Resource Indexing and Querying in Large Connected Environments

Fouad Achkouty, Richard Chbeir (), Laurent Gallon, Elio Mansour and Antonio Corral
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Fouad Achkouty: Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France
Richard Chbeir: Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France
Laurent Gallon: Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 40000 Mont de marsan, France
Elio Mansour: Scient Analytics, 10 Impasse Grassi, 13100 Aix-en-Provence, France
Antonio Corral: Department of Computer Science, University of Almeria, 04120 Almeria, Spain

Future Internet, 2023, vol. 16, issue 1, 1-27

Abstract: The proliferation of sensor and actuator devices in Internet of things (IoT) networks has garnered significant attention in recent years. However, the increasing number of IoT devices, and the corresponding resources, has introduced various challenges, particularly in indexing and querying. In essence, resource management has become more complex due to the non-uniform distribution of related devices and their limited capacity. Additionally, the diverse demands of users have further complicated resource indexing. This paper proposes a distributed resource indexing and querying algorithm for large connected environments, specifically designed to address the challenges posed by IoT networks. The algorithm considers both the limited device capacity and the non-uniform distribution of devices, acknowledging that devices cannot store information about the entire environment. Furthermore, it places special emphasis on uncovered zones, to reduce the response time of queries related to these areas. Moreover, the algorithm introduces different types of queries, to cater to various user needs, including fast queries and urgent queries suitable for different scenarios. The effectiveness of the proposed approach was evaluated through extensive experiments covering index creation, coverage, and query execution, yielding promising and insightful results.

Keywords: IoT; resource indexing; resource querying; date engineering (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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