Research

Research Summary

My research focuses on routing, resource allocation, and energy-efficient design of networked systems, with an emphasis on wireless ad hoc networks, underwater acoustic sensor networks, and drone-assisted aerial networks. I develop optimization-based models and increasingly combine them with reinforcement learning to address scalability and uncertainty.

More recently, I have been exploring hybrid classical–quantum network routing, investigating how physical constraints such as entanglement lifetime and network reliability influence routing and control decisions.

Key Research Topics

Operations Research and Mathematical Optimization for Networked Systems

Development of linear, integer, and mixed-integer programming (MILP) models, including network flow–based formulations, for complex optimization problems in communication networks. Core themes include network lifetime maximization, energy efficiency, resource allocation, energy–delay–reliability trade-offs, and resilience under physical, topological, and security constraints.

Wireless Ad Hoc and Underwater Acoustic Sensor Networks

Design and optimization of energy-efficient routing, topology control, and communication strategies for terrestrial wireless ad hoc networks and underwater acoustic sensor networks. Research topics span network lifetime & energy-efficiency analysis, k-connectivity–based reliability, multi-sink architectures, void-region mitigation, adversarial effects, and operation under harsh and resource-constrained environments.

Resilient Drone-Assisted Aerial Networks

Design and restoration of resilient k-connected drone networks using integer programming and heuristic optimization techniques. This research investigates mobility-aware connectivity restoration, minimum-movement strategies, and topology adaptation in grid-based aerial deployments. Recent work develops exact optimization models and scalable heuristics to balance resilience, execution time, and mobility cost in drone-assisted communication systems.

Hybrid Optimization and Learning-Based Network Control

Integration of mathematical optimization frameworks with machine learning and reinforcement learning techniques to enable adaptive, data-driven, and scalable control of complex networked systems. Current work explores hybrid optimization–learning methodologies for dynamic decision-making and emerging paradigms such as hybrid classical–quantum network routing architectures.

Code & Learning Resources

• wsn-opt-python: A hands-on Python tutorial for network-flow–based optimization models in wireless sensor networks, intended for students and researchers.
  

• Underwater Networks – Basics: An introductory document covering core principles of underwater acoustic communication and sensor networks.