State Estimation for Tactical Networks: Challenges and Approaches

Tactical Networks are difficult and complex environments characterized by multiple restrictions that affect network behaviors, protocols, and systems. This paper discusses challenges encountered related to network state estimation, particularly while deploying such a capability within realistic tactical networks. Approaches to mitigate these challenges within our Smart Estimation of Network State Information (SENSEI) framework are discussed, along with techniques to integrate state estimation into other adaptive protocols and middleware for tactical networks. The performance of SENSEI in the context of the Anglova scenario - a realistic emulation scenario for tactical networks, is analyzed and presented. We hope these observations and results would be of use to other researchers developing similar dynamically adaptive middleware for tactical networks.