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A Real-Time Big Data Gathering Algorithm Based on Indoor Wireless Sensor Networks for Risk Analysis of Industrial Operations
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Date : 2017-08-25
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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.
 
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