Clustered data muling in the internet of things in motion

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dc.contributor.authorTuyishimire, Emmanuel
dc.contributor.authorBagula, Antoine
dc.contributor.authorIsmail, Adiel
dc.date.accessioned2023-02-20T10:25:30Z
dc.date.available2023-02-20T10:25:30Z
dc.date.issued2019
dc.description.abstractThis paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sensors for efficient and accurate data collection. In this case, it would be expensive for one UAV to visit all the sensors; hence the need to partition the ground network into an optimum number of clusters with the objective of having the UAV visit only cluster heads (fewer sensors). In such a setting, the sensor readings (sensor data) would be sent to cluster heads where they are collected by the UAV upon its arrival. This paper proposes a clustering scheme that optimizes not only the sensor network energy usage, but also the energy used by the UAV to cover the area of interest. The computation of the number of optimal clusters in a dense and uniformly-distributed sensor network is proposed to complement the k-means clustering algorithm when used as a network engineering technique in hybrid UAV/terrestrial networks. Furthermore, for general networks, an efficient clustering model that caters for both orphan nodes and multi-layer optimization is proposed and analyzed through simulations using the city of Cape Town in South Africa as a smart city hybrid network engineering use-case.en_US
dc.identifier.citationTuyishimire, E. et al. (2019). Clustered data muling in the internet of things in motion. Sensors ,19(3), 484. https://doi.org/10.3390/s19030484en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s19030484
dc.identifier.urihttp://hdl.handle.net/10566/8440
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectClusteringen_US
dc.subjectHybrid networken_US
dc.subjectComputer securityen_US
dc.subjectComputer scienceen_US
dc.subjectSouth Africaen_US
dc.titleClustered data muling in the internet of things in motionen_US
dc.typeArticleen_US

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