Abstract
Field wireless sensor network is the current global engineering field research hotspot for structural health monitoring wireless sensor network that is one of the important branches to real‐time monitoring of the safety status of the upper wood engineering structure to avoid the occurrence of many safety accidents caused by major structural and equipment damage and to guide the maintenance of major structures; the establishment of a wireless sensor network system is one of the current research priorities. This paper researches and designs a wireless sensor network system level scheme for structural health monitoring that is divided into two parts based on the hardware platform design and software development based on the system that focuses on the time synchronization protocol and synchronous acquisition method featuring synchronous acquisition start time scheme, time separation method, and flexible optimization model of time information. The method applies to high‐frequency acquisition to guarantee the time of sampling points in structural environmental measurement. The accuracy of the information and the reliability of the field diagnosis, for the detection of harmful substances, as well as leading to the construction of green habitat environment have a qualitative leap, for the design of green habitat environment that has enough progress.
Highlights
IntroductionWireless sensor networks are usually energy limited and susceptible to environmental interference, in order to improve the energy efficiency of wireless sensor networks and guarantee data reliability, and energy-efficient data fusion algorithm (EEDFA) based on a split cluster wireless sensor network is proposed; EEDFA operates in a network with a split cluster structure; the data acquisition and transmission process are divided into multiple cycles; the sensors collect data in multiple time slots within the cycle to form data vector each cycle the sensor downscales the collected data vector to reduce the transmission load, while considering the environmental interference using interval type-two fuzzy system to generate data reliability factor to guarantee the reliability of the data each cluster head receives the data from the member nodes for distributed processing and removes the redundant data using wood similarity function and sends the fused data to the hop cluster head or base station simulation results show that compared to PFF REDA
Wireless sensor networks (WSNs) are composed of a large number of miniature sensor nodes that combine sensing capability, computing capability, and short-range communication capability deployed in a monitoring area, whose nodes are generally powered by batteries that carry limited energy, and the energy is usually difficult to be replenished
To extend the network life cycle, it is necessary to reduce the network energy consumption in WSNs [2]; to monitor events more accurately, sensor nodes are usually deployed densely, which may lead to the high spatial correlation between data collected by multiple neighboring sensors, and at the same time, there are temporal correlations between data continuously collected by individual sensor nodes, and these correlations make a large amount of Journal of Sensors redundant data in the network
Summary
Wireless sensor networks are usually energy limited and susceptible to environmental interference, in order to improve the energy efficiency of wireless sensor networks and guarantee data reliability, and energy-efficient data fusion algorithm (EEDFA) based on a split cluster wireless sensor network is proposed; EEDFA operates in a network with a split cluster structure; the data acquisition and transmission process are divided into multiple cycles; the sensors collect data in multiple time slots within the cycle to form data vector each cycle the sensor downscales the collected data vector to reduce the transmission load, while considering the environmental interference using interval type-two fuzzy system to generate data reliability factor to guarantee the reliability of the data each cluster head receives the data from the member nodes for distributed processing and removes the redundant data using wood similarity function and sends the fused data to the hop cluster head or base station simulation results show that compared to PFF REDA. The REDA algorithm proposed in the literature uses a splitcluster structure to fuse the data at the common nodes and cluster heads, respectively, and removes redundant data with pattern codes, which improves the bandwidth utilization of the data, but the pattern codes are not effective in identifying similar data SCDRE algorithm that performs similarity analysis on data collected between different sensor nodes using Pearson correlation coefficient to remove similar data; using correlation coefficient alone is not a good measure of data similarity All these algorithms assume that the collected data is correct without judging the credibility of the data source, the accuracy of the data is not guaranteed and increases the unnecessary communication overhead [3]. Fieldbus is a fully digital, open two-way communication network connecting intelligent field devices and control rooms
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