The Internet of Things (IoT) enabled photoplethys-mogram (PPG) monitoring devices have gained more attention for understanding the initial health status, remotely monitoring health parameters over time and getting valuable clinical in-formation anytime, anywhere. Continuous transmission of PPG data wirelessly consumes a significant amount of energy, which is one of the constraints of battery-operated IoT devices. In this paper, we present a lightweight quality-aware waveform change detection (QA-WCD) method for enabling event-triggered communication protocol that can maximize the lifetime of connected devices. The QA-WCD method consists of preprocessing, Fourier transform (FT) based noise suppression and pulse rate (PR) estimation, onset-peak detection using derivative PPG (dPPG) and waveform change detection with five decision rules. On three standard databases, the proposed QA-WCD method achieves promising results in detecting waveform changes segment-wise and cycle-wise using the proposed PPG parameters. Evaluation results show that the method had a sensitivity and specificity of 100% in detecting cycle changes (CCs) and no cycle changes (NoCCs), respectively for the PPG signals with less prominent diastolic wave portions. The QA-WCD based data transmission protocol can increase the service lifetime of IoT devices and re-duce network/server traffic in remote personal health monitoring with huge IoT devices connected with health service providers. © 2023 IEEE.