@inproceedings{dargie-icccn-25,
  author = {Dargie, Waltenegus and Kidane, Zegeye Mekasha},
  title = {{Mitigating Cross-Technology Interference in Low-Power Wireless Networks}},
  booktitle = {2024 33rd International Conference on Computer Communications and Networks (ICCCN)},
  pages = {1-8},
  year = {2024},
  volume = {},
  number = {},
  abstract = {Emerging autonomous and semi-autonomous vehicles such as Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vessels (USV) use unlicensed frequency bands to interact with their remote control stations and peers. Typically, these systems are cost-intensive and their safe operation is of paramount importance. As a result, they establish powerful wireless communication links. When low-power IoT sensing nodes operate nearby, their performance can be considerably affected by cross-technology interference arising from the powerful systems. Different coexistence strategies have been proposed to deal with cross-technology interference, including dynamic channel-hopping, low-level spectrum sensing and channel adaptation, channel blacklisting, and direct cross-technology communication. These approaches require advanced spectrum scanning, detection, and clustering as well as knowledge of low-level packet structure and modulation schemes. In this paper, we propose a packet transmission strategy relying on link quality statistics alone to deal with cross-technology interference. Our approach does not require intimate knowledge of modulation schemes; nor does it require the modification of any hardware components. Evaluation based on traces of field experiments show that our approach improves Packet Delivery Ratio (PDR) by more than 30% when compared to baseline results and by more than 20% when compared to state-of-the-art solutions.},
  keywords = {Wireless sensor networks;Monte Carlo methods;Wireless networks;Modulation;Interference;Autonomous aerial vehicles;Peer-to-peer computing;Wireless sensor networks;UAV;link quality fluctuation;Poisson Process;Internet of Things},
  doi = {10.1109/ICCCN61486.2024.10637646},
}