Internet of Things Garage

Efficient Resource Discovery

Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, called Cloud of Things, plays a key role in managing the connected things, their data, and services. One of the main challenges in the Cloud of Things is the resource discovery process to enable applications to use a specific resource. Most of the existing work uses some kind of multi-criteria decision analysis technique to perform the resource discovery, which acts as a black box where a set of user constraints are used as input parameters to select a set of resources as output. However, these works do not evaluate the quality and characteristic of the selected resources regarding the varying criteria and their possible weights reflecting user needs.

In this project, we analyze the quality and characteristics of three multi-criteria decision analysis techniques (SAW, TOPSIS, and VIKOR) and the impact of user constraints on them. We evaluate the quality of the proposed solutions in terms of capacity and diversity. The results show that the algorithms have statistical equivalency regarding capacity and the VIKOR algorithm achieve the highest diverseness on average, which indicates the proposed set of solutions has different characteristics then SAW and TOPSIS algorithms.

Team

 
 
 
 

Funding

FAPESP

The Sao Paulo Research Foundation (FAPESP) is an independent public foundation with the mission to foster research and the scientific and technological development of the State of Sao Paulo.

USP

The University of Sao Paulo (USP) is a public university in the Brazilian state of Sao Paulo. It is the largest Brazilian public university and the country's most prestigious educational institution.

Outcomes

Journal
Luiz H. Nunes, Julio C. Estrella, Charith Perera, Stephan Reiff-Marganiec, Alexandre N. Delbem, Multi-Objective Internet of Things Resource Discovery through Pareto-optimality Criterion, Software: Practice and Experience Journal (SPE), Volume 47, Issue 10, 2017, Pages 1325-1341 (16)

Journal
Luiz Nunes, Julio Estrella, Luis Nakamura, Rafael de Libardi, Carlos Ferreira, Liuri Jorge, Charith Perera, Stephan Reiff-Marganiec, A Distributed Sensor Data Search Platform for Internet of Things Environments, International Journal of Services Computing (IJSC) , Volume 4, Issue 1, Pages 1-12, 2016 (12)

Workshop
Luiz Henrique Nunes, Julio Cezar Estrella, Charith Perera, Stephan Reiff-Marganiec, Alexandre Cladio Botazzo Delbem, The Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things Environments, Proceedings of 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, March, 2018, Pages 1-7 (7)

Workshop
Luiz Henrique Nunes, Julio Cezar Estrella, Charith Perera, Stephan Reiff-Marganiec, Alexandre Cladio Botazzo Delbem, The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study, Proceedings of the 9th International Conference on Utility and Cloud Computing (UCC) , Shaghai, China, December, 2016, Pages 245-250 (6)