Multi-Objective Optimization Modeling of Clustering-Based Agricultural Internet of Things
Publication Type
Conference Proceeding
Publication Date (Issue Year)
2020
Journal Name
IEEE Vehicular Technology Conference, 2020-November
Abstract
In 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.
Keywords
Clustering-based Agricultural Internet of Things (CA-IoT), Multi-objective Optimization(MOO), Cluster head (CH)
Rsif Scholar Name
Emmanuel Effah
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Recommended Citation
Effah, E., Ousmane Thiare, O., & Wyglinski, A. (2020). Multi-Objective Optimization Modeling of Clustering-Based Agricultural Internet of Things. IEEE Vehicular Technology Conference, 2020-November https://doi.org/10.1109/VTC2020-FALL49728.2020.9348460