Search

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng., Competing Interests: The authors have declared that no competing interests exist.

We propose the regularized linear programming discriminant (LPD) rule with folded concave penalty in the ultrahigh-dimensional regime. We use the local linear approximation (LLA) algorithm to redirect the model with folded concave penalty to a weighted ℓ1 model. The strong oracle property of the solution constructed by the one-step local linear approximation (LLA) algorithm is verified. In addition, we propose efficient and parallelizable algorithms based on feature space split to address the computational challenges due to ultrahigh dimensionality. The proposed feature-split algorithm is compared to existing methods by both numerical simulations and applications to real data examples. The numerical comparisons suggest that the proposed method works well for ultrahigh dimensions, while the linear programming solver and alternating direction method of multiplier (ADMM) algorithm may fail for such high dimensions. for this article are available online. [ABSTRACT FROM AUTHOR]