Age of Gossip With Cellular Drone Mobility
Version age scaling depends on drone speed vs. dissemination speed tradeoff
A new paper from University of Maryland researchers introduces a theoretical model for information freshness in cellular networks with drone mobility. The network includes n nodes within cells, where nodes gossip with each other in fully-connected fashion within a cell. A mobile drone receives updates from an external source and disseminates them to nodes in the cell it currently occupies. The drone's movement between cells follows a continuous-time Markov chain (CTMC), and the system is analyzed using the version age of information metric to quantify freshness.
The key finding is a dual-bottleneck effect: the version age scaling of nodes is constrained by the slower of two processes — drone mobility speed (λ_m(n)) and drone dissemination rate (λ_d(n)). If λ_d(n) is much larger than λ_m(n), age scales with the inverse of λ_m(n), independent of λ_d(n). Conversely, if λ_m(n) dominates, age scales with the inverse of λ_d(n). Interestingly, the expected duration between drone-to-cell services depends only on the CTMC stationary distribution and λ_d(n), not on λ_m(n). However, version age instability makes high-probability analysis challenging for general CTMCs, so the authors focus on the fully-connected drone mobility model to demonstrate this tradeoff.
- Version age scales with the slower of drone mobility speed (λ_m(n)) and dissemination rate (λ_d(n)), creating a dual-bottleneck
- Expected drone-to-cell service duration depends on CTMC stationary distribution and dissemination rate, not mobility speed
- Analysis uses n nodes and f(n) cells; fully-connected drone mobility model simplifies high-probability bounds
Why It Matters
This model guides drone-assisted IoT networks toward optimal update freshness by balancing mobility and dissemination.