A Virtual Potential Field Based Coverage-Enhancing Algorithm for Directional Sensor Networks

2007 ◽  
Vol 18 (5) ◽  
pp. 1152 ◽  
Author(s):  
Dan TAO
Keyword(s):  
Sensor Networks ◽  
Potential Field ◽  
Directional Sensor Networks ◽  
Directional Sensor

Optimization of the Virtual Potential Field Based on Coverage-Enhancing Algorithm for Directional Sensor Networks

2014 ◽  
Vol 651-653 ◽  
pp. 1882-1887
Author(s):  
Jun Zhu ◽  
Chen Shi ◽  
Shan Shan Zhu ◽  
Jun Zhang
Keyword(s):  
Sensor Networks ◽  
Potential Field ◽  
Boundary Region ◽  
Sensor Nodes ◽  
Target Area ◽  
Virtual Node ◽  
Directional Sensor Networks ◽  
Coverage Ratio ◽  
Coverage Holes ◽  
Directional Sensor

After directional sensor nodes are randomly thrown into target area, coverage ratio often less than the anticipant value, in order to improve the coverage, sensor nodes should turn from overlapping regions to coverage holes by a much faster way. In this paper, we improved the existing potential field based coverage-enhancing algorithm (PFCEA), presented a optimization of the virtual potential field based on coverage-enhancing algorithm for directional sensor networks (OPFCEA), we introducing a new-style virtual node to enhance the coverage of boundary region and a new-style control for the rotation angle. By these ways, we can improve network’s performance. This algorithm enhanced the coverage ratio of the network, the simulation results show the effectiveness of the algorithm.

An Improved Algorithm of Virtual Potential Field in Directional Sensor Networks

2011 ◽  
Vol 467-469 ◽  
pp. 1562-1568 ◽  
Author(s):  
Jun Jie Huang ◽  
Yi Shi ◽  
Li Juan Sun ◽  
Ru Chuan Wang ◽  
Hai Ping Huang
Keyword(s):  
Sensor Networks ◽  
Potential Field ◽  
Energy Cost ◽  
Simulation Experiment ◽  
The Novel ◽  
Directional Sensor Networks ◽  
Virtual Force ◽  
Virtual Field ◽  
Improved Algorithm ◽  
Directional Sensor

In view of the irrationality that traditional virtual potential field algorithms brought about on the criterion for the generation of virtual force in directional sensor networks, we propose a novel criterion for judgment. Cross-set test is used to determine whether the sensory region has any overlap, thereby restrain the energy cost of nodes and the performance of the algorithm is improved. In simulation experiment, the validity of the novel criterion and IAPD (improved algorithm of virtual potential field in directional sensor networks) are verified by the compare of IAPD and traditional virtual field algorithms.

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.

Efficient target tracking in directional sensor networks with selective target area’s coverage

2017 ◽  
Vol 68 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Amir Hossein Mohajerzadeh ◽  
Hasan Jahedinia ◽  
Zahra Izadi-Ghodousi ◽  
Dariush Abbasinezhad-Mood ◽  
Mahdi Salehi
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Directional Sensor Networks ◽  
Selective Target ◽  
Directional Sensor
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