| Summary: | 碩士 === 國立臺北科技大學 === 電機工程研究所 === 105 === Wireless Sensor Networks (WSNs) have become more and more mature in energy-efficiency technology. However, due to the power restriction, the sensor nodes will eventually face the power depletion, and thus the concept of energy self-sufficiency for WSN has become one of the important research topics. The primary goal of this thesis is to develop a wireless sensor node with a piezoelectric energy-harvesting module, which is capable of being a core element of a WSN, by using the energy-harvesting technique. The development of this module can solve the problem of node battery replacement, retain the active data collection characteristic of WSN, and increase the deployment flexibility while monitoring area and the number of sensor nodes increase. Furthermore, using the developed nodes, this work develops an environmental monitoring system. The system consists of a front-end sensing platform, back-end server and cloud monitoring platform. The front-end sensing platform uses the base station to coordinate, control and collect the sensing data. The acquired data of each node is transmitted to the back-end server by layered multi-hop scheme. The main work of the server is to receive, analyze, store and present the information collected by the front-end wireless sensor networks. The returned data can be displayed immediately after processing and analyzed for diagnosing motor faults. Moreover, the analysis results and environmental data will be uploaded to the cloud database; users will be able to achieve remote monitoring through the Internet application and develop intelligent monitoring function accordingly. This work develops a prototype system and performs a field test in the scenario of continuous motor operation in a factory. The collected factory environmental data and motor vibration data will be presented and diagnosed via the back-end server, and uploaded to the cloud database. According to the experiment, the node transmits the heavy load packet for 10 minutes and then enter the sleep mode. With the development of the energy-harvesting modules at the rated motor speed of 1800rpm, charging for 24.3 hours is observed to reach the node power self-sufficiency. Finally, the developed energy-harvesting modules is integrated with the wireless sensor network to collect temperature, humidity and motor vibration data, which is transmitted to the back-end server for analysis and presentation. The analyzed results is uploaded through the Internet to the cloud database storage for remote monitoring, which verifies the practicality of the proposed factory environment monitoring system.
|
|---|
