A Reinforcement Learning QoS Negotiation Model for IoT Middleware

Itorobong Udoh, Gerald Kotonya

2020

Abstract

A large number of heterogeneous and mobile devices interacting with each other, leading to the execution of tasks with little human interference, characterizes the Internet of Things (IoT) ecosystem. This interaction typically occurs in a service-oriented manner facilitated by an IoT middleware. The service provision paradigm in the IoT dynamic environment requires a negotiation process to resolve Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences. This paper proposes a negotiation model that allows negotiating agents to dynamically adapt their strategies using a model-based reinforcement learning as the QoS preferences evolve and the negotiation resources changes due to the changes in the physical world. We use a simulated environment to illustrate the improvements that our proposed negotiation model brings to the QoS negotiation process in a dynamic IoT environment.

Paper Citation

in Harvard Style

Udoh I. and Kotonya G. (2020). A Reinforcement Learning QoS Negotiation Model for IoT Middleware.In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-426-8, pages 205-212. DOI: 10.5220/0009350102050212

in Bibtex Style

@conference{iotbds20,
author={Itorobong Udoh and Gerald Kotonya},
title={A Reinforcement Learning QoS Negotiation Model for IoT Middleware},
booktitle={Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2020},
pages={205-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009350102050212},
isbn={978-989-758-426-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - A Reinforcement Learning QoS Negotiation Model for IoT Middleware
SN - 978-989-758-426-8
AU - Udoh I.
AU - Kotonya G.
PY - 2020
SP - 205
EP - 212
DO - 10.5220/0009350102050212