# Energy Efficient WSN: a Cross-layer Graph Signal Processing Solution to Information Redundancy

## TL;DR

This paper proposes a cross-layer graph signal processing approach to optimize sensor sampling in wireless sensor networks, balancing data reconstruction accuracy with network lifetime extension.

## Contribution

It introduces an iterative method for selecting sensor nodes that preserves network lifetime while maintaining data reconstruction quality.

## Key findings

- Reconstruction RMSE can be traded off for more disjoint sampling sets.
- The approach linearly improves network lifetime.
- Validated on real-life dataset.

## Abstract

In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.