The Internet of Things (IoT) paradigm is defined as a complex large scale and distributed, and dynamic infrastructure composed of a huge number of heterogeneous devices. Identifying particular services provided by a massive number of IoT devices remains a challenging problem. The classical centralized discovery approaches are no more suitable. In our previous work, we have proposed an avatar-based Fog-Cloud architecture to support IoT object management. The avatars are defined as virtual entities of heterogeneous IoT objects. They are endowed with reasoning capabilities that make them able to coordinate with each other to accomplish an IoT application. Through this paper, we propose to extend our previous work by a new distributed mechanism for efficient discovery of IoT services relying on Social Networking (SN) and clustering methods. This is particularly interesting in large scale IoT systems since it allows to reduce the search space so that only the neighboring social avatars most apt to participate in the collaboration to accomplish an IoT application are considered. The proposed solution has been evaluated in connected vehicles context.
In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices. This number will exceed 70 billion by 2025 according to Statista a . Therefore, with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge. Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard. In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints. Mainly, the existing works consider static QoS. However, in real life scenarios, QoS of IoT services can be fluctuating. To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension. In this context, we propose a QoS fluctuation-aware selection approach of IoT services. To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services. The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem. Then a local selection using the obtained local constraints is performed in a parallel and distributed way. The performance of the proposed approach is evaluated and validated via experiment series.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.