r-Extreme Signalling for Congestion Control
This work addresses congestion management for smart city applications, but it appears incremental as it modifies an existing signaling model without introducing a new paradigm.
The paper tackles the problem of congestion control in smart cities by proposing a deterministic method using extreme values from a time window to signal costs, showing that the distribution of agents across resources converges under certain assumptions.
In many "smart city" applications, congestion arises in part due to the nature of signals received by individuals from a central authority. In the model of Marecek et al. [arXiv:1406.7639, Int. J. Control 88(10), 2015], each agent uses one out of multiple resources at each time instant. The per-use cost of a resource depends on the number of concurrent users. A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time. In this paper, the interval to broadcast per resource is obtained by taking the minima and maxima of costs observed within a time window of length r, rather than by randomisation. We show that the resulting distribution of agents across resources also converges in distribution, under plausible assumptions about the evolution of the population over time.