Google Gravity Balloon | Tested & Working |

The optimization problem: maximize the number of user-hours connected given constraints on battery (solar recharge rate), wind prediction error, and balloon longevity. This became a partially observable Markov decision process (POMDP) with >10^6 state variables.

1. Introduction: The 95% Problem In 2011, Google X (now X Development) proposed a radical solution to a persistent economic reality: while satellites offered global coverage but were expensive and high-latency, and cell towers offered high bandwidth but were geographically limited, nearly 95% of the world’s population lived within range of a cellular signal—yet only half were connected. The problem wasn't coverage; it was economic viability in rural and remote regions. google gravity balloon

Project Loon was born from a counterintuitive question: What if the cell tower floated? The optimization problem: maximize the number of user-hours

Loon required —a fully sealed, rigid envelope that maintains internal pressure higher than the external atmosphere at all times. The challenge: as the sun heats the balloon, internal pressure rises, stressing the polyethylene film. Introduction: The 95% Problem In 2011, Google X

Loon’s envelope used helium. To lift a 15 kg payload (electronics + batteries) plus a 15 kg envelope, the balloon required displacing ~30 kg of air. At 20 km altitude (pressure ≈ 50 hPa), the volume needed is: