grand challenges in methodologies for complex networks

Ness ShroffDept. of ECE and CSEThe Ohio State UniversityE-mail: shroff@ece.osu.edu

Grand Challenges in Methodologies for Complex Networks

September 20, 2012

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

Examples of Complex NetworksCommunication Networks

InternetWireless & Sensor Networks

Online Social NetworksProfessional (LinkedIn…)Personal (Facebook, Twitter…)

Cyber-physical Smart-gridActuator based sensor networks

CloudData-center networks…

Methodological SuccessesStochastic optimization and control unified with

combinatorial techniquesMathematical Decomposition Framework Distributed and robust low-complexity protocols

Opportunistic scheduling (MAC)Congestion controlRoutingEnergy/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…)

Grand ChallengesAnalytical framework to design solutions that

simultaneously achieve: low complexity, high-throughput, and low delayDeep connections between calculus of variations, probabilistic

methods, limit theorems, and combinatorial techniquesControl “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

Thank you

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