The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT
Open accessAbstract
The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices' transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Internet of Things in industries: A survey
Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172
Wireless communications with unmanned aerial vehicles: opportunities and challenges
Zeng, Y., Zhang, R., & Lim, T. J. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7470933 · Cited by 4,048
The Internet of Things (IoT): Applications, investments, and challenges for enterprises
Lee, I., & Lee, K. (2015) · Business Horizons · doi:10.1016/j.bushor.2015.03.008 · Cited by 2,803
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges
Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. H. D. N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2844296 · Cited by 1,467
UAV Communications for 5G and Beyond: Recent Advances and Future Trends
Li, B., Fei, Z., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2887086 · Cited by 1,377
Modeling air-to-ground path loss for low altitude platforms in urban environments
Al-Hourani, A., Kandeepan, S., & Jamalipour, A. (2014) · 2014 IEEE Global Communications Conference · doi:10.1109/glocom.2014.7037248 · Cited by 1,182
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications
Mozaffari, M., Saad, W., Bennis, M., & Debbah, M. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2751045 · Cited by 1,093
A Survey of Air‐to‐Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
Khawaja, W., Guvenc, I., Matolak, D. W., Fiebig, U., & Schneckenberger, N. (2020) · UAV Communications for 5G and Beyond · doi:10.1002/9781119575795.ch2 · Cited by 842
Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network
Zhan, C., Zeng, Y., & Zhang, R. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2017.2776922 · Cited by 784
Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications
Lazarescu, M. T. (2013) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2013.2243032 · Cited by 611
Cited by
Optimization Methods Applied to Motion Planning of Unmanned Aerial Vehicles: A Review
Israr, A., Ali, Z. A., Alkhammash, E. H., & Jussila, J. J. (2022) · Drones · doi:10.3390/drones6050126 · Cited by 61
Reliable and Efficient Data Collection in UAV-based IoT Networks
Joshi, P., Kalita, A., & Gurusamy, M. (2023) · arXiv · doi:10.48550/arxiv.2311.05303 · Cited by 61
“Using UAV data collectors, on the other hand, proved to be a more efficient strategy, allowing for faster and more reliable data collection [8].”
Metadata
- Authors
- Wang, Z., Liu, R., Liu, Q., Thompson, J. S., & Kadoch, M.
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-02
- Volume
- 7
- Issue
- 2
- Pages
- 1122-1139
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2984459839
- Cited by
- 308
- References
- 31
- FWCI
- 26.54
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT. https://app.cochat.ai/papers/10.1109/jiot.2019.2952364
- The paper
- Wang, Z., Liu, R., Liu, Q., Thompson, J. S., & Kadoch, M. (2020). Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2019.2952364
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.