Biblio

Platoon is one of cooperative driving applications where a set of vehicles can collaboratively sense each other for driving safety and traffic efficiency. However, platoon without security insurance makes the cooperative vehicles vulnerable to cyber-attacks, which may cause life-threatening accidents. In this paper, we introduce malicious attacks in platoon maneuvers. To defend against these attacks, we propose a Cyphertext-Policy Attribute-Based Encryption (CP-ABE) based Platoon Secure Sensing scheme, named CPSS. In the CPSS, platoon key is encapsulated in the access control structure in the key distribution process, so that interference messages sending by attackers without the platoon key could be ignored. Therefore, the sensing data which contains speed and position information can be protected. In this way, speed and distance fluctuations caused by attacks can be mitigated even eliminated thereby avoiding the collisions and ensuring the overall platoon stability. Time complexity analysis shows that the CPSS is more efficient than that of the polynomial time solutions. Finally, to evaluate capabilities of the CPSS, we integrate a LTE-V2X with platoon maneuvers based on Veins platform. The evaluation results show that the CPSS outperforms the baseline algorithm by 25% in terms of distance variations.

Traditional image compressed sensing (CS) coding frameworks solve an inverse problem that is based on the measurement coding tools (prediction, quantization, entropy coding, etc.) and the optimization based image reconstruction method. These CS coding frameworks face the challenges of improving the coding efficiency at the encoder, while also suffering from high computational complexity at the decoder. In this paper, we move forward a step and propose a novel deep network based CS coding framework of natural images, which consists of three sub-networks: sampling sub-network, offset sub-network and reconstruction sub-network that responsible for sampling, quantization and reconstruction, respectively. By cooperatively utilizing these sub-networks, it can be trained in the form of an end-to-end metric with a proposed rate-distortion optimization loss function. The proposed framework not only improves the coding performance, but also reduces the computational cost of the image reconstruction dramatically. Experimental results on benchmark datasets demonstrate that the proposed method is capable of achieving superior rate-distortion performance against state-of-the-art methods.