nternet of Vehicles (IoV) communication serves as a critical enabler for intelligent transportation systems, where the reliability of data sharing fundamentally determines overall system performance. Data reliability not only dictates the quality of IoV services but also constitutes a core factor in ensuring traffic safety and improving road operational efficiency. At present, IoV data sharing confronts several key challenges: (i) degradation of message credibility due to malicious attackers; (ii) ineffectiveness of conventional reputation mechanisms under high vehicular mobility; and (iii) channel congestion and information loss caused by redundant data transmissions. To address these issues, this paper proposes a Hybrid Hash Chord Protocol, which integrates Geohash geocoding with the Chord distributed lookup algorithm to establish a location-aware peer-to-peer data forwarding mechanism. This approach enables efficient data transmission with reduced hop count. In addition, a recursive traffic data filtering method is introduced to effectively suppress duplicate data reporting. Moreover, a Bayesian Dynamic Fading Reputation Model is developed, incorporating time decay factors and historical behavior weighting to mitigate intelligent attacks and node inertia, thereby enhancing road safety and traffic efficiency. Experimental results indicate that, within an IoV data-sharing context, the proposed model improves traffic efficiency by approximately 4% and reduces overall communication overhead by 36% compared to existing schemes. When the reputation threshold is set to 0.35, the model achieves a 100% malicious vehicle detection rate, whereas benchmark methods remain below 80% under the same threshold. In summary, the proposed framework achieves a notable balance among enhancing data trustworthiness, optimizing traffic performance, and minimizing communication costs, offering a practicable solution for building efficient and reliable IoV data-sharing systems.
Zhu, R., Xue, Z., Song, J., Liu, W., Helal, S., Zhang, X., et al. (2026). BTCP: A Blockchain-based Trusted Data Sharing Framework with Congestion Control and Proximity Evaluation for Internet of Vehicles. IEEE INTERNET OF THINGS JOURNAL, TBD, 1-13 [10.1109/JIOT.2026.3663192].
BTCP: A Blockchain-based Trusted Data Sharing Framework with Congestion Control and Proximity Evaluation for Internet of Vehicles
Helal S.Membro del Collaboration Group
;
2026
Abstract
nternet of Vehicles (IoV) communication serves as a critical enabler for intelligent transportation systems, where the reliability of data sharing fundamentally determines overall system performance. Data reliability not only dictates the quality of IoV services but also constitutes a core factor in ensuring traffic safety and improving road operational efficiency. At present, IoV data sharing confronts several key challenges: (i) degradation of message credibility due to malicious attackers; (ii) ineffectiveness of conventional reputation mechanisms under high vehicular mobility; and (iii) channel congestion and information loss caused by redundant data transmissions. To address these issues, this paper proposes a Hybrid Hash Chord Protocol, which integrates Geohash geocoding with the Chord distributed lookup algorithm to establish a location-aware peer-to-peer data forwarding mechanism. This approach enables efficient data transmission with reduced hop count. In addition, a recursive traffic data filtering method is introduced to effectively suppress duplicate data reporting. Moreover, a Bayesian Dynamic Fading Reputation Model is developed, incorporating time decay factors and historical behavior weighting to mitigate intelligent attacks and node inertia, thereby enhancing road safety and traffic efficiency. Experimental results indicate that, within an IoV data-sharing context, the proposed model improves traffic efficiency by approximately 4% and reduces overall communication overhead by 36% compared to existing schemes. When the reputation threshold is set to 0.35, the model achieves a 100% malicious vehicle detection rate, whereas benchmark methods remain below 80% under the same threshold. In summary, the proposed framework achieves a notable balance among enhancing data trustworthiness, optimizing traffic performance, and minimizing communication costs, offering a practicable solution for building efficient and reliable IoV data-sharing systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


