Solving wireless sensor network coverage problem using LAEDA
Coverage improvement is one of the main problems in wireless sensor networks. Given a finite number of sensors, improvement of the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. For achieving good coverage, the sensors should be placed...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2020-04-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02318 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs18272_13626 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Khezri, Shirin |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Nazaari A, Mahdi |e author |
| 245 | 0 | 0 | |a Solving wireless sensor network coverage problem using LAEDA |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-04-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18272 | ||
| 520 | |a Coverage improvement is one of the main problems in wireless sensor networks. Given a finite number of sensors, improvement of the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. For achieving good coverage, the sensors should be placed in adequate places. In this article, estimation of distribution algorithm based on learning automata is presented for solving the sensor placement (LAEDA-SP) in distributed sensor networks by considering two factors: 1) the complete coverage and 2) the minimum costs. The proposed algorithm is a model based on search optimization method that uses a set of learning automata as a probabilistic model of high-quality solutions seen in the search process. It is applied in a various area with different size. The results not only confirmed the successes of using the new method in sensor replacement but also they showed that the proposed method performs more efficiently compared to the state-of-the-art methods such as simulated annealing (SA) and population-based incremental learning algorithms (PBIL). | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a LAEDA, Distributed sensor network, Sensor placement | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 1: April 2020; 452-458 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18272/13626 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18272/13626 |z Get fulltext |