A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

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Title: A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection
Authors: Delaney, Declan
O'Hare, G. M. P. (Greg M. P.)
Permanent link: http://hdl.handle.net/10197/8194
Date: 1-Dec-2016
Abstract: No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: MDPI
Keywords: Internet of ThingsModellingStandardised testingFrameworkWireless sensor networks
DOI: 10.3390/s16122038
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
Earth Institute Research Collection

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