A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumptio

A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs

Image super-resolution via dynamic network

Convolutional neural networks (CNNs) depend on deep network architectures to extract accurate information for image super-resolution. However, obtained information of these CNNs cannot completely express predicted high-quality images for complex scenes. In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block. The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super-resolution. To enhance robustness of obtained super-resolution model for complex scenes, a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super-resolution model for varying scenes. To prevent interference of components in a wide enhancement block, a refinement block utilizes a stacked architecture to accurately learn obtained features. Also, a residual learning operation is embedded in the refinement block to prevent long-term dependency problem. Finally, a construction block is responsible for reconstructing high-quality images. Designed heterogeneous architecture can not only facilitate richer structural information, but also be lightweight, which is suitable for mobile digital devices. Experimental results shows that our method is more competitive in terms of performance and recovering time of image super-resolution and complexity. The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet

Image super‐resolution via dynamic network

Abstract Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution. However, obtained information of these convolutional neural networks cannot completely express predicted high‐quality images for complex scenes. A dynamic network for image super‐resolution (DSRNet) is presented, which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block. The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution. To enhance robustness of obtained super‐resolution model for complex scenes, a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes. To prevent interference of components in a wide enhancement block, a refinement block utilises a stacked architecture to accurately learn obtained features. Also, a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem. Finally, a construction block is responsible for reconstructing high‐quality images. Designed heterogeneous architecture can not only facilitate richer structural information, but also be lightweight, which is suitable for mobile digital devices. Experimental results show that our method is more competitive in terms of performance, recovering time of image super‐resolution and complexity. The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet

A PSO based coverage hole patching scheme for WSNs

In this paper, a mobile assist node deployment algorithm is proposed to patch coverage holes in wireless sensor network (WSNs). In initial phase, sensor nodes are randomly deployed in target area, they remain static or switch to sleep mode after deployment. Then, we partition the network into girds and calculate the coverage rate of each gird. Finally, we wake mobile sensors from sleep mode to fix coverage hole, and we utilize particle swarm optimization (PSO) algorithm to calculate the moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs