## 1 Introduction  
Damage detection and further diagnosis via structural-health monitoring could be improved with smart sensors that periodically gather, order, and make data available to stakeholders. With the increase of Internet of Things (IoT) applications in the industry, smart sensors are becoming a crucial component, acting not only as transducers, but as an integral solution that incorporates various capabilities into the value chain. Moreover, smart sensors are being applied in several fields, such as healthcare, civil engineering, the automotive industry, and power generation by extending the traditional functions of sensors, i.e., a transducer plus signal conditioning. A smart sensor is an electronic device that takes inputs from the physical world, converts the data from one physical domain to another, and contains a signal-conditioning stage. It also processes data through a central processing unit (CPU), and provides information from the gathered data for further decision making. Recently, with the development of new material technologies, smart sensors have also incorporated energy-harvesting, IoT, and cloud-computing capabilities. Structural-health monitoring (SHM) and further diagnosis are activities that have benefited from the development of smart sensors (including intelligent materials). The integrity of automotive structures, e.g., vehicle chassis, aircraft-wing structures, or structural supports in buildings, is of great economic value, and require measurements of mechanical vibrations and signal conditioning for data processing. Various approaches to monitoring structures have been reported. Strain gauges are probably the most widely studied and applied transducers to measure strain and mechanical vibrations on structures, but they present some drawbacks. In general terms, strain gauges generate a change in their electrical resistance that is proportional to the applied force that causes an elongation of the gauge. Moreover, the use of strain gauges requires a signal-conditioning stage called the Wheatstone bridge, an intensely studied electrical circuit to measure unknown electrical resistance [1]. Although they have been widely considered, the main disadvantages of conventional strain gauges are their high nonlinearity, temperature compensation, and the need for constant calibration to correctly function [2]. For these reasons, researchers are motivated to replace strain gauges with other materials that do not present these disadvantages. In the last decade, a type of smart material that has been used as a force-electrical-resistance transducer is the elastomer. Specifically, this work refers to magnetorheological elastomers (MREs) that, rather than cross-linked structures, were applied as force-electrical-signal transducers within rubber molecules due to qualities such as high tensile strength, thermal stability, and increased number of linear structures [3; 4]. An MRE is a composite with sensitive magnetic particles that are suspended on a polymeric matrix. MREs belong to a new group of composites that have physical or chemical properties that can be controlled by external stimuli such as magnetic or electric fields, stresses, temperature, moisture, and pH [5]. Magnetorheological (MR) phenomena in elastomers manifest as a change in the mechanical properties of a viscoelastic material by applying an external magnetic field [6; 7]. The availability of required energy in smart sensors is an area of research that has become crucial in recent years. Batteries have been serving as the primary power source to drive these devices. Although batteries have their advantages, they are expensive, take up a significant amount of space, add weight to the sensor, need to be replaced or recharged, and, when discarded, they are highly polluting. In this context, the results of innovative schemes have been focused on harvest energy as an alternative to using batteries. A promising approach that relies on power extraction from the ambient is known as energy harvesting.