A Dynamic Processing System for Sensor Data in IoT

With the development of the Internet of Things (IoT for short), innumerable Wireless Sensor Networks (WSNs) are deployed to capture the information of environmental status in the surrounding physical environment. The data from WSNs, called sensor data, are generated in high frequency. Similar to data of other open-loop applications, for example, network monitoring data, sensor data are heterogeneous, redundant, real-time, massive, and streaming. Hence, sensor data cannot be treated as the IoT business data, which brings complexity and difficulty to information sharing in the open-loop environment. This paper proposes a dynamic sensor data processing (SDP) system to capture and process sensor data continuously on the basis of data streaming technology. Particle Swarm Optimization (PSO) algorithm is employed to train threshold dynamically for data compression avoiding redundancy. With the help of rules setting, the proposed SDP is able to detect exception situations. Meanwhile, the storage models in SQL and NOSQL databases are analyzed and compared trying to seek an appropriate type of database for sensor data storage. The experimental results show that our SDP can compress sensor data through dynamically balancing the accuracy and compression rate and the model on NOSQL database has better performance than the model on SQL database.