Traversal path algorithm based on boundaries of intersection region in three-dimensional directional sensor networks

2014 ◽  
Author(s):  
Panpan Tao ◽  
Lijuan Sun ◽  
Jian Guo ◽  
Linfeng Liu
Keyword(s):  
Sensor Networks ◽  
Three Dimensional ◽  
Directional Sensor Networks ◽  
Directional Sensor

scholarly journals Dynamic Adjustment Optimisation Algorithm in 3D Directional Sensor Networks Based on Spherical Sector Coverage Models

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaochao Dang ◽  
Chenguang Shao ◽  
Zhanjun Hao
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Three Dimensional ◽  
Target Coverage ◽  
Dynamic Adjustment ◽  
Directional Sensor Networks ◽  
Coverage Ratio ◽  
Optimisation Algorithm ◽  
Sensing Model ◽  
Directional Sensor

In directional sensor networks research, target event detection is currently an active research area, with applications in underwater target monitoring, forest fire warnings, border areas, and other important activities. Previous studies have often discussed target coverage in two-dimensional sensor networks, but these studies cannot be extensively applied to three-dimensional networks. Additionally, most of the previous target coverage detection models are based on a circular or omnidirectional sensing model. More importantly, if the directional sensor network does not design a better coverage algorithm in the coverage-monitoring process, its nodes’ energy consumption will increase and the network lifetime will be significantly shortened. With the objective of addressing three-dimensional target coverage in applications, this study proposes a dynamic adjustment optimisation algorithm for three-dimensional directional sensor networks based on a spherical sector coverage model, which improves the lifetime and coverage ratio of the network. First, we redefine the directional nodes’ sensing model and use the three-dimensional Voronoi method to divide the regions where the nodes are located. Then, we introduce a correlation force between the target and the sensor node to optimise the algorithm’s coverage mechanism, so that the sensor node can accurately move to the specified position for target coverage. Finally, by verifying the feasibility and accuracy of the proposed algorithm, the simulation experiments demonstrate that the proposed algorithm can effectively improve the network coverage and node utilisation.

scholarly journals Surface coverage algorithm in directional sensor networks for three-dimensional complex terrains

2016 ◽  
Vol 21 (4) ◽  
pp. 397-406 ◽  
Author(s):  
Fu Xiao ◽  
Xiekun Yang ◽  
Meng Yang ◽  
Lijuan Sun ◽  
Ruchuan Wang ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Surface Coverage ◽  
Three Dimensional ◽  
Directional Sensor Networks ◽  
Directional Sensor

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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