To address the problem of inadequate feature extraction and binary code discrete optimization faced by deep hashing methods using a relaxation-quantization strategy, a novel deep attention-guided hashing method with pairwise labels (DAHP) is proposed to enhance global feature fusion, better learn the contextual information of image features to effectively enhance the feature representation, and solve the problem of losing feature information in discrete optimization by optimizing the loss function. First, we introduce a new concept called the anchor hash code generation(AHCG) algorithm, we train the ResNet with the position attention and channel attention mechanisms with the anchor points in Hamming space as supervised information, we fit the binary code representing the picture to the vicinity of each anchor point, and finally, we use the optimized loss function to calculate the pairwise loss and the anchor loss, allowing the hash function to generate hash code with strong discriminative power. The experiments were conducted on four benchmark datasets, and the retrieval accuracy of the proposed method outperformed the retrieval accuracies of the state-of-the-art methods.

Under a high-density traffic environment in a vehicular ad hoc network (VANET), many safety messages will not be verified in time and then get dropped. To touch such an issue appropriately, we aim at improving the efficiency of message verification and the number of verified safety messages. In our proposed scheme, messages are properly classified and prioritized first so that more important basic safety messages can be verified earlier. To improve batch verification, a dynamic batch verification scheme with an adjustable batch size is designed accordingly. The batch size is halved when failures of batch verification occur consecutively over a certain threshold to improve the efficiency of batch verification. On the contrary, the batch size is doubled when successes of batch verification occur consecutively over a certain threshold to increase the messages in a batch to be verified. For a failure of batch verification, a divide-and-conquer approach is utilized to find the illegitimate message(s). Via analysis, the superiority of our design over some closely related schemes in terms of verification delay and number of verified basic safety messages etc. is successfully demonstrated. By simulation, performance of our design and some closely related schemes is further investigated in realistic scenarios.