Decentralized IoT Data Management via Peer-to-Peer Blockchain

The patent segments the centralized database into multiple distributed nodes across a peer-to-peer network. Each node stores portions of the IoT data locally, eliminating the single-point bottleneck. This segmentation enables horizontal scalability as new nodes can be added to the network without requiring centralized coordination, directly resolving the scalability limitation while maintaining operational simplicity through automated peer discovery and data routing.

The patent introduces a new dimensional approach by organizing data around spatial proximity and temporal relevance rather than traditional centralized hierarchical structures. Data is stored in log files distributed across edge machines in the same dimension where it was generated, enabling efficient querying through spatial-temporal indexing without requiring data movement across network dimensions, thus improving scalability while maintaining management simplicity.

The patent applies local quality by enabling each peer node to maintain local copies of relevant data and local indexes in log files. When queries are executed, the system first checks local storage before distributing query requests across the network. This local caching strategy reduces data transfer costs by serving frequently accessed data locally while maintaining querying simplicity through transparent fallback to distributed sources when data is not locally available.

The patent implements copying by creating replicated copies of IoT data across multiple peer nodes in the network. Instead of centralizing all data in data centers, the system distributes copies to edge machines where the data was generated or is relevant. This copying strategy reduces data transfer costs by placing data closer to query sources while maintaining querying simplicity through automatic replication management and consistent data synchronization protocols.

The patent segments the massive IoT data volume into distributed log files stored across multiple peer nodes rather than consolidating all data centrally. Each node stores only the data relevant to its local context, achieving comprehensive data retention through distributed segmentation. This approach enables complete data analysis by querying across segmented nodes while avoiding storage capacity bottlenecks by eliminating the need for a single centralized storage infrastructure.

The patent applies local quality by enabling each peer node to maintain locally-relevant data in log files with local indexes. The system prioritizes storing complete data locally at the edge where it was generated, rather than transferring all data centrally. This local quality strategy ensures data completeness for local analysis while reducing overall storage requirements by eliminating redundant centralized storage, thus resolving the contradiction between data completeness and storage capacity.