Enhancing energy efficiency in IoT-WSN systems via a hybrid crow search and firefly algorithm

  • In the realm of enterprise technology, Internet of Things (IoT)-based wireless devices have witnessed significant advancements, enabling seamless interactions among machines, sensors, and physical objects. A critical component of IoT, Wireless Sensor Networks (WSN), have proliferated across various real-time applications, influencing daily life in both critical and non-critical domains. These WSN nodes, typically small and battery-operated, necessitate efficient energy management. This study focuses on the integration of crow search optimization and firefly algorithms to optimize energy efficiency in IoT-WSN systems. It has been observed that the energy reserve (RE) of a node and its communication costs with the base station are pivotal in determining its likelihood of becoming a Cluster Head (CH). Consequently, energy-saving data aggregation techniques are paramount to prolonging network longevity. To this end, a hybrid approach combining crow search and firefly optimization has beenIn the realm of enterprise technology, Internet of Things (IoT)-based wireless devices have witnessed significant advancements, enabling seamless interactions among machines, sensors, and physical objects. A critical component of IoT, Wireless Sensor Networks (WSN), have proliferated across various real-time applications, influencing daily life in both critical and non-critical domains. These WSN nodes, typically small and battery-operated, necessitate efficient energy management. This study focuses on the integration of crow search optimization and firefly algorithms to optimize energy efficiency in IoT-WSN systems. It has been observed that the energy reserve (RE) of a node and its communication costs with the base station are pivotal in determining its likelihood of becoming a Cluster Head (CH). Consequently, energy-saving data aggregation techniques are paramount to prolonging network longevity. To this end, a hybrid approach combining crow search and firefly optimization has been proposed. The crow search algorithm plays a significant role in enhancing data transfer efficiency, while the firefly algorithm is instrumental in selecting optimal cluster heads. This integrated methodology not only promises to extend the network’s lifespan but also ensures a balance between energy conservation and data transmission efficacy.show moreshow less

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Metadaten
Author:Thembelihle DlaminiORCiDGND, Weston Mwashita
URN:urn:nbn:de:bvb:384-opus4-1174562
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117456
ISSN:2958-1907OPAC
ISSN:2958-1915OPAC
Parent Title (English):Journal of Sustainability for Energy
Publisher:Acadlore Publishing Services Limited
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2024/12/10
Volume:2
Issue:4
First Page:197
Last Page:206
DOI:https://doi.org/10.56578/jse020403
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Physik
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Physik / Professur für Quantencomputing und Quantengeräte
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)