Please use this identifier to cite or link to this item: https://repository.rsif-paset.org/xmlui/handle/123456789/155
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dc.contributor.authorEffah, Emmanuel-
dc.contributor.authorThiare, Ousmane-
dc.contributor.authorWyglinski, Alexander-
dc.date.accessioned2022-04-01T16:30:56Z-
dc.date.available2022-04-01T16:30:56Z-
dc.date.issued2021-02-15-
dc.identifier.urihttps://repository.rsif-paset.org/xmlui/handle/123456789/155-
dc.descriptionConference paper presented at the 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), 18 Nov.-16 Dec. 2020 at Victoria, BC, Canada.en_US
dc.description.abstractIn this paper, we propose a new multi-objective optimization (MOO) framework to maximize power consumption and coverage stability of the clustering-based Agricultural Internet of Things (CA-IoT). The planning, design, and operational phases of CA-IoT networks give rise to energy management, connectivity, and application-related challenges which often result in conflicting MOO problem. The correlations amongst these objectives and their impacts on the network lifespan and operational efficiencies remain unresolved. The impacts and correlations amongst the core MOO decision metrics for our framework are uniquely established from an extensive characterization and implementation of a real CA-IoT network. Sample results from a CA-IoT network based on our MOO Framework performed better than the state of the art in terms of network lifespan, network stability periods, and coverage stability.en_US
dc.publisherIEEE Xploreen_US
dc.subjectClustering-based Agricultural Internet of Things (CA-IoT), Multi-objective Optimization(MOO), Cluster head (CH)en_US
dc.titleMulti-Objective Optimization Modeling of Clustering-Based Agricultural Internet of Thingsen_US
dc.typePresentationen_US
Appears in Collections:ICTs including Big Data and Artificial Intelligence

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