Mobile distributed mesh network optimization with a black box optimizer

Frederick M. Chache; Sean Maxon; Ram M. Narayanan; Ramesh Bharadwaj

doi:10.1117/12.2622391

27 May 2022 Mobile distributed mesh network optimization with a black box optimizer

Frederick M. Chache, Sean Maxon, Ram M. Narayanan, Ramesh Bharadwaj

Author Affiliations +

Proceedings Volume 12108, Radar Sensor Technology XXVI; 121080O (2022) https://doi.org/10.1117/12.2622391
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Mobile Ad-hoc Networks are a growing field of interest. They have many real-world applications, such as enabling internet connected sensors to operate in environments without pre-existing infrastructure. In past work, we have demonstrated that the Long Range (LoRa) radio frequency (RF) modulation technique, in conjunction with a mesh network can meet these needs in static networks. To extend this to applications with mobile nodes, several adaptations have been implemented to extend the original B.A.T.M.A.N (Better Approach to Mobile Ad-hoc Networking) mesh network algorithm. Node movement models were developed and tested to improve simulation accuracy. We also implemented situationally aware, machine learning (ML) based, route discovery techniques to ensure adequate network information is available in dynamic environments, without adding excessive overhead in static situations. To optimize these changes, a Black Box Optimizer was used in conjunction with an event-based simulation tool to train the ML model.

Conference Presentation

Citation Download Citation

Frederick M. Chache, Sean Maxon, Ram M. Narayanan, and Ramesh Bharadwaj "Mobile distributed mesh network optimization with a black box optimizer", Proc. SPIE 12108, Radar Sensor Technology XXVI, 121080O (27 May 2022); https://doi.org/10.1117/12.2622391

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