Employing Intelligent Aerial Data Aggregators for the Internet of Things: Challenges and Solutions
Open accessAbstract
Internet-of-Things (IoT) devices equipped with temperature and humidity sensors and cameras are increasingly deployed to monitor remote and human-unfriendly areas (e.g., farmlands, forests, rural highways, and electricity infrastructures). Aerial data aggregators such as autonomous drones provide a promising solution for collecting sensory data of IoT devices in human-unfriendly environments, enhancing network scalability and connectivity. The flexibility of a drone and favorable line-of-sight connection between the drone and IoT devices can be exploited to improve data reception at the drone. This article first discusses challenges of the drone-assisted data aggregation in IoT networks, such as incomplete network knowledge at the drone, limited buffers of the IoT devices, and lossy wireless channels. Next, we investigate the feasibility of onboard deep-reinforcement-learning-based solutions to allow a drone to learn its cruise control and data collection schedule online. For deep reinforcement learning in a continuous operation domain, deep deterministic policy gradient (DDPG) is suitable to deliver effective joint cruise control and communication decision, using its outdated knowledge of the IoT devices and network states. A case study shows that the DDPG-based framework can take advantage of the continuous actions to substantially outperform existing non-learning-based alternatives.
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Devices in WFL can often be less secure than servers or desktop computers used in wired FL.
“These devices can often be less secure than servers or desktop computers that might be used in wired FL [82].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [82]
WFL cannot always guarantee the security of the training process.
“Recent works have shown that WFL cannot always guarantee the security of the training process [46], [82], [93], [105], [106].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [82]
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- Authors
- Li, K., Ni, W., Noor, A., & Guizani, M.
- Affiliations
- Real-Time and Embedded Computing Systems Research Centre,Portugal
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Qatar University (QA)
- Venue
- IEEE Internet of Things Magazine
- Published
- 2022-03
- Volume
- 5
- Issue
- 1
- Pages
- 136-141
- ISSN
- 2576-3180 (ISSN-L)
- 2576-3199
- OpenAlex
- W4280601641
- Cited by
- 11
- References
- 14
- Citations by year
- 2026: 1 · 2024: 4 · 2023: 5 · 2022: 1
- FWCI
- 1.11
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Internet of Things Magazine (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- Funders
- FCT
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, autonomous drones, deep reinforcement learning, deep deterministic policy gradient, cruise control, data collection scheduling, remote area monitoring, temperature and humidity sensors
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