Energy-saving clustering algorithm based on improved PSO for WSNs

2016 ◽  
pp. 205-210
Author(s):  
Z Sun ◽  
C Zhou
Keyword(s):  
Energy Saving ◽  
Clustering Algorithm ◽  
Improved Pso

scholarly journals eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine Learning

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 462
Author(s):  
Guilherme Henrique Apostolo ◽  
Flavia Bernardini ◽  
Luiz C. Schara Magalhães ◽  
Débora C. Muchaluat-Saade
Keyword(s):  
Machine Learning ◽  
Energy Saving ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Local Area Networks ◽  
Local Area ◽  
Wireless Local Area Networks ◽  
Machine Learning Algorithms ◽  
Decision Algorithm ◽  
User Coverage

As wireless local area networks grow in size to provide access to users, power consumption becomes an important issue. Power savings in a large-scale Wi-Fi network, with low impact to user service, is undoubtedly desired. In this work, we propose and evaluate the eSCIFI energy saving mechanism for Wireless Local Area Networks (WLANs). eSCIFI is an energy saving mechanism that uses machine learning algorithms as occupancy demand estimators. The eSCIFI mechanism is designed to cope with a broader range of WLANs, which includes Wi-Fi networks such as the Fluminense Federal University (UFF) SCIFI network. The eSCIFI can cope with WLANs that cannot acquire data in a real time manner and/or possess a limited CPU power. The eSCIFI design also includes two clustering algorithms, named cSCIFI and cSCIFI+, that help to guarantee the network’s coverage. eSCIFI uses those network clusters and machine learning predictions as input features to an energy state decision algorithm that then decides which Access Points (AP) can be switched off during the day. To evaluate eSCIFI performance, we conducted several trace-driven simulations comparing the eSCIFI mechanism using both clustering algorithms with other energy saving mechanisms found in the literature using the UFF SCIFI network traces. The results showed that eSCIFI mechanism using the cSCIFI+ clustering algorithm achieves the best performance and that it can save up to 64.32% of the UFF SCIFI network energy without affecting the user coverage.

scholarly journals Collision-Free and Energy-Saving Trajectory Planning for Large-Scale Redundant Manipulator Using Improved PSO

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Min Jin ◽  
Dan Wu
Keyword(s):  
Energy Saving ◽  
Trajectory Planning ◽  
Large Scale ◽  
Pso Algorithm ◽  
Inverse Kinematic ◽  
Outdoor Environment ◽  
Inverse Kinematic Problem ◽  
Concrete Pump Truck ◽  
Concrete Pump ◽  
Improved Pso

The large-scale boom system, such as the five-arm concrete pump truck with the arm length of 36–65 meters, usually operates in an unknown dynamic outdoor environment. The motion safety and the energy consumption are thus the two vital measurements to the effectiveness of the trajectory planning for the large-scale boom system. Due to the redundancy of the large-scale boom system and some drawbacks of the original particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented to solve the inverse kinematic problem of the redundant large-scale boom system. By the improved PSO algorithm, the energy-saving trajectory planning of the large-scale boom system that operates in a workspace without obstacles and with obstacles is optimized, which considers different important degrees of the subgoals, respectively. The optimal results from the simulation study and the practical application verify the effectiveness of the proposed planning strategy. At the same time, the performance of the improved strategy is compared with that of the traditional, and the superiority is further demonstrated.

scholarly journals Energy Efficiency Improvement in LEACH Protocol for Wireless Sensor Network

2020 ◽  
Vol 9 (2) ◽  
pp. 505-508
Keyword(s):  
Energy Saving ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Vice President ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficiency Improvement ◽  
Leach Protocol ◽  
And Performance ◽  
Effective Use

The most important reason for saving energy on wireless sensor networks is communication. Data transmission consumes about 70% of the energy of the sensor node. Effective use of energy on sensor nodes is a good way to increase the lifetime of WSN. In order to extend the life of the network, energy-saving routing protocols must be designed. In this article, I will discuss (LEACH), which is the first and most popular energy-saving hierarchical clustering algorithm for WSN and an improvement to Leach and VLeach that attempts to eliminate the shortcoming of V-LEACH and LEACH protocols, In this method, initially, the "sub-cluster head" and "cluster head" are selected according to the energy and distance parameters. The head of the cluster and the vice president of the cluster make decisions based on the distance and the remaining energy of the sensor nodes. Compared with standard leaching, this algorithm can provide better network life, efficiency and performance.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

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