Abstract
Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.
Highlights
IntroductionWireless Sensor Networks (WSNs) is a multi-hop network that combine sensor technology, information processing technology, embedded technology, and wireless communication technology
Wireless Sensor Networks (WSNs) is a multi-hop network that combine sensor technology, information processing technology, embedded technology, and wireless communication technology.It consists of a large number of wireless sensor nodes that are deployed, monitored, processed, and transmitted in the monitoring area, with characteristics of small size, low cost, self-organizing networks, and massive scale of coverage [1,2,3]
As one of the modern optimization algorithms, a genetic algorithm is characterized by the ability to jump out the optimal local solution with probability 1 for the nonlinear extremum problem, and find the globally optimal solution, which is based on the intersection and variation in the algorithm
Summary
Wireless Sensor Networks (WSNs) is a multi-hop network that combine sensor technology, information processing technology, embedded technology, and wireless communication technology. It consists of a large number of wireless sensor nodes that are deployed, monitored, processed, and transmitted in the monitoring area, with characteristics of small size, low cost, self-organizing networks, and massive scale of coverage [1,2,3]. In a WSN-based greenhouse environmental monitoring system, a large number of homogeneous sensor nodes are usually deployed in the sensing area [7], to perform periodic environmental data collection and transmission.
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