Learning-aided SDC control in mobile ad hoc networks

Abstract Software Defined Coalition (SDC) is a promising architecture for flexibly controlling network resources in tactical environments. In highly dynamic mobile ad hoc network scenarios, however, the SDC controllers may be fragmented from the mobile nodes they manage rendering impossible their in-time resource reconfiguration and causing severe network performance degradation. To address this challenge, we explore the idea of complementing the SDC controllers with distributed control mechanisms running locally at the mobile nodes in a hybrid architecture design. We then apply learning methods to predict the network dynamics and fragmentation events, so that we can tune the routing policies in time to prevent network failures from happening. We show the benefits of our hybrid approach in a demo emulating a real tactical ad hoc network scenario using the Anglova tactical ad hoc network dataset.
  • Qiaofeng Qin (Yale)
  • Konstantinos Poularakis (Yale)
  • Andreas Martens (IBM UK)
  • Kevin Chan (ARL)
  • Leandros Tassiulas (Yale)
Date Apr-2021
Venue Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, SPIE DCS, 2021