Your browser doesn't support javascript.
loading
Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study.
Nazib, Rezoan Ahmed; Moh, Sangman.
Afiliação
  • Nazib RA; Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea.
  • Moh S; Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea.
Sensors (Basel) ; 21(8)2021 Apr 16.
Article em En | MEDLINE | ID: mdl-33923854
ABSTRACT
Owing to automation trends, research on wireless sensor networks (WSNs) has become prevalent. In addition to static sinks, ground and aerial mobile sinks have become popular for data gathering because of the implementation of WSNs in hard-to-reach or infrastructure-less areas. Consequently, several data-gathering mechanisms in WSNs have been investigated, and the sink type plays a major role in energy consumption and other quality of service parameters, such as packet delivery ratio, delay, and throughput. However, the data-gathering schemes based on different sink types in WSNs have not been investigated previously. This paper reviews such data-gathering frameworks based on three different types of sinks (i.e., static, ground mobile, and aerial mobile sinks), analyzing the data-gathering frameworks both qualitatively and quantitatively. First, we examine the frameworks by discussing their working principles, advantages, and limitations, followed by a qualitative comparative study based on their main ideas, optimization criteria, and performance evaluation parameters. Next, we present a simulation-based quantitative comparison of three representative data-gathering schemes, one from each category. Simulation results are shown in terms of energy efficiency, number of dead nodes, number of exchanged control packets, and packet drop ratio. Finally, lessons learned from the investigation and recommendations made are summarized.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2021 Tipo de documento: Article