This thesis presents the design and evaluation of a low-power wireless sensor system intended to operate in conjunction with a vibrational piezoelectric energy harvester and a power conditioning circuit inside a plane wing. The system was tested in a controlled laboratory environment under various configurations, including different transmission modes, power levels, and sampling frequencies, to assess its performance, energy efficiency, and stability. Key findings indicate that sampling frequency significantly influences measurement stability. Continuous transmission mode, where data is sent without delay, while offering high temporal resolution, introduced greater variability in temperature readings, likely due to environmental noise and power supply fluctuations. In contrast, burst mode, which only sends 3 measurements in quick succession, achieves a better balance between resolution and stability, suggesting that incorporating intentional delays can mitigate noise sensitivity. Thermal recovery tests revealed that sampling density affects response time, with delayed mode showing slower returns to baseline, possibly due to self-heating or insufficient data resolution. An attempt to implement a low-power sleep mode revealed that actual current consumption did not decrease as expected, indicating an incomplete power-down mode and highlighting the need for further investigation into power management. The power consumption study confirmed that oscillator frequency and transmission power significantly affect current draw, although an anomaly observed at 4 MHz in delayed mode suggests potential efficiency zones in the transceiver’s operation. Integration with the energy harvester demonstrated the system’s ability to function under energy-constrained conditions. Testing with different storage capacitors showed that a 680 𝜇F capacitor provided the best balance between data transmission quality and energy availability. These results highlight trade-offs between energy storage capacity and responsiveness, and emphasize the importance of hardware optimization in energy-autonomous sensor systems.