# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
### Challenges  
The biggest challenge at present is that current technologies and schemes cannot reasonably and effectively process the large amount of data generated in the industrial field. There are two technical schemes for processing industrial data at this stage. The first involves accessing industrial sites using embedded industrial gateways and collecting data through serial ports and servers [11; 12; 13]. The second scenario involves using a data terminal unit (DTU) to collect data and transfer them transparently to the cloud. However, both data collection schemes have many drawbacks:  
* High complexity of development: IIoT devices come with various I/O mechanisms due to their different types and development protocols, leading to diverse interfaces for communication between hardware and software. The communication between different devices can result in varying data structures, posing challenges for data integration and processing.
* Low development reuse rate: The lack of standardization in software and hardware is a common challenge in the IIoT field. With diverse industrial environments, constant efforts are required to develop and adapt new hardware and software solutions, leading to resource waste and high costs.
* Poor maintainability and mobility: The reliance on gateways for data collection and storage in the IIoT can result in limited independence between devices and applications. This can lead to maintenance challenges in case of any issues.  
Figure 1: In the context of the Industrial Internet of Things, large amounts of multidimensional data have been generated.  
* Poor privacy: The hardware equipment was acquired from a manufacturer. However, the cost was high and it is tightly integrated with the manufacturer's platform, which limits the user's control over data management.
* Datasets are too simplistic: Collecting data from different sensors using multiple devices can result in higher operating costs and more cumbersome processes.  
In order to solve the above problems, this paper proposed a new technical scheme based on the original scheme and combined with multisensor information fusion technology. The scheme is based on distributed sensor data generation and the fusion of a smart cloud edge network. The scheme is equipped with a data generation device, which is easy to install, has strong signal penetration, and has low delays and low energy consumption. Compared with traditional data collection schemes, this method has the following advantages:  
* Low complexity of development:The data collection and transmission devices utilize a unified I/O mechanism, development protocol, and data format type, which enables efficient and accurate communication between the hardware and software due to the same model being used.
* High development reuse rate: Users can adjust the peripheral devices of the equipment to suit different industrial scenarios, without the need for a redevelopment of the hardware, communications, and software.
* Good maintainability and mobility: The dataset generator has a small and easy-to-install shape, with strong independence that eliminates the need for a gateway and simplifies later debugging and maintenance of the data collection process.
* Good privacy: The hardware equipment used in this scheme is self-developed, giving users complete control over the collection and transmission of datasets without any restrictions from intermediate manufacturers in terms of data storage and transmission.
* Diversity of datasets: The collection device is capable of collecting and storing data information from various sensors simultaneously. Afterwards, mature synchronization algorithms can be used to create a more standardized dataset.