# A Cross-Layer Approach to Data-aided Sensing using Compressive Random Access

## TL;DR

This paper presents a cross-layer data-aided sensing approach for IoT that leverages compressive sensing and random access to efficiently reconstruct signals with minimal measurements, even under transmission errors.

## Contribution

It introduces a novel node selection criterion and a compressive transmission request mechanism for efficient data collection in IoT using compressive sensing techniques.

## Key findings

- Achieves accurate signal reconstruction with fewer active nodes.
- Demonstrates robustness to transmission request errors.
- Provides error analysis and simulation validation.

## Abstract

In this paper, data-aided sensing as a cross-layer approach in Internet-of-Things (IoT) applications is studied, where multiple IoT nodes collect measurements and transmit them to an Access Point (AP). It is assumed that measurements have a sparse representation (due to spatial correlation) and the notion of Compressive Sensing (CS) can be exploited for efficient data collection. For data-aided sensing, a node selection criterion is proposed to efficiently reconstruct a target signal through iterations with a small number of measurements from selected nodes. Together with Compressive Random Access (CRA) to collect measurements from nodes, compressive transmission request is proposed to efficiently send a request signal to a group of selected nodes. Error analysis on compressive transmission request is carried out and the impact of errors on the performance of data-aided sensing is studied. Simulation results show that data-aided sensing allows to reconstruct the target information with a small number of active nodes and is robust to nodes' decision errors on compressive transmission request.