1. Ness Shroff Dept. of ECE and CSE The Ohio State University E-mail: shroff@ece.osu.edushroff@ece.osu.edu Grand Challenges in Methodologies for Complex Networks September 20, 2012

2. Complex Networks Heterogeneous Mobile Dynamic System Rule-based or Selfish “agents” interact Multi-time scale Varied Aggregation Limited feedback Uncertainty (stochasticity) Local and Global (Resource) Constraints

3. Examples of Complex Networks Communication Networks Internet Wireless & Sensor Networks Online Social Networks Professional (LinkedIn…) Personal (Facebook, Twitter…) Cyber-physical Smart-grid Actuator based sensor networks Cloud Data-center networks…

4. Methodological Successes Stochastic optimization and control unified with combinatorial techniques Mathematical Decomposition Framework Distributed and robust low-complexity protocols Opportunistic scheduling (MAC) Congestion control Routing Energy/Power control… Glauber Dynamics (statistical physics) Global optima can be achieved through purely local interactions Focus: Long-term metrics (stability, throughput, lifetime, energy…) Less so on short-term metrics (delay, convergence speeds…)

5. Grand Challenges Analytical framework to design solutions that simultaneously achieve: low complexity, high-throughput, and low delay Deep connections between calculus of variations, probabilistic methods, limit theorems, and combinatorial techniques Control “meta-dynamics” taking into account user preferences, social interactions, cyber-physical interplay to achieve global behavior (optimality, consensus, equilibria…) New methodologies involving dynamic game theory, but now with underlying social/cyberphysical graph structures and user behavior (rational vs myopic behavior) Manage uncertainty and sensitivities to imperfections (e.g., feedback delays, errors, non-observability…) Breakthroughs in partially observable decision processes (POMDP) New learning techniques to infer system and user behavior in this highly dynamic setting

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