The registration of multi-resolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multi-resolution remote sensing image registration. Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor. Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration. We performed comprehensive experiments on four sets of multi-resolution optical remote sensing images and two sets of synthetic aperture radar images. The results demonstrate that the proposed registration model can achieve sub-pixel registration. The relative registration accuracy improved by 1.6 − 7.5%, whereas the overall performance improved by 4.5 − 14.1%.

Ride-sharing services, such as ride-hailing and carpooling, have become attractive travel patterns for worldwide users. Due to the high dynamic topology, heterogeneous wireless communication mode, and centralization, the Internet of Vehicles (IoV) is much more vulnerable to security issues such as privacy theft, single point of failure, data island, and unauthorized access, resulting in great security risks, while ride-sharing services provide convenience. Blockchain technology used to solve the security problems of the IoV has become a current research hotspot, including authentication and privacy protection. Nevertheless, the existing algorithms still face challenges such as large amount of computation, low throughput, low scalability, consensus, and node security. Achieving an efficient, lightweight, and scalable secure blockchain–based IoV system still needs to be solved urgently. In this paper, we propose an effective consensus algorithm called Modified Proof of Reputation (MPoR). Firstly, by using the average network access time of the whole network nodes as the filtering threshold, the number of consensus nodes can be controlled adaptively. Then, a new multiweight reputation algorithm is proposed to quantify the reputation value of nodes, so as to detect and eliminate malicious nodes in the consensus node pool. Theoretical analysis and extensive simulation experiments reflect that under the IoV scenario, MPoR can adaptively select the number of consensus nodes, to effectively improve the consensus efficiency. When malicious nodes are less than 1/3 of the total nodes in the network, MPoR can effectively resist latent attack and collusive attack and has strong robustness.

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Privacy-preserving task assignment is vital to assign a task to appropriate workers and protect workers’ privacy or task privacy for spatial crowdsourcing (SC). Existing solutions usually require each worker to travel to the task location on purpose to perform this task, which fails to consider that workers have specific trajectories and carry out the task on their way in a hitchhiking manner. To this end, this paper proposes a privacy-preserving hitchhiking task assignment scheme for SC, named PKGS. Specifically, we formulate the privacy-preserving hitchhiking task assignment as a decision problem of the relationship between dot and line under privacy protection. In particular, we present a privacy-preserving travel distance calculation protocol and a privacy-preserving comparison protocol through the Paillier cryptosystem and the SC framework. Results of theoretical analysis and experimental evaluation show that PKGS can not only protect the location privacy of both each worker and the task simultaneously but also assign the task to the worker holding a minimum travel distance. In contrast to prior solutions, PKGS outperforms in the computation of travel distance and task assignment.

Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it is essential to consider how to protect the security of the data itself. Existing studies have evaluated the reliability of interactive data through reputation quantification, but there are still some issues in the design of secure reputation management schemes, such as its low efficiency, poor security, and unreliable management. Aiming at the above-mentioned issues, in this paper we propose an effective VANET model with a secure reputation based on a blockchain, and it is called the double-layer blockchain-based reputation evaluation & management model (DBREMM). In the DBREMM, we design a reputation management model based on two parallel blockchains that work collaboratively, and these are called the event chain and reputation chain. A complete set of reputation evaluation schemes is presented. Our schemes can reduce observation errors and improve evaluation reliability during trust computation by using direct trust calculation based on the multi-factor Bayesian inference. Additionally, we propose an indirect trust calculation based on the historical accumulated reputation value with an attenuation factor, and a secure a reputation fusion scheme based on the number threshold with the fluctuation factor, which can reduce the possibility of attacks, such as collusive attacks and false information injection. Theoretical analysis and extensive simulation experiments reflect the DBREMM’s security algorithm effectiveness, accuracy, and ability to resist several attacks.