# Integration of AI in Self-Powered IoT Sensor Systems
## 8 Discussion
RQ2 aims at research directions that should integrate IoT sensors with AI, particularly ML. Based on the values extracted from the analysis, five primary directions have been identified:  
1. Standardization of IoT sensor taxonomy to approach a universal classification framework, based on which comparisons between works can be made. 2. Develop optimized ML algorithms for the hardware of self-powered sensors by integrating less-explored models in the literature. 3. Addressing end-to-end systems that allow for AI integration, using data interpretation, and covering the entire chain--capture, local processing, and automated decision-making. To integrate such a component, interdisciplinary collaboration between hardware engineers, AI researchers, and software developers is necessary. 4. Creating interconnected platforms in the IoT field, as many works in the specialized literature address the domains in isolation. One direction is to develop a holistic solution that considers the effects of a decision in one domain on others, for example, the automated adjustment of the home environment based on data provided by a wearable device. 5. Comparative studies between self-powering technologies are needed because, although TENG dominates the literature, comparing performances between TENG, piezoelectric, BFC, HEX, or hybrid sources is limited. Future research should evaluate these technologies under real usage conditions, particularly to assess them in the long term. The compatibility with existing technical standards is highlighted in Table 6. Self-powered sensors based on TENG, reconfigurable intelligent surface (RIS)-indexed modulation [263], and chaotic schemes such as Differential Chaos Shift Keying (DCSK) [247] are limited by current technical standards. Additionally, the level of market acceptance is low compared to established protocols such as long range (LoRa) [264], ZigBee [265], BLE [266], Narrowband Internet of things (NB-IoT) [267], etc. New technologies are not yet standardized, which necessitates a series of modified hardware and software architectures. For example, emerging solutions require the development of dedicated circuits for energy harvesting and management in the case of TENG. The lack of standardization makes it more difficult for new technologies to be widely adopted. Therefore, future research directions should include a stage of analysis of compatibility with communication protocols, platforms, market openness, consumer reluctance, and existing infrastructure. Additionally, proposals for gradual integration strategies are necessary to facilitate the transition without compromising the functionality of existing systems. In Table 6, the value of the upper solution was marked with 1 and the value of the lower solution with 0. Analyzing Table 6, it is observed that emerging technologies are superior in terms of energy efficiency, being able to function without a battery. In contrast, established technologies are superior in terms of compatibility, standardization, integration into current infrastructure, market openness, and the existence of applications that integrate them. RQ3 analyzes which field should be prioritized for accelerating AI technologies combined with powered IoT sensors, and the bibliometric analysis shows that the healthcare IoT sector is the most active in this area. That is precisely why the fields that should be intensively explored are those in the agricultural industry due to their importance in ensuring food for the population and animals. The impact in this regard would be twofold: it would increase resources and provide an approach to optimizing food production. Therefore, the authors believe that agricultural research integrating AI and powered sensors should be a priority. The bibliometric analysis conducted in this paper demonstrates a geographical dispersion of publications that includes locations across multiple continents. This geographical diversity reflects the global concern for the development of self-powered IoT sensors integrated with AI algorithms.