## 1 Introduction  
In the current era of big data, the exponential increase in data volume has spurred researchers to explore the application of big data that include sensitive and private information [1; 2; 3]. The integrity of data is crucial to ensure that accurate knowledge can be extracted from secure big data analysis. Regular collection and analysis of spatiotemporal data are carried out in the big data environment. However, the escalating security risks (e.g., big data privacy, quality, and security mechanisms) pose a significant threat to these applications [4]. In the storage of big data, spatiotemporal big data are commonly stored in a distributed file system. In a distributed storage system, multiple nodes must collaborate to perform specific tasks. As such, compromising one or more nodes can undermine the accuracy of computation results, and management becomes more complex. Consequently, traditional asymmetric and symmetric encryption techniques are challenging to directly apply in big data storage solutions [5]. In light of this, existing big data platform audits have become inadequate, leaving them vulnerable to numerous security challenges. Ensuring spatiotemporal big data validity and signatures is crucial to secure the storage of big data [6]. Data integrity becomes a primary security concern for users since they no longer have control over their data once they upload them. Cloud storage enables applications to effectively manage their remote data but is also vulnerable to tampering. In cloud storage, directly encrypting large data sets increases the risk of key management and requires significant computing overhead due to the large scale, large increment, and fast change of data sets [7]. If the secret key is exposed, the entire data set can be compromised and stolen. Blockchain technology has drawn attention from academia and industry due to its characteristics, such as immutability, confidentiality, integrity, and authorization [8]. In reference to the literature [9], a public audit scheme was developed for verifying the integrity of cloud storage data using blockchain technology. Data owners store lightweight verification tags on the blockchain and generate proofs by building a Merkle hash tree using these tags to minimize computation and communication overhead during integrity verification. The literature [10] proposes a blockchain-based multirobot collaborative application framework and potential benefits to combat the COVID-19 epidemic, such as monitoring and outdoor and hospital end-to-end delivery systems. The challenges and opportunities of integrating blockchain, multirobot collaboration, and the Internet of Things to address COVID-19 and future pandemics were also discussed. The researchers propose a conceptual framework to address digital twin collaboration needs through the use of blockchain, predictive analytics, and digital twin technology [11], a ledger-based digital twin framework that focuses on real-time operational data analysis and distributed consensus algorithms. It also describes how the conceptual framework can be applied using use cases of intelligent transportation systems, namely, intelligent logistics and railway predictive maintenance. A novel solution for big data security was proposed by [PERSON] et al. in 2022 [12], which utilized blockchain technology and fragmentation to construct a security framework. In this framework, data fragments are distributed and stored within the big data environment to add an additional layer of data protection. [PERSON] et al. presented the blockchain-based secure storage mechanism, which is designed to address the issue of limited blockchain storage capacity. The model employs an on-chain and off-chain collaborative storage approach, where on-chain and off-chain security authentication protocols are constructed using updatable subvector commitments, ensuring the consistency of on-chain and off-chain data, and enabling batch processing capabilities [13].