About: Improving Coverage Area in Sensor Deployment Using Genetic Algorithm

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Improving Coverage Area in Sensor Deployment Using Genetic Algorithm
Creator	Abdou, Wahabou Ezin, Eugène Gouton, Pierre Tossa, Frantz
Source	PMC
abstract	Wireless sensor networks (WSN) are a collection of autonomous nodes with a limited battery life. They are used in various fields such as health, industry, home automation. Due to their limited resources and constraints, WSNs face several problems. One of these problems is the optimal coverage of a observed area. Indeed, whatever the domain, ensuring optimal network coverage remains a very important issue in WSNs, especially when the number of sensors is limited. In this paper, we aim to cover a two-dimensional Euclidean area with a given number of sensors by using genetic algorithm in order to find the best placement to ensure a good network coverage. The maximum coverage problem addressed in this paper is based on the calculation of the total area covered by deployed sensor nodes. We first, define the problem of maximum coverage. For a given number of sensors, the proposed algorithm find the best position to maximize the sensor area coverage. Finally, the results show that the proposed method well maximize the sensor area coverage.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50426-7_30
has license	no-cc
sha1sum (hex)	5a2e92b00c7ba9d245b88f567802f3b84d881af0
schema:url	https://doi.org/10.1007/978-3-030-50426-7_30
resource representing a document's title	Improving Coverage Area in Sensor Deployment Using Genetic Algorithm
has PubMed Central identifier	PMC7302559
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:5a2e92b00c7ba9d245b88f567802f3b84d881af0#body_text
is schema:about of	named entity 'The' named entity 'sensor' named entity 'area' named entity 'number' named entity 'coverage' named entity 'position' named entity 'For' named entity 'WSN' named entity 'PAPER' named entity 'PROPOSED METHOD' named entity 'COVERAGE' named entity 'TO COVER' named entity 'NUMBER OF' named entity 'ONE OF' named entity 'SENSOR' named entity 'HEALTH' named entity 'COVERED BY' named entity 'NETWORK' named entity 'ENSURE' named entity 'GOOD' named entity 'EUCLIDEAN' named entity 'VERY IMPORTANT' named entity 'FACE' named entity 'RESULTS' named entity 'AUTONOMOUS' named entity 'GIVEN' named entity 'ORDER' named entity 'autonomous' named entity 'sensors' named entity 'Indeed' named entity 'two-dimensional' named entity 'algorithm' named entity 'limited' named entity 'based' named entity 'area' named entity 'sensors' named entity 'problem' named entity 'two-dimensional' named entity 'sensor' named entity 'MIGA' named entity 'sensor' named entity 'WSN' named entity 'natural selection' named entity '1, 2' named entity 'sensor' named entity 'Monte Carlo method' named entity 'sensor' named entity 'home automation' named entity 'Genetic Algorithm' named entity 'sensor' named entity 'sensor' named entity 'multiple-choice' named entity 'fitness function' named entity 'genetic algorithm' named entity 'biological evolution' named entity 'sensor' named entity 'WSN' named entity 'sensor' named entity 'genotype' named entity 'coverage model' named entity 'phenotype' named entity 'sensor' named entity 'sensor node' named entity 'crossover operator' named entity 'sensor' named entity 'sensor' named entity 'mathematical modeling' named entity 'WSN' named entity 'genetic operators' named entity 'sensor' named entity 'Sensor' named entity 'wireless technologies' named entity 'coverage model' named entity 'WSN' named entity 'algorithm' named entity 'sensor' named entity 'WSNs' named entity 'genotype' named entity 'sensor' named entity 'WSN'

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