The Internet of Things (IoT), as a crucial direction in modern technological development, has been widely applied across multiple fields, from smart homes to industrial automation, and even smart cities, making IoT ubiquitous. The core of IoT lies in the interconnection of various devices and sensors to achieve data collection, transmission, and processing. However, achieving effective collaboration and data sharing among IoT devices, especially across different platforms, remains a significant challenge in current technology. This article will explore how to achieve cross-platform data sharing and application in IoT, analyze current technological bottlenecks, and propose some solutions.

1. Current Status of Cross-Platform Data Sharing and Application in IoT

1.1 The Need for Cross-Platform Data Sharing

With the proliferation of IoT technology, an increasing number of devices and systems require cross-platform data sharing. For example, smart home devices need to interact with mobile apps, cloud platforms, and other smart devices; sensors in industrial IoT need to share data with different control systems and analytical platforms. To enhance the collaborative efficiency of devices and systems, cross-platform data sharing in IoT has become particularly important.

Cross-platform data sharing not only reduces information silos and improves data utilization efficiency but also promotes interoperability among devices from different manufacturers, thereby increasing the application value of IoT. Therefore, achieving efficient and reliable cross-platform data sharing is one of the key issues in the current development of IoT technology.

1.2 Current Technological Bottlenecks

Despite the growing demand for cross-platform data sharing in IoT, many technical challenges remain in practice:

• Device Heterogeneity: IoT devices are diverse, with different manufacturers using various protocols, standards, and communication methods. The significant differences in data formats and interfaces across platforms pose substantial technical obstacles to cross-platform data sharing.

• Data Security and Privacy Protection: Cross-platform data sharing often involves data transmission and storage, requiring robust security measures. Ensuring smooth data flow while maintaining data privacy and security is a major challenge.

• Network Bandwidth and Latency Issues: IoT devices typically generate large amounts of data. Achieving low-latency data transmission under limited bandwidth conditions is a significant challenge for cross-platform sharing.

• Unified Data Management: Managing cross-platform data involves interface standards, data formats, and data models across different platforms, requiring strong data management capabilities.

These issues limit the promotion and widespread adoption of cross-platform data sharing and applications in IoT.

2. Key Technologies for Cross-Platform Data Sharing

2.1 Data Standardization and Protocol Unification

To achieve cross-platform data sharing in IoT, data standardization and protocol unification are essential. Differences in data formats between devices and platforms are often a primary cause of information silos and difficulties in data sharing.

• Standardized Data Formats: Currently, data formats such as JSON (JavaScript Object Notation) and XML (Extensible Markup Language) are widely used for IoT data transmission and exchange. Defining unified data formats can reduce the need for data conversion between different devices and platforms.

• Unified Communication Protocols: IoT devices and platforms use various communication protocols, such as MQTT (Message Queuing Telemetry Transport), CoAP (Constrained Application Protocol), and HTTP/HTTPS. To achieve cross-platform data sharing, adopting open-standard communication protocols like MQTT or CoAP, which are lightweight and suitable for IoT scenarios, can reduce resource consumption during data transmission and improve efficiency.

Through data format standardization and protocol unification, interoperability between devices and platforms is enhanced, providing a foundation for cross-platform data sharing.

2.2 Integration of Cloud Platforms and Edge Computing

In IoT applications, cloud platforms and edge computing are two important technological architectures. Cloud platforms offer robust data storage and processing capabilities, while edge computing enables real-time data processing and analysis at the device level. Combining these two can effectively address the challenges of cross-platform data sharing in IoT.

• Cloud Platforms: After IoT devices upload data to the cloud, the platform can manage, store, and analyze the data uniformly. Additionally, cloud platforms can provide API interfaces, enabling different platforms to access and share data through APIs.

• Edge Computing: By processing data closer to the devices, edge computing reduces latency and reliance on cloud bandwidth. After preliminary data processing at the edge nodes, only data requiring further analysis is uploaded to the cloud. Edge computing allows for more flexible data processing and transmission, thereby improving the efficiency of cross-platform data sharing.

The integration of cloud platforms and edge computing not only enhances IoT data processing capabilities but also improves the efficiency of data flow between different platforms.

2.3 Data Sharing and Security Management

In cross-platform data sharing, data security is a critical factor. Data may be vulnerable to network attacks during transmission or unauthorized access during storage. Therefore, ensuring data security and privacy is one of the core issues in IoT cross-platform data sharing.

• Encryption Technology: Encrypting data ensures that it is not tampered with or stolen during transmission. Commonly used encryption algorithms include symmetric and asymmetric encryption. Employing strong encryption algorithms can effectively protect data security.

• Identity Authentication and Access Control: In IoT platforms, identity authentication and access control among devices, users, and platforms are crucial. Mechanisms such as OAuth 2.0 and JWT ensure that only authorized devices and users can access data, thereby enhancing the security of data sharing.

• Blockchain Technology: Blockchain technology, with its decentralized and tamper-proof characteristics, provides reliable security for IoT data sharing. It ensures that data cannot be altered during cross-platform sharing and enables transparency and traceability.

By applying these security technologies, cross-platform data sharing in IoT can proceed smoothly while ensuring data security.

3. Application Scenarios of IoT Cross-Platform Data Sharing

3.1 Smart Homes

Smart homes are one of the most typical application scenarios for IoT. In smart homes, various household appliances, sensors, and control systems need to collaborate through data sharing. For example, a smart temperature control system adjusts the operation of air conditioners or heaters based on data from room temperature sensors; a smart lighting system automatically adjusts indoor lighting brightness based on user behavior data. These devices need to share data across different platforms.

Through standardized protocols and cloud platform support, smart home devices can easily achieve cross-platform data sharing and collaboration, enabling users to manage their home environments more intelligently.

3.2 Smart Healthcare

In the field of smart healthcare, cross-platform data sharing is equally important. For instance, patient data generated by various medical devices (such as ECG machines, blood pressure monitors, and glucose meters) in hospitals needs to be shared with medical information management systems and cloud platforms, allowing doctors to comprehensively understand patients' health conditions and develop treatment plans. Additionally, patients' health data can be shared with personal health management platforms via smart devices, enabling more precise health monitoring and management.

With the support of IoT technology, cross-platform data sharing in the healthcare industry can significantly improve the efficiency and quality of medical services.

3.3 Smart Transportation

Smart transportation systems are another important application area for IoT technology. In smart transportation systems, traffic sensors, vehicles, and road management platforms need to share data and collaborate. For example, sharing real-time traffic information between smart streetlights and traffic monitoring cameras can help traffic management systems optimize signal light control and alleviate traffic congestion.

Through cross-platform data sharing, smart transportation systems can operate more intelligently and efficiently, improving traffic flow and reducing accident rates.

4. Conclusion

Cross-platform data sharing and application in IoT are essential prerequisites for achieving intelligence and automation. With the support of technologies such as data standardization, the integration of cloud platforms and edge computing, and data security management, the technological bottlenecks of IoT cross-platform data sharing are gradually being resolved. In application scenarios like smart homes, smart healthcare, and smart transportation, the realization of cross-platform data sharing will significantly enhance the application value of IoT and drive the progress of societal intelligence. However, to further address practical application issues, continuous optimization of technical solutions and the promotion of industry standards are necessary to truly achieve comprehensive interconnection in IoT.