Problem
Research questions and friction points this paper is trying to address.
IoT data pricing
LEO satellite constellations
pricing cliff
geographic pricing asymmetry
store-and-forward
Pricing IoT Data Delivered via LEO Satellites
🤖 AI Summary
This study addresses the pricing cliff and geographic pricing asymmetry arising from discontinuous data delivery in store-and-forward low Earth orbit (LEO) satellite architectures. We propose an integrated pricing model that combines SGP4 orbital simulation, buffered traffic modeling, and decision-window value decay. The pricing cliff phenomenon is formally defined, and an analytical formula for geographic premium is derived without requiring residual value assumptions. Experimental results demonstrate that expanding constellation size significantly suppresses geographic premiums; specifically, with 25 satellites, the premium falls below 1% while exhibiting diminishing marginal returns. These findings provide a theoretical foundation for differentiated pricing strategies in satellite-terrestrial networks.
📝 Abstract
IoT terminals served by LEO satellite constellations transmit data to passing satellites in discrete uplink windows. That data is delivered to buyers only when the satellite reaches a ground station. This store-and-forward structure makes the achievable price a discontinuous function of the delivery completeness threshold (a phenomenon we call a pricing cliff) and creates a geographic pricing asymmetry between polar and equatorial terminals that depends on constellation size. We present an integrated pricing model combining a full SGP4 orbital simulation of the deployed Kinéis constellation with a buffer flow model and a value decay framework with decay constants derived from operational decision-window timescales in the application literature and anchored to observed satellite imagery market prices. We show that polar and equatorial terminals are dominated by structurally different latency components requiring different infrastructure interventions, formally characterize the pricing cliff phenomenon and its commercial significance, and derive a geographic pricing premium that is exactly independent of the residual value assumption. Going from 1 to 25 satellites reduces the polar--equatorial premium by 25x, with diminishing returns beyond n=10; at full constellation size the premium falls below 1% of peak data value and remains below 1.5% across the full plausible range of buyer urgency.
Innovation
Methods, ideas, or system contributions that make the work stand out.
LEO satellite IoT pricing
SGP4 orbital simulation
pricing cliff
geographic pricing premium
value decay framework
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