A Lagrangean relaxation approach to lifetime maximization of directional sensor networks

Networks ◽  
2021 ◽  
Author(s):  
Annabella Astorino ◽  
Manlio Gaudioso ◽  
Giovanna Miglionico
Keyword(s):  
Sensor Networks ◽  
Lagrangean Relaxation ◽  
Lifetime Maximization ◽  
Directional Sensor Networks ◽  
Directional Sensor

Coverage Control Algorithms for Directional Sensor Networks

2011 ◽  
Vol 22 (10) ◽  
pp. 2317-2334 ◽  
Author(s):  
Dan TAO ◽  
Hua-Dong MA
Keyword(s):  
Sensor Networks ◽  
Control Algorithms ◽  
Directional Sensor Networks ◽  
Coverage Control ◽  
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.

A new genetic-based approach for solving k-coverage problem in directional sensor networks

2021 ◽  
Author(s):  
Abolghasem Alibeiki ◽  
Homayun Motameni ◽  
Hosein Mohamadi
Keyword(s):  
Sensor Networks ◽  
Coverage Problem ◽  
Directional Sensor Networks ◽  
Directional Sensor

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

Group scheduling problems in directional sensor networks

2014 ◽  
Vol 47 (12) ◽  
pp. 1651-1669 ◽  
Author(s):  
Alok Singh ◽  
André Rossi
Keyword(s):  
Sensor Networks ◽  
Group Scheduling ◽  
Scheduling Problems ◽  
Directional Sensor Networks ◽  
Directional Sensor

scholarly journals Sensor scheduling for target coverage in directional sensor networks

2017 ◽  
Vol 13 (6) ◽  
pp. 155014771771364 ◽  
Author(s):  
Jinglan Jia ◽  
Cailin Dong ◽  
Xinggang He ◽  
Deying Li ◽  
Ying Yu
Keyword(s):  
Sensor Networks ◽  
Target Coverage ◽  
Sensor Scheduling ◽  
Directional Sensor Networks ◽  
Directional Sensor
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