2022
DOI: 10.1155/2022/1164613
|View full text |Cite
|
Sign up to set email alerts
|

Multicluster Analysis and Design of Hybrid Wireless Sensor Networks Using Solar Energy

Abstract: A wireless touch network is a distributed, self-organizing network of multiple sensors and actuators in combination with multiple sensors and a radio channel. Also, the security area of such a network can be several meters to several meters. The main difference between wireless sensor networks from traditional computer and telephone networks is the lack of a fixed infrastructure owned by a specific operator or provider. Each user terminal in a touch network is capable of acting as a terminal device only. Despi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 24 publications
(4 citation statements)
references
References 20 publications
0
4
0
Order By: Relevance
“…They developed a genetic algorithm-optimized learning model to analyse the effectiveness of charging scheduling of a bidirectional power network. Another effective model for energy scheduling in smart buildings was proposed by [10]. They utilized the Deep Reinforcement Learning (DRL) approach to classify the device demand and predict EC and demand for effective scheduling.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…They developed a genetic algorithm-optimized learning model to analyse the effectiveness of charging scheduling of a bidirectional power network. Another effective model for energy scheduling in smart buildings was proposed by [10]. They utilized the Deep Reinforcement Learning (DRL) approach to classify the device demand and predict EC and demand for effective scheduling.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The cost of electricity is a significant factor in the total operational cost and varies according to the time of day, demand, and utility provider policies. Let đ¶ elec (𝑡) denote the electricity cost at time 𝑡, which can be defined as EQU (10).…”
Section: Cost Modelmentioning
confidence: 99%
“…By narrowing down the feature set, the model can focus on the most crucial data points, thereby improving efficiency, reducing overfitting, and simplifying interpretability. [20][21][22]. Entropy measures the amount of uncertainty or randomness in a dataset, and by applying it recursively, the algorithm aims to identify and retain only the most informative features for model training.…”
Section: Entropy-based Recursive Feature Elimination (Erfe)mentioning
confidence: 99%
“…There's an urgent need for a robust model that navigates the intricacies of device communication data and provides tangible insights for device functionality enhancement and security fortification [16][17][18][19][20]. This backdrop amplifies the motivation behind the proposed work, which aims to leverage the K-means clustering algorithm to decode intricate communication patterns from modern electronic devices [21][22][23].…”
Section: Literature Reviewmentioning
confidence: 99%