Papers for

drone network operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Fixed server assignments show growing inefficiency with uneven workloads

The Fixed Server Locality Gap of Count Load Assignment Games

Abstract: Marginal-contribution pricing makes the social objective an exact potential, but does not ensure that every stable assignment is efficient. We study indivisible clients with heterogeneous workloads, arbitrary nonnegative assignment costs, and private eligibility menus over a fixed number of shared servers and optional local execution. Each server's social cost is its occupancy multiplied by its aggregate workload and a server-specific coefficient. For every fixed server cap $M$, we prove that the price of anarchy over this class is $Θ_M(κ^{1-2^{-M}})$ as the workload-ratio cap $κ$ grows. The upper bound applies to every equilibrium and every feasible comparison, without an acyclic-comparison or strongly-connected-component restriction. Its proof combines a common-multiplier certificate, a source-weighted residual inequality and an ordered moment recurrence. A matching chain family has a realization by mobile unmanned aerial vehicle (UAV) clients and fixed ground servers, with positive altitude, positive-width coverage and an explicitly paid wireless baseline. We prove exact waypoint elimination and give an input-computable component refinement. Scheduling and congestion identities delimit which established bounds transfer. Exact enumeration of 2,800 synthetic games identifies inefficient equilibria, while 270 primary-parameterized synthetic instance-budget records evaluate incremental latency relative to a feasible multistart reference. Tightness concerns the heterogeneity exponent for fixed $M$, not matching leading constants or measured frequency of the worst-case configurations.

Fri 11 SeptComputer Science and Game Theory
The gist
The paper looks at how tasks with different sizes get assigned to shared servers when each server has limits. The authors study how the system's worst inefficiency grows as workloads vary more widely, finding precise mathematical bounds. They also show this problem can model real-world scenarios like drones connecting to fixed stations. Their results help identify when stable task assignments are less efficient and clarify what affects these outcomes.
Open 2609.12572v1