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Algorithms and Optimization. Aravind Srinivasan University of Maryland. State-of-the-art, recent advances. Protocol Design individual layers: e.g., random-access protocols with good efficiency ratio cross-layer optimization; e.g., MAC+routing Capacity-estimation

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algorithms and optimization

Algorithms and Optimization

Aravind Srinivasan

University of Maryland

state of the art recent advances
State-of-the-art, recent advances
  • Protocol Design
    • individual layers: e.g., random-access protocols with good efficiency ratio
    • cross-layer optimization; e.g., MAC+routing
  • Capacity-estimation
    • well-developed for “random” instances
    • beginnings of algorithmic (worst-case) approaches
  • Selfishness (initial stages) and locality
  • The role of random walks (opt., resource discovery, epid. protocols, diffusion, …)
open problems
Open Problems
  • Distributed Linear Programming for wireless, more general optimization
  • Capacity vs. latency
  • Traffic models (for all of the above): periodic, gradually-varying? Adversarial queuing theory?
  • New measures: e.g., interaction between lifetime maximization and Markov-Chain conductance
  • Group-Steiner models for relays
  • Rigorous analysis of random access for emerging standards
desired advances at phy layer
Desired advances at PHY layer
  • Realistic models that are amenable to analysis (e.g., latency-minimization for SINR model)
  • Overheads of new technologies: e.g., in opportunistic freq. assignment (lessons from WDM)
challenges for future networks
Challenges for future networks
  • Need for distributed alg.s; even a standard definition is lacking (theory suggests polylogarithmic convergence-time)
  • Understanding of emerging technologies, e.g., cognitive/MCMR networks. Sample questions:
    • incorporate delays due to channel-hopping into latency-minimization alg.s
    • channel assignment in heterogeneous MCMR networks
  • Robustness:fault/attack models, robustness against node inactivity (e.g., directed diffusion)
gaps discussion
Gaps, Discussion
  • Models: for new technologies (e.g., MCMR, cognitive), mobility, fault-tolerance
  • How much re-optimization is feasible? Continually-improving algorithms, stochastic opt.
  • Potentially very rich collaboration between “CS theory” and “networking”: graph theory, geometry, distributed and randomized alg.s, security, adversarial models, self-stabilization, …
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