As edge devices grow smarter and application scenarios become more diverse, users' demands for lower latency and higher efficiency in data storage and processing have risen sharply. Individual edge devices and nodes are no longer sufficient to meet these expanding storage requirements. Consequently, developing efficient, low-latency, and cost-effective solutions for collaborative storage across edge devices and nodes has become a critical challenge. In this paper, we present a framework for the transaction and pricing of storage resources in an edge computing environment involving multiple edge service providers, to address trust and incentive issues in storage resource collaboration. Firstly, we propose a secure and decentralized storage resource trading mechanism by leveraging blockchain technology and smart contracts. We introduce Proof of Transaction Expectation (PoTE), an efficient, reliable, and lightweight consensus mechanism, to ensure transaction transparency, openness, and non-repudiation. Secondly, we introduce a game theory-based storage resource pricing model, where a leader interacts with multiple followers to optimize profits while maintaining service quality. To address dynamic pricing and storage resource allocation problems under incomplete information, we propose the Stackelberg Game Approach based on Multi-Agent Reinforcement Learning (SGA-MARL), which formulates the optimal pricing and trading share decisions in the two-stage Stackelberg game as a stochastic Markov Decision Process (MDP). Simulations and prototype testing validate the effectiveness of the proposed system, with results showing that the PoTE consensus achieves up to 40% higher throughput than Proof-of-Work while reducing latency by over 50% compared to PBFT, and the SGA-MARL algorithm improves leader profit by approximately 30% and resource satisfaction rates by over 80% compared to baseline methods like MA-PPO and DQN.

Systems that integrate distributed databases and existing blockchain platforms have recently emerged, which conveniently leverage their respective strengths to build efficient, secure, and usable data sharing and collaboration environments for different organizations. However, the performance of such systems can be limited by the native blockchain platforms due to the high latency of transactions. In this paper, we present CoralDB, a bottom-up fully redesigned hybrid system of blockchain and database, aimed at enabling untrusted organizations to collaborate and share data efficiently and securely at the database level. The storage layer of CoralDB ensures data security and system throughput through key modules such as customized block structure, consensus mechanism, and transaction pool. On top of the storage layer, a database layer is introduced, which extends the blockchain of the storage layer by incorporating connection pools, collaborative tables, and query interfaces, to enhance the usability and efficiency of data collaboration and sharing. Extensive experimental results demonstrate that CoralDB provides security assurances at the level of blockchain and enables efficient decentralized data collaboration and sharing.

Although many impressive works on learning-based camera ego-motion estimation methods have been proposed recently, most of them promote the accuracy of camera pose estimation by various sequential learning with loop closure optimization, while neglecting the improvement of PoseNet itself. In this paper, we focus on the coupling of rotation and translation in ego-motion estimation, and design a cascade decoupling structure to separately learn the rotation and translation of camera relative motion between adjacent frames. Meanwhile, a rigid-aware unsupervised learning framework with iterative pose refinement scheme is proposed for camera ego-motion estimation. It can disambiguate rigid motion and deformations in dynamic scenarios by jointly learning of optical flow, stereo disparity and camera pose. Validated with evaluation experiments on the public available datasets, our method is superior to the state-of-the-art unsupervised methods, and can achieve comparable results with the supervised ones.