# Recent Trends in AI-Based Intelligent Sensing
### Related Works  
Many researchers have conducted surveys related to intelligent sensing models to tackle challenging issues of particular applications and provide solutions to cope with existing vulnerabilities. However, most of the existing survey articles on intelligent sensing have not explicitly focused on new methods based on AI and ML/DL for real-time applications and associated research challenges. The survey in [15] was conducted from two viewpoints. The first is the intelligent approaches based on AI to solve issues related to wireless sensor networks (WSNs) and the second is to design intelligent applications that incorporate sensor networks. In [16], the authors have discussed the research directions of AI 2.0 and the new models based on AI technology. New forms of intelligent manufacturing systems are also explored. Various AI algorithms are implemented as estimators (i.e., software sensors) in chemical operating units and their advantages are shown. Practical implications and limitations were also discussed for the proper design of AI-based estimators in [17]. In [18], the authors have focused mainly on different intelligent techniques used in vehicular applications and listed research challenges and issues in the integration of AI and vehicular systems. In [19], the authors have discussed AI algorithms coupled with gas sensor arrays (GSAs) embedded in robots as electronic noses to explore potential applications such as gas explosive detection, environmental monitoring, beverage and food production and storage. They also discussed the types of gas sensors, gas sensor limitations and possible solutions. Another application based on intelligent sensing was given in [20; 21]. They focused on the use of ML and AI technology to fight the coronavirus pandemic. The studies used AI-based embedded sensors to track the spread of COVID-19 infections and side effects, thereby helping health professionals to diagnose common symptoms of the virus. The article [22] surveys the future of healthcare technologies for H-IoT. It summarizes the features of H-IoT systems based on generic IoT systems. Several ML and DL methods were reviewed in [23] for big data applications together with open issues and research directions. Different ML-based algorithms to address issues of WSNs (i.e., congestion control, synchronization, and energy harvesting) were surveyedin [24] and their drawbacks were discussed. An overview of current data mining and ML techniques employed for activity recognition (AR) were presented in [25]. The authors also discussed how an activity is captured using different sensors. In [26], the authors reviewed how recent ML and DL algorithms can be coupled with sensor technologies for particular sensing applications. They have also compared a new smart sensing system based on ML with a conventional sensing system and discussed its future opportunities. A comprehensive survey of various DL algorithms that can be applied to sensor data for predictive maintenance was provided in [27]. Ref. [28] performs a comprehensive survey of the applications of the DL models for different network layers, that includes data link layer, physical layer, routing layer etc. Literature review in [29] is based on the ML algorithms, which were used to solve the WSN issues in the period of 2002-2013. Also, this paper investigates the ML solutions to enhance the functional behaviors of WSNs, for example, quality of service (QoS) and data integrity. Table 1 summarizes some recent survey articles in the field of intelligent sensing with their advantages and limitations. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference Number** & **Year** & **Technology Used** & **Elucidation and Comments** & **Advantages** & **Limitations** \\ \hline
[30] & 2019 & IoT scenarios variables, sensor analysis and application analysis. & The details of emerging IoT scenarios are discussed.