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Tuyishimire E, Bagula A, Ismail A. Clustered Data Muling in the Internet of Things in Motion. Sensors (Basel). 2019;19(3)doi: 10.3390/s19030484.
Tuyishimire, E., Bagula, A., & Ismail, A. (2019). Clustered Data Muling in the Internet of Things in Motion. Sensors (Basel, Switzerland), 19(3), . https://doi.org/10.3390/s19030484
Tuyishimire, Emmanuel, et al. "Clustered Data Muling in the Internet of Things in Motion." Sensors (Basel, Switzerland) vol. 19,3 (2019). doi: https://doi.org/10.3390/s19030484
Tuyishimire E, Bagula A, Ismail A. Clustered Data Muling in the Internet of Things in Motion. Sensors (Basel). 2019 Jan 24;19(3). doi: 10.3390/s19030484. PMID: 30682867; PMCID: PMC6387288.
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Sensors (Basel). 2019 Jan 24;19(3). doi: 10.3390/s19030484.

Clustered Data Muling in the Internet of Things in Motion.

Sensors (Basel, Switzerland)

Emmanuel Tuyishimire, Antoine Bagula, Adiel Ismail

Affiliations

  1. ISAT Laboratory, University of the Western Cape, Cape Town, Bellville 3575, South Africa. [email protected].
  2. ISAT Laboratory, University of the Western Cape, Cape Town, Bellville 3575, South Africa. [email protected].
  3. ISAT Laboratory, University of the Western Cape, Cape Town, Bellville 3575, South Africa. [email protected].

PMID: 30682867 PMCID: PMC6387288 DOI: 10.3390/s19030484

Abstract

This 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.

Keywords: Clustering; Hybrid network; UAV

References

  1. Sensors (Basel). 2015 Jan 13;15(1):1365-88 - PubMed
  2. Sensors (Basel). 2015 Jun 30;15(7):15443-67 - PubMed
  3. Sensors (Basel). 2015 Dec 23;16(1):null - PubMed
  4. Sensors (Basel). 2018 Jul 06;18(7):null - PubMed

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