WiMi Proposes A Vehicular Networks-based Consensus Algorithm to Improve Data Security And Response Speed
In recent years, autonomous vehicles (AVs) have attracted significant attention as an evolving technology for intelligent transportation systems (ITS). These vehicles usually have various onboard resources, such as sensors, radar, cameras, storage devices, event recorders, etc. These devices perform different operations, such as object detection, congestion monitoring, pathfinding, etc. Self-driving vehicles will capture large amounts of data for analysis and make real-time intelligent decisions based on surrounding events. AVs equipped with sensors can capture gigabytes of data, which needs to be processed using complex machine-learning algorithms to infer logical outcomes. For communication efficiency, storage, and high-end processing, 5G and 6G technologies and roadside units (RSUs) connected to a Mobile Edge Computing (MEC) server can be used to receive all the data sent by the vehicle, where the MEC server runs machine learning techniques to generate useful predictions.
WiMi's VBCA solution, a lightweight decentralized ledger system, allows easy integration of recent blocks with existing P2P networks. It aims to provide a network for physical layer vehicles and devices that can share information efficiently and reliably. The solution reduces communication latency by combining blockchain with P2P networks, enabling the network to combine all active blocks into a lightweight blockchain. The scheme uses a hierarchical architecture to achieve an efficient consensus mechanism. Fixed nodes are responsible for attaching blocks to the blockchain, and all fixed nodes store copies of the blockchain. The scheme design estimates the number of active and inactive blocks in the network and maintains only active blocks instead of full blocks to improve the lightweight property.
In WiMi's VBCA scheme, the system architecture nodes are divided into two node types: fixed nodes and mobile nodes. Fixed nodes are RSUs that provide geographic coverage for specific areas on the map by connecting to high-power edge servers and are interconnected via backhaul links. Mobile nodes use their sensors to capture event data and send it to the nearest fixed node. Through P2P networks, vehicles can use DSRC to connect more reliably to nearby RSUs, thereby reducing communication latency.
The consensus algorithm runs on the edge server and appends the verified protocol information to the blockchain stored on the edge server. Since vehicles are equipped with different types of sensors, e.g., self-driving cars can be equipped with cameras, radar, etc., the edge server will receive a large amount of data. Based on the collected data, various statistical and machine-learning tools can be applied to train models that generate multiple predictions for different applications. For example, a predictive learning-based approach can predict the expected load on numerous parts of the traffic network during a specific time window. The prediction information can be stored in a separate blockchain and shared among all fixed nodes through which vehicles can query the information. The system is built based on a network of vehicles and edge servers for managing traffic data and predicting traffic flow. The system works in five layers: application layer, contract layer, consensus layer, network layer, and data layer. The application layer provides the user interface that allows end users (vehicles) to perform general input/output operations. The contract layer verifies the authentication of vehicles and fixed nodes and deploys intelligent contracts. The consensus layer uses custom consensus algorithms to establish trust between nodes in the network. The network layer connects all nodes in a hybrid P2P fashion, with each node using a discovery protocol to find nearest neighbor RSUs to establish links and exchange messages. The data layer manages the protocols and blocks in the ledger, using tools such as hash functions, timestamps, and Merkle trees to ensure data integrity and security.
Furthermore, intelligent contracts ensure decentralization in the framework by allowing multiple fixed nodes to attach a block to the ledger. This algorithm technology significantly increases throughput and the number of blocks created by each node while ensuring decentralization. In addition, transaction latency is reduced by separating the protocol confirmation and block creation processes.
WiMi has several technologies used in autonomous driving, intelligent transportation systems, and smart cars. With the development of autonomous driving and intelligent transportation systems, vehicular networks are becoming increasingly important. The global market size for autonomous driving and intelligent transportation systems is expanding and is expected to grow in the coming years. This provides a broad market space for WiMi's VBCA technology. In addition, with the development and popularization of 5G and 6G technologies, vehicular network data transmission speed and stability will be further improved, further promoting the application and development of consensus algorithm technology based on vehicular networks.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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