Optimization of Routing-Based Clustering Approaches in Wireless Sensor Network: Review and Open Research Issues
| Autor: | Rizwan Patan, Ganesh Gopal Deverajan, Amir H. Gandomi, Asha Jerlin Manuel |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science QoS lcsh:TK7800-8360 Throughput 02 engineering and technology computer.software_genre Open research energy consumption 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Cluster analysis throughput Quality of service lcsh:Electronics 020206 networking & telecommunications Energy consumption Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Data mining Routing (electronic design automation) Wireless sensor network computer Energy (signal processing) clustering |
| Zdroj: | Electronics, Vol 9, Iss 1630, p 1630 (2020) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics9101630 |
| Popis: | In today’s sensor network research, numerous technologies are used for the enhancement of earlier studies that focused on cost-effectiveness in addition to time-saving and novel approaches. This survey presents complete details about those earlier models and their research gaps. In general, clustering is focused on managing the energy factors in wireless sensor networks (WSNs). In this study, we primarily concentrated on multihop routing in a clustering environment. Our study was classified according to cluster-related parameters and properties and is subdivided into three approach categories: (1) parameter-based, (2) optimization-based, and (3) methodology-based. In the entire category, several techniques were identified, and the concept, parameters, advantages, and disadvantages are elaborated. Based on this attempt, we provide useful information to the audience to be used while they investigate their research ideas and to develop a novel model in order to overcome the drawbacks that are present in the WSN-based clustering models. |
| Databáze: | OpenAIRE |
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