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Enhance data collection process of a UAV-aided low power iot wireless sensor network

Samarakkody, Pasan; Guruge, Sachith; Samaradeera, Dulaj; Jayatunga, Eranda; Porambage, Pawani (2021-05-07)

 
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https://doi.org/10.1109/WCNCW49093.2021.9419977

Samarakkody, Pasan
Guruge, Sachith
Samaradeera, Dulaj
Jayatunga, Eranda
Porambage, Pawani
Institute of Electrical and Electronics Engineers
07.05.2021

P. Samarakkody, S. Guruge, D. Samaradeera, E. Jayatunga and P. Porambage, "Enhance Data Collection Process of a UAV-aided Low Power IoT Wireless Sensor Network," 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2021, pp. 1-6, doi: 10.1109/WCNCW49093.2021.9419977

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doi:https://doi.org/10.1109/WCNCW49093.2021.9419977
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Abstract

Amalgamation of low power IoT devices in Wireless Sensor Networks (WSNs) has enhanced the usage of WSNs as it lessens the requirements of total power consumption. Most such IoT devices comparatively employ short-range radio access technologies such as Bluetooth Low Energy (BLE) to transfer sensor data. When a WSN is situated in a remote region such that neither regular human monitoring is plausible nor Internet coverage is readily available, it requires an alternative method to gather data from sensor nodes. Hence, in this research, an attempt was made to improve the data collection process and monitor the parametric performance of a low power IoT WSN aided by an Unmanned Aerial Vehicle (UAV). The impact of physical parameters related to different sensor node arrangements was investigated to effectively utilize the total flying time of the drone. Subsequently, adjustments were proposed for improvements by referring to previous literature. The Hamiltonian path defined in Graph Theory was applied to reduce the traverse distance of the drone further by tracing the sensor nodes within the grid area systematically. A prototype was implemented using commercially available devices to demonstrate the feasibility and the functionality of the proposed solution. Our results show that the proposed system increases the data collection performance by 23% compared to referenced architecture.

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