ARTIFICIAL INTELLIGENCE BASED APPROACH FOR IOT SERVICES COMPOSITION BY ANALYZING THE USER NEEDS
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Date
2023
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Rabah BOUCETTI
Abstract
In the Internet of Things (IoT) environment, the services provided by the connected objects
are published as services through the web. This allows to machines to interact between them
and, makes the IoT services composition possible. However, the vast proliferation of smart
object generates services with the same functionalities but different in terms of quality of
services (QoS) proprieties. This makes the satisfaction of the user requirements often complex
and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of
QoS) is a challenge, due to the high number of candidate services for the composition. This
challenge consists of selecting the most appropriate services so that the composite service
must meet both the functional and the non-functional requirements of the user. To deal with
this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural
Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale
environments. The combination between GA and NN allows finding the quasi-optimal IoT
service composition. This latter is based on global QoS optimization. To reach this objective,
the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical
composition. Then, the proposed first GA is used to obtain the ideal theoretical composition
with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the
inappropriate concrete IoT services, and retain only the services having the same categories of
the atomic theoretical services composing the ideal theoretical composition. This allows us to
optimize the search space as well as the execution time. Finally, we apply the second GA on
the concrete services of the retained categories, in order to obtain the IoT service concrete
composition with an overall QoS optimization. The simulation results show that the proposed
approach has the best composition time, the best Hypervolume indicator and the best
compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On
another side, it has almost the same performances compared to TS-QCA and, it finds the nearoptimal composition in a very short time compared to PSA, which is an optimal approach.
Thus, the obtained results show the effectiveness of our approach.