1 research outputs found
Dynamic Pricing and Mean Field Analysis for Controlling Age of Information
Today many mobile users in various zones are invited to sense and send back
real-time useful information (e.g., traffic observation and sensor data) to
keep the freshness of the content updates in such zones. However, due to the
sampling cost in sensing and transmission, a user may not have the incentive to
contribute the real-time information to help reduce the age of information
(AoI). We propose dynamic pricing for each zone to offer age-dependent monetary
returns and encourage users to sample information at different rates over time.
This dynamic pricing design problem needs to well balance the monetary payments
as rewards to users and the AoI evolution over time, and is challenging to
solve especially under the incomplete information about users' arrivals and
their private sampling costs. After formulating the problem as a nonlinear
constrained dynamic program, to avoid the curse of dimensionality, we first
propose to approximate the dynamic AoI reduction as a time-average term and
successfully solve the approximate dynamic pricing in closed-form. Further, by
providing the steady-state analysis for an infinite time horizon, we show that
the pricing scheme (though in closed-form) can be further simplified to an
-optimal version without recursive computing over time. Finally,
we extend the AoI control from a single zone to many zones with heterogeneous
user arrival rates and initial ages, where each zone cares not only its own AoI
dynamics but also the average AoI of all the zones in a mean field game system
to provide a holistic service. Accordingly, we propose decentralized mean field
pricing for each zone to self-operate by using a mean field term to estimate
the average age dynamics of all the zones, which does not even require many
zones to exchange their local data with each other.Comment: arXiv admin note: text overlap with arXiv:1904.0118