Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm
Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue ta...
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ftdoajarticles:oai:doaj.org/article:056d8d3f2ad046f7bbf900ac0f100a4b 2023-08-27T04:08:43+02:00 Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm Haitao Huang Min Tian Jie Zhou Xiang Liu 2023-07-01T00:00:00Z https://doi.org/10.3934/mbe.2023656 https://doaj.org/article/056d8d3f2ad046f7bbf900ac0f100a4b EN eng AIMS Press https://www.aimspress.com/article/doi/10.3934/mbe.2023656?viewType=HTML https://doaj.org/toc/1551-0018 doi:10.3934/mbe.2023656 1551-0018 https://doaj.org/article/056d8d3f2ad046f7bbf900ac0f100a4b Mathematical Biosciences and Engineering, Vol 20, Iss 8, Pp 14675-14698 (2023) task allocation precision agriculture soil moisture wireless sensor networks algorithm network benefit Biotechnology TP248.13-248.65 Mathematics QA1-939 article 2023 ftdoajarticles https://doi.org/10.3934/mbe.2023656 2023-08-06T00:35:51Z Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue task allocation for SMWSNs to improve the network benefits of SMWSNs. However, the task allocation of SMWSNs is an NP (Non-deterministic Polynomial)-hard issue, and its complexity becomes even higher when constraints such as limited computing capabilities and power are taken into consideration. In this paper, a novel differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA) is proposed. DEAEBOA has significantly improved the task allocation efficiency of SMWSNs, effectively avoided plan stagnation, and greatly accelerated the convergence speed. In the meantime, a new adaptive operator was designed, which signally ameliorates the accuracy and performance of the algorithm. In addition, a new elite operator and differential evolution strategy are put forward to markedly enhance the global search ability, which can availably avoid local optimization. Simulation experiments were carried out by comparing DEAEBOA with the butterfly optimization algorithm (BOA), particle swarm optimization (PSO), genetic algorithm (GA), and beluga whale optimization (BWO). The simulation results show that DEAEBOA significantly improved the task allocation efficiency, and compared with BOA, PSO, GA, and BWO the network benefit rate increased by 11.86%, 5.46%, 8.98%, and 12.18% respectively. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles Boa ENVELOPE(15.532,15.532,66.822,66.822) Mathematical Biosciences and Engineering 20 8 14675 14698 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
task allocation precision agriculture soil moisture wireless sensor networks algorithm network benefit Biotechnology TP248.13-248.65 Mathematics QA1-939 |
spellingShingle |
task allocation precision agriculture soil moisture wireless sensor networks algorithm network benefit Biotechnology TP248.13-248.65 Mathematics QA1-939 Haitao Huang Min Tian Jie Zhou Xiang Liu Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
topic_facet |
task allocation precision agriculture soil moisture wireless sensor networks algorithm network benefit Biotechnology TP248.13-248.65 Mathematics QA1-939 |
description |
Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue task allocation for SMWSNs to improve the network benefits of SMWSNs. However, the task allocation of SMWSNs is an NP (Non-deterministic Polynomial)-hard issue, and its complexity becomes even higher when constraints such as limited computing capabilities and power are taken into consideration. In this paper, a novel differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA) is proposed. DEAEBOA has significantly improved the task allocation efficiency of SMWSNs, effectively avoided plan stagnation, and greatly accelerated the convergence speed. In the meantime, a new adaptive operator was designed, which signally ameliorates the accuracy and performance of the algorithm. In addition, a new elite operator and differential evolution strategy are put forward to markedly enhance the global search ability, which can availably avoid local optimization. Simulation experiments were carried out by comparing DEAEBOA with the butterfly optimization algorithm (BOA), particle swarm optimization (PSO), genetic algorithm (GA), and beluga whale optimization (BWO). The simulation results show that DEAEBOA significantly improved the task allocation efficiency, and compared with BOA, PSO, GA, and BWO the network benefit rate increased by 11.86%, 5.46%, 8.98%, and 12.18% respectively. |
format |
Article in Journal/Newspaper |
author |
Haitao Huang Min Tian Jie Zhou Xiang Liu |
author_facet |
Haitao Huang Min Tian Jie Zhou Xiang Liu |
author_sort |
Haitao Huang |
title |
Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
title_short |
Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
title_full |
Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
title_fullStr |
Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
title_full_unstemmed |
Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
title_sort |
reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm |
publisher |
AIMS Press |
publishDate |
2023 |
url |
https://doi.org/10.3934/mbe.2023656 https://doaj.org/article/056d8d3f2ad046f7bbf900ac0f100a4b |
long_lat |
ENVELOPE(15.532,15.532,66.822,66.822) |
geographic |
Boa |
geographic_facet |
Boa |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Mathematical Biosciences and Engineering, Vol 20, Iss 8, Pp 14675-14698 (2023) |
op_relation |
https://www.aimspress.com/article/doi/10.3934/mbe.2023656?viewType=HTML https://doaj.org/toc/1551-0018 doi:10.3934/mbe.2023656 1551-0018 https://doaj.org/article/056d8d3f2ad046f7bbf900ac0f100a4b |
op_doi |
https://doi.org/10.3934/mbe.2023656 |
container_title |
Mathematical Biosciences and Engineering |
container_volume |
20 |
container_issue |
8 |
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14675 |
op_container_end_page |
14698 |
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