RT info:eu-repo/semantics/article
T1 Improve Quality of Service for the Internet of Things using Blockchain & Machine Learning Algorithms.
A1 Lawrence Nforh Chesuh, Lawrence
A1 Fernández Díaz, Ramón Ángel
A1 Alija Pérez, José Manuel
A1 Benavides Cuéllar, María del Carmen
A1 Alaiz Moretón, Héctor
A1 CheSuh, Lawrence Nforh
A1 Díaz, Ramón Ángel Fernández
A1 Perez, Jose Manuel Alija
A1 Cuellar, Cármen Benavides
A1 Moretón, Héctor Alaiz
A2 Lenguajes y Sistemas Informaticos
K1 Informática
K1 Quality of Service (QoS
K1 Internet of Things (IoT)
K1 Blockchain
K1 Machine Learning
K1 Classification accuracy
K1 Naive Bayes
K1 Decision Tree
K1 Ensemble Learning
K1 3301 Ingeniería y Tecnología Aeronáuticas
AB [EN] The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74% compared to NB and DT classifiers.
PB Elsevier
SN 2542-6605
LK https://hdl.handle.net/10612/18693
UL https://hdl.handle.net/10612/18693
NO Lawrence Nforh Chesuh, L.; Fernández Díaz, R. Á.; Alija Pérez, J. M.; Benavides Cuéllar, M. D. C.; Alaiz Moretón, H. (2024). Improve Quality of Service for the Internet of Things using Blockchain & Machine Learning Algorithms.. Internet of Things,
DS BULERIA. Repositorio Institucional de la Universidad de León
RD Jul 12, 2024