Butt, M. Majid and Dey, Indrakshi and Dzaferagic, Merim and Murphy, Maria and Kaminski, Nicholas and Marchetti, Nicola (2020) Agent-Based Modeling for Distributed Decision Support in an IoT Network. IEEE Internet of Things Journal, 7 (8). pp. 6919-6931. ISSN 2372-2541

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Abstract

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system.

Item Type:	Article
Additional Information:	Cite as: M. M. Butt, I. Dey, M. Dzaferagic, M. Murphy, N. Kaminski and N. Marchetti, "Agent-Based Modeling for Distributed Decision Support in an IoT Network," in IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6919-6931, Aug. 2020, doi: 10.1109/JIOT.2020.2976802.
Keywords:	Internet of Things; Analytical models; Mathematical model; Computational modeling; Biological system modeling; Decision making; Media Access Protocol;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15985
Identification Number:	https://doi.org/10.1109/JIOT.2020.2976802
Depositing User:	Dr Indrakshi Dey
Date Deposited:	24 May 2022 09:16
Journal or Publication Title:	IEEE Internet of Things Journal
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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