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Title	Drone-Assisted Load Distribution Framework For Traffic Optimization In Iot Networks
ID_Doc	21080
Authors	Soni P.; Mense O.M.; Kanti Addya S.
Year	2025
Published	International Conference on Communication Systems and Networks, COMSNETS, 2025
DOI	http://dx.doi.org/10.1109/COMSNETS63942.2025.10885760
Abstract	Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms. © 2025 IEEE.
Author Keywords	DBS; Greedy Optimization; IoT; Traffic Load Balancing

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