Focusing on the issues of high cost, high susceptibility to damage and low prediction accuracy of soil moisture monitoring and forecasting system, the soil moisture monitoring based on non-fixed wireless sensor network and improved grey wolf algorithm optimization neural network was designed and implemented. In the proposed soil moisture monitoring system, non-fixed and plug-in sensor bluetooth network was used to collect moisture data, and high-precision multi-source location access fusion method was used for wide-area outdoor high-precision positioning. In terms of algorithms, focusing on the issue that Grey Wolf Optimizer (GWO) algorithm easily falls into local optima in its later iterations, an improved GWO algorithm based on rearward explorer mechanism was proposed. Firstly, according to the fitness value of the population, the explorer type was added to the original individual types of the algorithm. Secondly, the search period of population was divided into three parts: active exploration period, cycle exploration period and population regression period. Finally, the unique location updating strategy was used for the explorer during the different period, which made the algorithm more random in the early stage and keep updating in the middle and late stages, thus strengthening the local optimal avoidance ability of the algorithm. The algorithm was tested on the standard functions and applied to optimize the neural network prediction model of soil moisture system. Based on the datasets obtained from the experimental plot No. 2 in a city, the experimental results show that the relative error decreases by about 4 percentage points compared with the direct neural network prediction model, and decreases by about 1 to 2 percentage points compared with the traditional GWO algorithm and Particle Swarm Optimization (PSO). The proposed algorithm has smaller error, better local optimal avoidance ability, and improves the prediction quality of soil moisture.

With the development of indoor positioning technology, a large amount of indoor location data and user data for consumer behavior makes the indoor Location Big Data (LBD) research and application possible. High-precision indoor location technology breaks the bottleneck of indoor location data with low accuracy. By clustering the indoor location data and dimension reduction pretreatment, a mining model was set up to extract the characteristics of custom and flow in the indoor shopping area. Then using the associated user consumption behavior to predict the characteristics of consumer behaviors, a collaborative mining method and architecture for large data of indoor location was put forward. Experiments on location dataset of billions of users in an airport and a shopping mall in Xidan were conducted. The results verify the accuracy and feasibility of the mining method based on this architecture of indoor positioning data.