The Coverage Optimization Method for Underwater Sensor Network Based on VF-PSO Algorithm

2020 ◽  
Author(s):  
Yujiao Sun ◽  
Yifan Hu ◽  
Lu Chen ◽  
Hailin Liu ◽  
Jie Chen ◽  
...  
Keyword(s):  
Sensor Network ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Coverage Optimization ◽  
Underwater Sensor Network

Coverage Optimization Methods in Wireless Homo-Sensor Network Based on Guided Swarms

2011 ◽  
Vol 148-149 ◽  
pp. 868-874
Author(s):  
Huan Yang Zheng
Keyword(s):  
Sensor Network ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Search Process ◽  
Migration Process ◽  
Swarm Optimization ◽  
Network Position ◽  
Coverage Optimization ◽  
Position Problem ◽  
Grid Based

An improved particle swarm optimization (PSO) algorithm is designed for the grid based wireless homo-sensor network position problem. The proposed method, called guided method, introduces the simulation of migration process to PSO and its mutation algorithm, using a previous designed sparse position plan to guide the swarm to the optimization solution, and accelerates the search process. Experiments show not only the feasibility and validity of the proposed method but also a marked improvement in performance over traditional PSO.

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.

scholarly journals Impact of emission trading and renewable energy support scheme on the optimality of generator side bidding

2020 ◽  
Vol 167 ◽  
pp. 05008 ◽  
Author(s):  
A Arya ◽  
SPS Mathur ◽  
M Dubey
Keyword(s):  
Renewable Energy ◽  
Ghg Emissions ◽  
Profit Maximization ◽  
Emission Trading ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Developed Countries ◽  
Test System ◽  
For Profit ◽  
System Operator

As a major Green House Gases (GHG) producer, CO2 in particular, the electricity industry’s emissions have turned in to a matter of immense concern in many countries, especially in India. India’s economy and fast economic development has attracts the attention of the world. Emission trading schemes (ETS) and renewable energy support schemes (RESS) are implemented by the various developed countries to alleviate the affect of GHG emissions. In this paper, an optimization based market simulation approach is proposed with the consideration of emission trading schemes and renewable support schemes. To simulate the bidding strategy and for profit maximization, a particle swarm optimization (PSO) algorithm is used. As above problem is a multi-objective optimization problem, Where, in the first level each Genco submit the bid to the independent system operator and in the next level a optimization method is used for the determination of optimal bidding with the implementation of emission trading schemes and renewable support schemes. It is assumed that each generator should submit bid as a price taker’s in sealed auction based on pay-as-bid market clearing price mechanism. The practicability of proposed optimization method is checked by an IEEE-30 bus test system consists of six suppliers.

scholarly journals Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

2018 ◽  
Vol 8 (2) ◽  
pp. 1084
Author(s):  
Hemavathi P ◽  
Nandakumar A. N.
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Cluster Head ◽  
Dynamic Network ◽  
Cost Effective ◽  
Pso Algorithm ◽  
Wireless Sensor ◽  
Cost Effective Method ◽  
Significant Research

Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency.

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