# A Multidimensional Data Collection and Edge Computing Analysis Method
###### Abstract  
With the development of IoT technology, many dimensions of data are generated in the environment where we live. The study of these data is critical to our understanding of the relationships between people and between people and cities. The core components of IoT technology are sensors and control circuits. However, merging various sensor data and real-time data processing is often a difficult problem, usually related to factors such as coverage, lighting conditions, and accuracy of object detection. Therefore, we firstly propose a wireless transmission hardware architecture for data acquisition mainly based on vision sensors, and at the same time, incorporate some sensors for data calibration to improve the accuracy of data detection. The collected data are fed back to the edge computing platform for fast processing. The edge platform is designed with a lightweight target detection model and data analysis model. Through this multidimensional data collection and analysis, a generalised functional model for public space utilization can be fitted, which enables the calculation of utilization rates for any parameter in public space. The technology improves a technical reference for multi-dimensional data collection and analysis. edge computing; sensors; target detection; utilization of space +
Footnote †: journal: Applied sciences  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0  
delay in real-time monitoring of public spaces and processing of data, imposing elevated equipment requirements. Consequently, its implementation in public space research proves challenging. In instances of emergencies, prompt processing becomes unattainable. As an emerging computing paradigm succeeding cloud computing, edge computing decentralizes computations to the network's periphery, in close proximity to users and data sources. It furnishes data caching and processing capabilities, boasting attributes of low latency, heightened security, and spatial awareness [6]. Edge computing excels in facilitating real-time data processing within public spaces. However, its front-end necessitates a meticulously designed pre-processing module, integrating sensors for seamless execution of data collection and fusion. Wireless sensor networks (WSNs) have evolved to cater to expansive deployments within urban landscapes and diverse environments alike [7]. By employing wireless sensor networks, the need for physical cabling can be eliminated, enabling sensor nodes to be strategically positioned in every corner of urban spaces [8]. The wireless sensor network is very suitable for urban public space research. At the same time, this architecture can comprehend intelligent instrument monitoring [9], security monitoring [10], medical security [11] and other aspects of the municipality. Currently, within the post-utilization evaluation phase of urban public space planning, the range of pertinent quantitative evaluation data encompasses questionnaire surveys, behavioral observations, on-site interviews, image records, literature reviews, and various other sources [12]. Relying on manual sorting, the efficiency is moderate, and the acquisition of data is considerably affected by external factors of time, space, and personnel, and it is hard to cover the entire space, time, and population used [13]. The effective integration of the Internet of Things [14; 15] and edge computing [16; 17; 18] can resolve the bottleneck of data collection in urban public space research. In future research endeavours, there is an urgent need for a low-cost adaptive monitoring technique that avoids the overhead of collecting data in public spaces. The edge computing platform hosts a tailored lightweight target detection model, efficiently scrutinizing public space parameters. This deep learning algorithmic research effectively mitigates data redundancy resulting from multifaceted sensor data collection in public space studies.