ESCAL: An Energy-Saving Clustering Algorithm Based on LEACH

2008 ◽  
Author(s):  
Chao Jing ◽  
Tianlong Gu ◽  
Liang Chang
Keyword(s):  
Energy Saving ◽  
Clustering Algorithm

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 Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise

2016 ◽  
Vol 8 (2) ◽  
pp. 136 ◽  
Author(s):  
Yifei Tong ◽  
Jingwei Li ◽  
Shai Li ◽  
Dongbo Li
Keyword(s):  
Energy Saving ◽  
Production Scheduling ◽  
Clustering Algorithm

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.

scholarly journals A Multihop Clustering Algorithm for Energy Saving in Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Saeed Ebadi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Saving ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Node Degree ◽  
Leach Protocol ◽  
Two Parameters

One of the important problems for wireless sensor networks is increasing the network lifetime. Clustering is an efficient technique for prolonging the lifetime of wireless sensor networks. This papers propose a multihop clustering algorithm (MHC) for energy saving in wireless sensor networks. MHC selects the clusterheads according to the two parameters the remaining energy and node degree. Also cluster heads select their members according to the two parameters of sensor the remaining energy and the distance to its cluster head. MHC is done in three phases quickly. Simulation results show that the proposed algorithm increases the network lifetime more than 16 percent compared of the LEACH protocol.

Subjective Time Preference and Willingness to Pay for an Energy-Saving Durable Good

2001 ◽  
Vol 32 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Gerrit Antonides ◽  
Sophia R. Wunderink
Keyword(s):  
Willingness To Pay ◽  
Energy Saving ◽  
Subjective Time ◽  
Durable Good ◽  
Discount Function ◽  
Hyperbolic Discount ◽  
The One ◽  
Two Parameter ◽  
Difference Effect ◽  
Better Than

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.

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