Fault diagnosis algorithm in VANETs using compressive sensing approach

Fault diagnosis is difficult especially in resourceconstraint, high mobility VANET environment. Due to several reasons, it is hard to achieve accuracy and efficiency at the same time. The idea of compressive sensing has shown that for some sparse condition. We can get enough accuracy by performing less measurements. In this paper, we present a fault diagnosis algorithm based on compressive sensing. The algorithm uses random walk to generate measurement matrix because it is easy to implement in practice. Simulations indicates that the algorithm performs well in fault diagnosis even the number of faults grows. Besides it can decrease its impact on common business by controlling the walks used.