Vol.37, No.2, 2021, pp.149-158, doi:10.32604/csse.2021.014608
A Novel IoT Application Recommendation System Using Metaheuristic Multi-Criteria Analysis
  • Mohammed Hayder Kadhim, Farhad Mardukhi*
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran
* Corresponding Author: Farhad Mardukhi. Email:
(This article belongs to this Special Issue: Sensors and Nano-sensors Technologies for Health-Care Applications)
Received 02 October 2020; Accepted 28 October 2020; Issue published 01 March 2021
There are a variety of Internet of Things (IoT) applications that cover different aspects of daily life. Each of these applications has different criteria and sub-criteria, making it difficult for the user to choose. This requires an automated approach to select IoT applications by considering criteria. This paper presents a novel recommendation system for presenting applications on the IoT. First, using the analytic hierarchy process (AHP), a multi-layer architecture of the criteria and sub-criteria in IoT applications is presented. This architecture is used to evaluate and rank IoT applications. As a result, finding the weight of the criteria and sub-criteria requires a metaheuristic approach. In this paper, a sequential quadratic programming algorithm is used to find the optimal weight of the criteria and sub-criteria automatically. To the best of our knowledge, this is the first study to use an analysis of metaheuristic criteria and sub-criteria to design an IoT application recommendation system. The evaluations and comparisons in the experimental results section show that the proposed method is a comprehensive and reliable model for the construction of an IoT applications recommendation system.
Internet of Things; smart objects; recommendation system; multi-criteria analysis; sequential quadratic programming
Cite This Article
M. Hayder Kadhim and F. Mardukhi, "A novel iot application recommendation system using metaheuristic multi-criteria analysis," Computer Systems Science and Engineering, vol. 37, no.2, pp. 149–158, 2021.
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