The Study On Data Collection Protocol In Microclimate Monitoring Based On Wireless Sensor Network

Wireless Sensor Network (WSN) has a wide range of applications as its advantages of affordability, flexibility, self-organization and good expansibility, etc. WSN applied in the microclimate observation application makes up for the demerits of automatic meteorological observation station. The most important purpose is to collect the information of monitoring objects to help us understanding the objective world. However, the microclimate observation data has the characteristics of magnanimity, high Spatio-temporal correlation, and so on, so it brings higher demand on the data collection protocol. The major works of the paper are about the suitable data collection protocols for the WSN application of microclimate observation by exploiting the Spatio-temporal correlation:(1) Summarizes the technologies of data collection in WSN, classifies the traditional data gathering protocols. Analyzes the challenges faced by the protocols. The design requirements are discussed according to the characteristics of the microclimate observation application. These studies provide preparation for the next step research of microclimate observation data collection protocols.(2) Proposes a data gathering protocol based on spatial correlationThere is a high spatial correlation among the readings of adjacent sensor nodes those are densely deployed in microclimate observation area. Large redundant data exists in data transmission. Firstly, this paper proposes a Distributed Energy-Aware Sampling protocol for data gathering (DEAS). The protocol can choose an approximate optimal set of sampling nodes within a limited number of iterations to finish transporting data and enforce error control by exploiting the spatial correlation among nodes. The results of simulation experiments demonstrate that the number of sampling nodes is smaller in every cycle, and the energy consumption of network is more even; the protocol is effective in reducing the energy consumption and prolongs the lifetime of the network by20%-30%compared with E2K and ODSA.(3) Proposes a clustered data collection framework based on time series prediction model In microclimate environment, the meteorological elements, like temperature and humidity, are continuous in time and space. The fourth chapter in this paper future exploits the temporal correlation among the data sensed by nodes and proposes a Clustered Data Collection Framework based on prediction model (CDCF). CDCF includes a time series prediction model based on curve fitting least square method and the efficient error control strategy. Clustered structure can consider the spatial correlation and time series prediction model can investigate the temporal correlation existed in data. The experimental simulation proves that CDCF only used10%-20%of the amount of raw data to finish the data collection in the networks, and the error of the data restored in sink is less than the threshold value the user defined.