a new predictive model for congestion control in wireless sensor networks
Clicks: 176
ID: 258122
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
176 views
25 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #33 of 38 articles by views in phytopathologia mediterranea
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
With the increase of various applications in the domain of wireless sensor networks, the tendency to use wireless sensors has gradually increased in different applications. On the other hand, diverse traffic with different priorities generated by these sensors requires providing adaptive quality of services based on users` needs. In this paper, a congestion control predictor model is proposed for wireless sensor networks, which considers parameters like network energy consumption, packet loss rate and percentage of delivered high and medium priority packets to the destination. This method consists of congestion prevention, congestion control, and energy control plans using shortest path
selection algorithm. In the congestion prevention plan, congestion is prevented by investigating the queues length. In the congestion control plan, the congestion is controlled by reducing the transmission rate. Finally, the energy control plan aims to partially balance the energy of nodes to prevent network failures due to node energy outage. Simulation results indicated that the
proposed method has a higher efficiency regarding the aforementioned parameters. In addition, comparisons with other well-known methods showed the effectiveness of the proposed method.
| Reference Key |
panah2017journala
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;NAJME TANZADE PANAH;REZA JAVIDAN;M. RAFIE KHARAZMI |
| Journal | phytopathologia mediterranea |
| Year | 2017 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.