A Real-Time Big Data Gathering Algorithm Based on Indoor Wireless Sensor Networks for Risk Analysis of Industrial Operations

The era of big data has begun and an enormous amount of real-time data is used for the risk analysis of various industrial applications. However, a technical challenge exists in gathering real-time big data in a complex indoor industrial environment. Indoor wireless sensor networks (WSNs) technology can overcome this limitation by collecting the big data generated from source nodes and transmitting them to the data center in real time. In this study, typical residence, office, and manufacturing environments were chosen. The signal transmission characteristics of an indoor WSN were obtained by analyzing the test data. According to these characteristics, a realtime big data gathering (RTBDG) algorithm based on an indoor WSN is proposed for the risk analysis of industrial operations. In this algorithm, sensor nodes can screen the data collected from the environment and equipment according to the requirements of risk analysis. Clustering data transmission structure is then established on the basis of the received signal strength indicator (RSSI) and residual energy information. Experimental results show that RTBDG not only uses the limited energy of network nodes efficiently, but also balances the energy consumption of all nodes. In the near future, the algorithm will be widely applied to risk analysis in different industrial operations.