Modeling Misbehavior Detection Timeliness in VANETs

Autor(es)

de Lucena, Mateus Martinez ; Frohlich, Antonio Augusto ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE 27TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA); v. N/A, p. 8-pg., 2022-01-01.

Resumo

Autonomous vehicles are complex Cyber-Physical Systems with strict timeliness constraints, where failure to meet expectations can lead to life-threatening situations. Misbehavior Detection Algorithms are designed to detect and filter misbehaving devices, thus avoiding the consumption of erroneous data. However, these detection algorithms tend to incur processing delays that may infringe on the timely consumption of the data before their expiry. The variable data volume and possible contention due to shared resources with other processes typical of VANETs can lead to even longer processing delays, where data would expire before verification is complete. We propose a Cooperative Misbehavior Detection Framework, where property monitors supervise data expiry. Misbehavior detection algorithms are modeled through Signal Temporal Logic. Property monitors are defined to verify, at run-time, that the timeliness and data correctness of the detection is respected before allowing data consumption. We model a VANET use-case with a threshold algorithm and simulate varying vehicle densities in the Luxembourg SUMO Traffic scenario. High vehicle density showed an increase in expired data due to the processing delay caused by the data volume. Nevertheless, the accuracy for each vehicle density was: low 96.508%, medium 98.348% and high 92.439%. (AU)

• Processo FAPESP: 20/05142-1 - Gateway seguro para a internet das coisas industriais
• Beneficiário: Antônio Augusto Medeiros Fröhlich
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE