# Integration of AI in Self-Powered IoT Sensor Systems
## 5 Self-Powered IoT Sensors Identification and Applications
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Battery-Powered IoT**} & \multirow{2}{*}{**Self-Powered IoT Sensor**} \\  & & **Sensor** \\ \hline \multirow{2}{*}{Energy Source} & \multirow{2}{*}{Internal battery} & Harvested from the \\  & & & environment (motion, light, etc.) \\ \hline Maintenance & Periodic battery & \\ Required & replacement & No maintenance needed \\ \hline Operational & \multirow{2}{*}{Prone to interruptions due} & Continuous and \\ Continuity & & to battery depletion & autonomous operation \\ \hline \multirow{2}{*}{Long-Term Cost} & Higher (battery & Lower (no recurring \\ replacement, labor) & energy cost) \\ \hline Deployment & Limited in remote or & Ideal for remote, embedded, or \\ Suitability & inaccessible areas & high-density systems \\ \hline Sustainability & Low (battery waste, higher & High (eco-friendly and \\  & energy demand) & energy efficient) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of battery-powered and self-powered IoT sensors. applications where energy comes from renewable sources, rule-based adaptive strategies are studied to maintain the availability of wireless sensor nodes when energy levels temporarily drop [190]. In this way, the continuation of monitoring and data transmission is ensured. The implementation of intelligent battery management systems improves the way energy is delivered and distributed to sensor components [191]. Regarding battery degradation and lifespan, the intelligent policy adapted to the operating conditions of the sensors reduces the negative impact of degradation, maintaining the quality of service provided by them. Compared to classical discharge methods, charge and discharge cycles extend the lifespan of batteries, thereby preventing premature failures [192]. The use of modern charging techniques, such as adaptive or pulsed charging, reduces battery wear [191]. Wireless solutions for battery management are the most convenient solutions for sensor implementation due to reduced maintenance complexity, energy efficiency, increased long-term reliability, and user convenience [193]. The efficiency with which self-powered IoT sensors harvest energy is closely correlated with environmental conditions [194]. These conditions contribute to the performance and autonomy of the systems. For example, in the case of TENGs sensors, the conversion efficiency decreases in environments with high humidity or very low temperatures. This is due to the density of triboelectric charge [195]. Regarding TENGs sensors, they depend on the temperature difference between the ambient environment and the heat source. In this way, under constant temperature conditions, efficiency decreases. In the case of applications taking place in indoor environments, solar sensors generate insufficient energy in the absence of direct light [196]. Studies [178; 180; 197] highlight these limitations, where hybrid power supply strategies or AI algorithms for self-adaptive operation concerning available energy address these issues. Consequently, the way these systems are designed must relate to computational techniques associated with realistic operating scenarios in which environmental conditions are explicitly mentioned in the design stage.