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Algorithms and OptimizationPowerPoint Presentation

Algorithms and Optimization

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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

- 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

- 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

- 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

- 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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