Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks
Abstract This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12646 |
| _version_ | 1797745310482563072 |
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| author | Dongji Li Shaoyi Xu Yan Li |
| author_facet | Dongji Li Shaoyi Xu Yan Li |
| author_sort | Dongji Li |
| collection | DOAJ |
| description | Abstract This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data. |
| first_indexed | 2024-03-12T15:21:29Z |
| format | Article |
| id | doaj.art-14a22ce9d5d148878a09b6fd2b9f9288 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-12T15:21:29Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-14a22ce9d5d148878a09b6fd2b9f92882023-08-11T03:57:48ZengWileyIET Communications1751-86281751-86362023-08-0117141706172010.1049/cmu2.12646Massive heterogeneous data collecting in UAV‐assisted wireless IoT networksDongji Li0Shaoyi Xu1Yan Li2School of Electronic and Information Engineering Beijing Jiaotong University Beijing ChinaSchool of Electronic and Information Engineering Beijing Jiaotong University Beijing ChinaSchool of Electronic and Information Engineering Beijing Jiaotong University Beijing ChinaAbstract This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data.https://doi.org/10.1049/cmu2.12646bandwidth allocationdata aggregationInternet of Thingsoptimisation |
| spellingShingle | Dongji Li Shaoyi Xu Yan Li Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks IET Communications bandwidth allocation data aggregation Internet of Things optimisation |
| title | Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks |
| title_full | Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks |
| title_fullStr | Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks |
| title_full_unstemmed | Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks |
| title_short | Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks |
| title_sort | massive heterogeneous data collecting in uav assisted wireless iot networks |
| topic | bandwidth allocation data aggregation Internet of Things optimisation |
| url | https://doi.org/10.1049/cmu2.12646 |
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