# Recent Trends in AI-Based Intelligent Sensing
## 7 Conclusions  
The continuous growth in intelligent sensing raises challenges related to the integration, communication, safety, and adaptation of algorithms in different stages and applications. This paper has presented a survey of AI-enabled intelligent sensing and its technology requirements, opportunities, and future directions. In the beginning, we pointed out the AI technology in intelligent sensing. Then we summarized the contributions of the work, highlighting key areas in intelligent sensing. We have reviewed various learning models with comparative analysis. Parameters that affect the performance of  
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**Project** & **Funding Finormalization** & **Technology Used in the Project** & **Aiaen of the Project** \\ \hline MILOA [20] & The European Union's Horizon 2000 Research and Innovation Programme & & Autonomous underwater technology, GPS, satellite communication, and solar panels. & This project design the WAY deliver units for some observing and monitoring systems. \\ \hline AMACH [20] & National Institute of Ocean Technology and IIT Methods, India & & Artificial intelligence, underwater navigation and imaging. &  
intelligent sensing are discussed based on the results of recent research. Then available datasets for use in intelligent sensing are presented to help the research community explore further. They represent a broad spectrum of datasets that have been used fruitfully in AI and intelligent sensing research. Advantages and limitations, format of information, and elucidations are provided. Next, we have presented the review of practical applications, including intelligent sensing in healthcare, pandemic monitoring, assistive technology, smart sensor networks, among others. The list is by no means exhaustive but instead serves to exemplify the ample applications of intelligent sensing. In addition, we have elaborated on the challenges and future research directions in intelligent sensing, pointing out challenges related to data security and privacy, data storage, power consumption, and hardware deployment. It is observed that intelligent sensing will grow more rapidly with communication technology and edge computing. Therefore, its involvement in data fusion, Industry 4.0, Industry 5.0, explainable AI, latency minimization, future citizenship, extended reality, convergence of AI and 6G, and software platforms in intelligent sensing is discussed in future research directions. Finally, we have presented noteworthy projects in intelligent sensing, mentioning project names, sources, technology used, and aims of the projects. These projects are dispersed in many countries and represent the use of intelligent sensing in diverse areas globally. We believe this work will help researchers get a deeper understanding of the different aspects of AI-enabled intelligent sensing. Conceptualization, A.S.; methodology, A.S.; writing--original draft preparation, A.S., V.S., M.J., H.-C.W., D.N.K.J. and C.M.W.B. ; writing--review and editing, A.S., H.-C.W., D.N.K.J. and C.M.W.B. ; funding acquisition, A.S., H.-C.W., D.N.K.J. and A.M. All authors have read and agreed to the published version of the manuscript. This work was funded, in part, by the Fundacao para a Ciencia e a Tecnologia, Portugal under Grant No. UIDB/04111/2020 (COPELABS), by the Ministry of Science and Technology, Taiwan, and Rebit Digital, grant number MOST 110-2622-E-197-001, by the International Cooperation project of the Sri Lanka Technological Campus, Sri Lanka, No. RRSG/19/5008, and Department of Science and Technology (DST), India under project name Sign Language to Regional Language Converter (SLRLC) with project number SEED/TIDE/063/2016. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: