scholarly journals A Sensor Deployment Approach Using Improved Virtual Force Algorithm Based on Area Intensity for Multisensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jiahao Xie ◽  
Daozhi Wei ◽  
Shucai Huang ◽  
Xiangwei Bu
Keyword(s):  
Sensor Deployment ◽  
Local Optimum ◽  
Detection Model ◽  
Coverage Optimization ◽  
Virtual Force ◽  
Global Coverage ◽  
Optimal Deployment ◽  
Coverage Quality ◽  
Simulation Results ◽  
Sensor Detection

Sensor deployment is one of the major concerns in multisensor networks. This paper proposes a sensor deployment approach using improved virtual force algorithm based on area intensity for multisensor networks to realize the optimal deployment of multisensor and obtain better coverage effect. Due to the real-time sensor detection model, the algorithm uses the intensity of sensor area to select the optimal deployment distance. In order to verify the effectiveness of this algorithm to improve coverage quality, VFA and PSOA are selected for comparative analysis. The simulation results show that the algorithm can achieve global coverage optimization better and improve the performance of virtual force algorithm. It avoids the unstable coverage caused by the large amount of computation, slow convergence speed, and easily falling into local optimum, which provides a new idea for multisensor deployment.

Optimal Deployment for Mobile Target Tracking in Sensor Networks

2012 ◽  
Vol 468-471 ◽  
pp. 1657-1660
Author(s):  
Ying Chi Mao
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Surveillance Systems ◽  
Challenging Problem ◽  
Irregular Pattern ◽  
Optimal Deployment ◽  
Mobile Target ◽  
Simulation Results

Mobile target tracking is a key application of wireless sensor network-based surveillance systems. Sensor deployment is an important factor in tracking performance and remains a challenging problem. In this paper, we address the problem of optimal sensor deployment for mobile target tracking. We analyze the tracking performance of three patterns. Simulation results demonstrate that the irregular pattern outperforms the other two patterns.

scholarly journals Design Coverage Optimization Based on Position of Constellations and Cost of the Launch Vehicle

2021 ◽  
Vol 17 ◽  
pp. 1160-1190
Author(s):  
Saeid Kohani ◽  
Peng Zong ◽  
Fengfan Yang
Keyword(s):  
Genetic Algorithms ◽  
Optimal Design ◽  
Launch Vehicle ◽  
Multiple Objective ◽  
Satellite Constellations ◽  
Coverage Optimization ◽  
Satellite Constellation ◽  
Constellation Design ◽  
Global Coverage ◽  
Simulation Results

This research will analyze the tradeoffs between coverage optimization based on Position dilution of precision (PDOP) and cost of the launch vehicle. It adopts MATLAB and STK tools along with multiple objective genetic algorithms (MOGA) to explore the trade space for the constellation designs at different orbital altitudes. The objective of optimal design solutions is inferred to determine the economic and efficient LEO, MEO, HEO or hybrid constellations and simulation results are presented to optimize the design of satellite constellations. The benefits of this research are the optimization of satellite constellation design, which reduces costs and increases regional and global coverage with the least number of satellites. The result of this project is the optimization of the number of constellation satellites in several orbital planes in LEO orbit. Validations are based on reviewing the results of several simulations. The results of graphs and tables are presented in the last two sections and are taken from the results of several simulations.

A Method to Multi-Sensor Networking for Target Tracking

2014 ◽  
Vol 533 ◽  
pp. 207-210
Author(s):  
Ning Wang ◽  
Hong Wei Quan ◽  
Xiu Yin Xue
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Sensor Deployment ◽  
Acoustic Sensor ◽  
Acoustic Sensors ◽  
Important Research ◽  
Detection Model ◽  
Important Research Topic ◽  
Sensor Networking ◽  
Sensor Detection

The acoustic sensor networking is an important research topic in multi-sensor target tracking system. An acoustic sensor network consists of multiple acoustic sensors which are located in fixed positions with specific deployment mode. It can improve the robustness and fault-tolerance of the target tracking system, especially when a single or few sensors do not work normally with some faults. This paper discusses the acoustic sensor detection model and gives a method to sensor deployment for target detection in target tracking system.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

scholarly journals A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments

2019 ◽  
Vol 9 (22) ◽  
pp. 4964 ◽  
Author(s):  
Yue ◽  
Guan ◽  
Wang
Keyword(s):  
Reinforcement Learning ◽  
Prior Information ◽  
Search Task ◽  
Efficiency Function ◽  
Awareness Information ◽  
Simulation Results ◽  
Searching Method ◽  
Sea Area ◽  
Sensor Detection ◽  
Multi Uav

In this paper, the important topic of cooperative searches for multi-dynamic targets in unknown sea areas by unmanned aerial vehicles (UAVs) is studied based on a reinforcement learning (RL) algorithm. A novel multi-UAV sea area search map is established, in which models of the environment, UAV dynamics, target dynamics, and sensor detection are involved. Then, the search map is updated and extended using the concept of the territory awareness information map. Finally, according to the search efficiency function, a reward and punishment function is designed, and an RL method is used to generate a multi-UAV cooperative search path online. The simulation results show that the proposed algorithm could effectively perform the search task in the sea area with no prior information.

Coverage Optimization of Hybrid Wireless Sensor Networks Based on Modified Particle Swarm Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 914-917
Author(s):  
Su Fen Yao ◽  
Jian Qiang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Pso Algorithm ◽  
Wireless Sensor ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Coverage Optimization ◽  
Adaptive Neighborhood

A strategy for controlling mobile nodes based on PSO algorithm with neighborhood disturbance was proposed for improving the network coverage rate in wireless sensor networks. The non-dominated sorting strategy was led into basic PSO algorithm to seek best particle and adaptive neighborhood disturbance operation was used to conquer the drawback of PSO falling into local optimum. Therefore, the effect of network coverage had been improved and the network energy consumption can be reduced.

Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 588-591
Author(s):  
Yan Li Yang ◽  
Wei Wei Ke
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Local Optimum ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Global Optimal ◽  
Simulation Results

An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

WSN Coverage Enhancement Algorithm Based on Particle Swarm Optimization

2014 ◽  
Vol 945-949 ◽  
pp. 2386-2393
Author(s):  
Jian Xia Wang ◽  
Xiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Local Optimum ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Effective Coverage ◽  
Coverage Optimization ◽  
Adaptive Operation ◽  
Chaotic Particle Swarm Optimization ◽  
Enhancement Algorithm

For the optimization of WSN coverage, this paper proposes a coverage optimization approach: Adaptive Disturbance Chaotic Particle Swarm Optimization, referred as ADCPSO. Based on the effective coverage of the network as the optimization goal, the method first conducts adaptive operation on the particles. Introducing perturbations factor and making particles trapped into local optimum quickly jump out, this method then use the randomness and the ergodicity of chaotic motion, to make local fine search. This effectively avoids particles’ “being premature” and improves the accuracy of the algorithm. The simulation results show that the ADCPSO algorithm can get better coverage.

On the Vulnerabilities of the Virtual Force Approach to Mobile Sensor Deployment

2014 ◽  
Vol 13 (11) ◽  
pp. 2592-2605 ◽  
Author(s):  
Novella Bartolini ◽  
Giancarlo Bongiovanni ◽  
Thomas F. La Porta ◽  
Simone Silvestri
Keyword(s):  
Sensor Deployment ◽  
Mobile Sensor ◽  
Virtual Force
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