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.
Internet of Things (IoT) has become a fast growing research topic in recent years. Internet connected sensors and devices allow for the collection and processing of a wealth of data. This in conjunction with sensor fusion can provide greater accuracy in object recognition and detection surpassing what could be obtained by sensors operating independently. However these distributed sensors must often operate in environments with poor to no access to the internet which can greatly reduce their effectiveness. Additionally these sensors can be attached to highly dynamic platforms further complicating communication and data routing. One possible solution is to use the B.A.T.M.A.N. (Better Approach To Mobile Ad hoc Networking) routing protocol adapted for use with LoRa, a low power long range RF protocol, to route sensor data through other nodes in order to reach internet access points and allow these devices to interact with the cloud that would have otherwise been unable. Other adaptations to the algorithm will be investigated, such as including other sensors, like GPS and message signal strength to better predict route quality. This system shows promise to be an effective, fault-tolerant solution for this application.
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