An Adaptive Genetic Algorithm of Adjusting Sensor Acquisition Frequency Portable meteorological stations are widely applied in environment monitoring systems, but they are always limited in powersupplying due to no cable power, especially in longterm monitoring scenarios. Reducing power consumption by adjusting a suitable frequency of sensor acquisition is very important for wireless sensor nodes. The regularity of historical environment data from a monitoring system is analyzed, and then an optimization model of an adaptive genetic algorithm for environment monitoring data acquisition strategies is proposed to lessen sampling frequency. According to the historical characteristics, the algorithm dynamically changes the recent data acquisition frequency so as to collect data with a smaller acquisition frequency, which will reduce the energy consumption of the sensor. Experiment results in a practical environment show that the algorithm can greatly reduce the acquisition frequency, and can obtain the environment monitoring data changing curve with less error compared with the highfrequency acquisition of fixed frequency.

An improved fusion crossover genetic algorithm for a timeweighted maximal covering location problem for sensor siting under satelliteborne monitoring Abstract   Traditional location problems usually focus on spatial and temporal impacts of facilities, but few studies have focused on sensor siting under satelliteborne monitoring in a spaceground integrated sensor network. Given the partial coverage and the requirement for continuous coverage in space and time, a timeweighted maximal covering location problem with partial coverage (TMCLPPC) model is introduced. This model is solved using a modified genetic algorithm (GA)based approach that utilizes the spatiotemporal characteristics of potential facility sites for faster convergence. The performance of the new GA is tested on a precipitation station siting problem in the Jinsha River Basin on the upper reaches of the Yangtze River in China. The results demonstrate that the proposed GA can significantly reduce the computation time compared with CPLEX, a commercial integer programming solver, and can outperform the greedy algorithm and the GAs with onepoint, twopoint, fusion, and uniform crossover operators. The applicability of the proposed method and exploration of the design in the new GA are also discussed.