To Tolerate or To Impute Missing Values in V2X Communications Data?

Abstract

Misbehavior detection is a critical task in vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (VANETs). However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communications data collected from simulated experiments. This article evaluates main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communications data. This article proposes two novel methods for imputing and tolerating missing data. The former is a novel imputation method that is based on the collaborative clustering and the latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster–Shafer fusion. The effectiveness of the proposed techniques is evaluated by the ground-truth vehicular reference misbehavior (VeReMi) data. Moreover, a multifactor amputation framework has been developed to induce missingness over V2X communications data with different missing ratios, mechanisms, and distributions. This provides a comprehensive benchmark resembling missingness over V2X communications data. The proposed methods are compared with five missing-tolerant and nine imputation methods. The attained results over the benchmark data indicate that the proposed missing-tolerant method is significantly better than other treatment methods in terms of accuracy and F-measure.