πŸ“„
RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks paper
⭐ 68.8
πŸ“„
Paper

RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks

000a1779aaf160a615b268d833c30cca91f55f98
Free2AITools Nexus Index
68.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 84
P: Popularity 60
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning app...

Source β†’
Semantic Scholar 52 Citations
Paper Information Summary
Entity Passport
Registry ID 000a1779aaf160a615b268d833c30cca91f55f98
License ArXiv
Provider semantic_scholar
πŸ“œ

Cite this paper

Academic & Research Attribution

BibTeX 
@misc{000a1779aaf160a615b268d833c30cca91f55f98,
  title = {RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks Paper},
  howpublished = {\url{https://api.semanticscholar.org/000a1779aaf160a615b268d833c30cca91f55f98}},
  note = {Accessed via Free2AITools.}
}
APA Style
RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks [Paper]. Free2AITools. https://api.semanticscholar.org/000a1779aaf160a615b268d833c30cca91f55f98

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 84
Popularity (P) 60
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks: Authority (A:84), Popularity (P:60), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning app..."

❝ Cite Node 

@article{RLBEEP,
  title={RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks},
  note={Indexed by Free2AITools}
}

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

πŸ“Š Research Signals

πŸ“ˆ52CitationsSemantic Scholar
πŸ›οΈ84AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField
πŸ“¦Data Source: semantic_scholar
πŸ”„ Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

πŸ“Š Engagement & Metrics

downloads
0
citations
52

Data indexed from public sources. Updated daily.