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X-APPLE-TRAVEL-ADVISORY-BEHAVIOR:AUTOMATIC
233926
20260401T112230Z
DTSTART;TZID=America/New_York:20260422T130000
DTEND;TZID=America/New_York:2
 0260422T150000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/ece-p
 hd-dissertation-defense-maya-flores
ECE PhD Dissertation Defense by: Maya Flores
\n\nImage\n  \n\n\n\nTitle:\nResilient Networking Strategies for Manned-Unm
 anned Teaming Under Adversarial Conditions\n\nAbstract:\nManned–unmanned
  teaming networks integrate human-operated platforms with unmanned systems
 , such as UAVs, to enhance situational awareness, mobility, and mission ef
 fectiveness in complex environments. The success of these teams depends cr
 itically on reliable wireless communication for coordinating task assignme
 nts and data exchange. However, the control and data links that sustain th
 is coordination are vulnerable to disruption from congestion, mobility-ind
 uced intermittency, and adversarial interference such as smart jamming. Th
 ese challenges motivate the development of resilient scheduling, routing, 
 and decision-making frameworks that adapt to uncertain and dynamic network
  conditions.\nThis dissertation investigates resilient networking strategi
 es for manned–unmanned teaming networks. First, we propose centralized s
 cheduling policies that incorporate redundancy in scheduling information t
 o improve control-message delivery under adversarial conditions. We also d
 evelop a hybrid scheduling policy that opportunistically switches between 
 centralized and decentralized scheduling, improving delay performance at l
 ow arrival rates while maintaining stability under heavy traffic by levera
 ging the benefits of both approaches. Second, we formulate a decision fram
 ework for relaying traffic from the unmanned swarm to a constrained manned
  controller as a Constrained Markov Decision Process (CMDP) under intermit
 tent link failures. We present simple threshold-based relay policies, and 
 we use Lagrangian relaxation and dynamic programming to identify policies 
 that minimize delay subject to a relay constraint. Finally, we investigate
  swarm mobility in a search and rescue mission scenario using path loss-ba
 sed wireless models, where the unmanned swarm must identify a loiter locat
 ion that balances reliable communication with the manned platform and reli
 able detection of an emitter of interest. We study the use of multi-armed 
 bandit algorithms for loiter-location decision making when the swarm does 
 not have complete knowledge of the communication environment. These algori
 thms enable the swarm to learn effective positioning strategies under unce
 rtainty and communication-sensing tradeoffs. Collectively, these contribut
 ions aim to provide a unified analytical and computational foundation for 
 adaptive and resilient networking in manned–unmanned teams, enabling eff
 ective coordination and robust performance in contested, resource-limited 
 environments.\n\nResearch Advisor:\nProf. Alex Wyglinski\nECE Department, 
 WPI\n\nCommittee Members:\nProf. Bo Tang\nECE Department, WPI\nProf. Bashi
 ma Islam\nECE Department, WPI\nDr. Thomas Stahlbuhk\nMIT Lincoln Laborator
 y\n
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