# Sensor scheduling with time, energy and communication constraints

## TL;DR

This paper introduces new convex relaxation algorithms for sensor scheduling that optimize estimation accuracy while balancing energy and communication constraints over multiple time instances.

## Contribution

It presents novel algorithms for sensor scheduling with power and communication constraints, directly minimizing MSE and providing theoretical bounds and experimental comparisons.

## Key findings

- Algorithms outperform state-of-the-art methods in simulations.
- Provides average case and lower bounds for MSE.
- Balances energy, communication, and accuracy effectively.

## Abstract

In this paper we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature.