This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot’s adaptability and path optimization capabilities in dynamic environments. In particular, Re-DQN improves the stability of the training process through a dynamic incentive layer and achieves more comprehensive area coverage and shorter planning times in high-dimensional continuous state spaces. Simulation results show that Re-DQN outperforms the other algorithms in terms of performance, convergence speed, and stability, making it a robust solution for comprehensive coverage path planning. Future work will focus on testing and optimizing Re-DQN in more complex environments and exploring its application in multi-robot systems to enhance collaboration and communication.

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness.

Many IoT applications require users to share their devices’ location, and enhanced privacy-protection means sharing location anonymously, unlinkably and without relying on any administrators. But under such protection, it is difficult to trust shared location data, which may be from unregistered devices or from the same one’s multiple logins or from the cloned device ID, even be generated by an attacker without any devices! Such untrusted location sharing cheats system, misleads users, even attacks system. To the best of our knowledge, such problems have not been solved in a decentralized system. To solve them in one scheme, we put forward the first decentralized accumulator for device registration and construct the first practical decentralized anonymous authentication for device login. When logging in, the device provides a special knowledge proof, which integrates zero-knowledge (for privacy) with knowledge-leakage (for identifying abnormal behaviors) designing for blockchain (for decentralization). Therefore, in our system, only registered IoT devices can upload location data and their logins are anonymous and unlinkable, while login exceeding K times in a system period or cloning ID to login concurrently can be identified and tracked without any trusted centers. In addition, we provide the security proofs and the application examples of the proposed scheme. And the efficiency analysis and experimental data show that the performance of our scheme can meet the needs of real-world location sharing on IoT.