Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks

The wild animal information collection based on the wireless sensor network (WSN) has an enormous number of applications, as demonstrated in the literature. Yet, it has many problems, such as low information density and high energy consumption ratio. The traditional Internet of Things (IoT) system has characteristics of limited resources and task specificity. Therefore, we introduce an improved deep neural network (DNN) structure to solve task specificity. In addition, we determine a programmability idea of software-defined network (SDN) to solve the problems of high energy consumption ratio and low information density brought about by low autonomy of equipment. By introducing some advanced network structures, such as attention mechanism, residuals, depthwise (DW) convolution, pointwise (PW) convolution, spatial pyramid pooling (SPP), and feature pyramid networks (FPN), a lightweight object detection network with a fast response is designed. Meanwhile, the concept of control plane and data plane in SDN is introduced, and nodes are divided into different types to facilitate intelligent wake-up, thereby realizing high-precision detection and high information density of the detection system. The results show that the proposed scheme can improve the detection response speed and reduce the model parameters while ensuring detection accuracy in the software-defined IoT networks.

Analysis on energy consumption of blended-wing-body underwater glider

As a new type of underwater observation platform, underwater glider is widely used in marine explorations and military surveys, and most gliders are powered by their own batteries whose capacities are limited. It is therefore necessary to analyze the energy consumption of underwater glider. In this article, the variation law of seawater density changing with depth is considered; based on the theory of rigid body dynamics, the motion model of blended-wing-body underwater glider is established; the energy consumption model of each component module is accounted by analyzing the energy consumption composition in the working process of blended-wing-body underwater glider; and the energy consumption under different navigation depths, different glide ratios, and different buoyancy adjustments regulation is simulated. The results demonstrate that as the glide depth is increased, the total energy consumption increases in a single cycle and decreases per gliding distance, leading to a smaller energy consumption ratio for the attitude adjustment module; on the other hand, as the buoyancy adjustment is increased, more energy is consumed in a single cycle and less energy is consumed per gliding distanced, resulting in a larger energy consumption ratio for the attitude adjustment module. As the glide ratio increases, the total energy consumption in a single cycle first increases and then decreases, while the energy consumption of per gliding distance and the energy consumption ratio of the attitude adjustment module are decreased.

Optimizing Parameters for Anticollision Systems between Adjacent Buildings under Earthquakes

Adjacent buildings with anticollision system would have the possibility of pounding under earthquake if there is insufficient separate distance between the two buildings. The effect of pounding and earthquake characters on the optimum parameters for anticollision system is studied through time-history analysis method in this paper. Interstory displacement ratio, energy consumption ratio, and the total strain energy of the two buildings are considered as control variables. The results show that the pounding between adjacent buildings will reduce the range of optimum parameters, and earthquake characters also have effect on the selection of optimum parameters. Therefore, it is strongly recommended to input more than three ground motion records for a time-history analysis to get the optimum parameters considering the effect of pounding and earthquake characters.