants, and more than a twofold reduction in delay for the case with 2000 participants. Notably, this enhancement comes without a substantial increase in signaling overhead, which remains only slightly more than double that of the centralized method. Moreover, simulations show that the proposed method can successfully distinguish malicious participants from the honest ones in most scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
