# Lossy Asymptotic Equipartition Property For Geometric Networked Data Structures

## TL;DR

This paper extends the Asymptotic Equipartition Property to geometric networked data, specifically wireless sensor networks modeled as colored geometric random graphs, using large deviation principles.

## Contribution

It introduces a generalized AEP for CGRGs and applies it to real sensor network data for water quality monitoring.

## Key findings

- Extended AEP to CGRGs using large deviation techniques
- Validated theoretical results with real sensor network data
- Provided insights into data compression for geometric network models

## Abstract

This article extends the Generalized Asypmtotic Equipartition Property of Networked Data Structures to cover the Wireless Sensor Network modelled as coloured geometric random graph (CGRG). The main techniques used to prove this result remains large deviation principles for properly defined empirical measures on CGRGs. As a motivation for this article, we apply our results to some data from Wireless Sensor Network for Monitoring Water Quality from a Lake..