## 1 Introduction  
The internet of things (IoT) is composed of numerous wireless sensor networks (WSN), which consists of sensors, communications, a processing unit, and a power source. Each system can have unique components for a specific application, but each WSN needs a power source for sensing, electronics, and data transmission. Currently these are powered by batteries, which fail over time. As the demand for the IoT increases, the need to create a sustainable energy source to power the WSN increases. Numerous energy harvesting methods have been developed to resolve this issue including photovoltaics, thermal, and vibration or kinetic energy harvesters [1, 2]. Vibration-based energy harvesters offer more diversity as most applications have some mechanical vibration that operates continuously. Kinetic energy harvesters can use different transduction mechanisms such as electromagnetic, electrostatic, piezoelectric, or triboelectric, but piezoelectric offers significant advantages such as high voltage output and less complex fabrication techniques for microelectromechanical systems (MEMS). There has been some initial research demonstrating the integration of kinetic energy harvesters into WSN at both the micro and macro scales [3, 4]. However,MEMS-based kinetic energy harvesters present several challenges which have limited their success. Challenges associated with MEMS kinetic energy harvesters include low power density, narrow bandwidth, lack of frequency tuning, and unreliability. Extensive research has been conducted on methods of increasing power density through optimizing materials, stress, electrode configuration, power management, and cantilever design [5; 6; 7; 8; 9; 10; 11]. Cantilevers are defined as a thin structure with a fixed end and a free end. MEMS energy harvesters typically have a narrow bandwidth (1-3 Hz) as they need a high Q factor to increase power density, but a narrow bandwidth prevents the device from being used in applications with varying resonant frequencies. Researchers have investigated numerous methods to increase bandwidth using non-linear dynamics [12; 13; 14], creating an array of devices with different frequencies [6; 15] and movable mass [16; 17; 18; 19; 20; 21; 22; 23], and using repulsive and attractive forces to slow or accelerate the cantilevers' dynamics [24; 25; 26; 27]. However, most of these methods either significantly decrease the power generated or they only slightly increase bandwidth to cover resonate frequency errors due to manufacturing. Most applications where energy harvesters would be used to power WSN have low-frequency vibrations (<250 Hz) and low accelerations (<1 g), but the frequency can change from application to application which makes creating a universal device difficult. Tuning the frequency is another viable method to optimize power generation by matching the resonant frequency of the energy harvester to the vibration frequency. There are two scenarios where tuning the frequency is necessary. The first is during operation; if the frequency spectrum of the vibration changes and the bandwidth of the device is narrow, then the power generated will be significantly lower than expected. In this case, it would be ideal if the energy harvester's frequency could be tuned to match the new vibration frequency. There have been several attempts to overcome this, including methods of altering the stiffness through light or electrical stimulation [28; 29]. Other methods include altering the mass distribution using magnetics [30; 31; 32]. However, these methods require energy to tune the frequency, thus reducing the overall power generated. The second scenario that requires tuning is during manufacturing. The advantage of MEMS energy harvesters is their ability to be batch fabricated to reduce cost; but with a narrow bandwidth (1-3 Hz), this means each device would need to be custom designed to meet the end-users' specifications for a particular application, which significantly reduces the appeal of using MEMS energy harvesters.