Entropy-Based Sensing Schemes for Energy Efficiency in Massive MTC

TL;DR

This paper investigates how data correlation in massive machine-type communications can be exploited to design energy-efficient sensing schemes by selectively switching sensors off, maximizing entropy under power constraints.

Contribution

It introduces a novel entropy-based sensor switching technique leveraging the rank deficiency of correlation matrices in mMTC, formulated as a convex optimization problem.

Findings

The proposed method effectively maximizes entropy while reducing sensor power consumption.

Correlation matrices in mMTC are well approximated as rank deficient, enabling sensor selection.

The convex formulation allows efficient numerical solutions for sensor switching strategies.

Abstract

Machine-type communications (MTC) are crucial in the evolution of mobile communication systems. Within this context, we distinguish the so-called massive MTC (mMTC), where a large number of devices coexist in the same geographical area. In the case of sensors, a high correlation in the collected information is expected. In this letter, we evaluate the impact of correlation on the entropy of a set of quantized Gaussian sources. This model allows us to express the sensed data with the data correlation matrix. Given the nature of mMTC, these matrices may be well approximated as rank deficient. Accordingly, we exploit this singularity to design a technique for switching off several sensors that maximizes the entropy under power-related constraints. The discrete optimization problem is transformed into a convex formulation that can be solved numerically.