# Recent Trends in AI-Based Intelligent Sensing
## 5 Challenges and Future Research Directions
### Future Directions
The work presented in [186] shows the performance of Byzantine-tolerant machine learning algorithms in Industry 5.0 with the involvement of edge computing technology. The goal of Industry 5.0 is to empower rather than replace workers. Moreover, applications of Industry 5.0 extend well beyond industrial production. For instance, Industry 5.0 can provide customized therapy and treatment to COVID-19 patients if detailed information about the patient is available [187]. Industry 5.0-based UAV secure communication using AI was presented in [188]. The work suggests mass customization and inclusion of cyber-physical systems in this area. In view of the development, Industry 5.0 will open up ample opportunities for future research. * Explainable AI (XAI): One of the prominent future advancements is explainable AI that resolves the complexity issues of the models and enables users to understand how the models reach specific decisions and recommendations [189]. Also, users will know how the workflow of AI models leads to different conclusions for different cases and the strengths/weaknesses of the models. Black box models like ANN and RF are difficult to understand and implement due to their complexity. Therefore,an explanation interface such as data visualization and scenario analysis has been built which presents more explanation towards models and helps humans to easily understand the relationship between input and predictions. Companies providing XAI which presents different interfaces for the explanation of complicated AI models include Google Cloud Platform, Flowcast, and Fiddler Labs [190]. 5. Extended Reality and AI: One of the AI-enabled future technology is the extended reality (XR) combined with all forms of real and virtual environments including augmented reality (AR), virtual reality (VR), and mixed reality (MR). XR is an immersive technology that creates training data synthetically for DNN. Moreover, it creates virtual environments [191]. XR environments include cameras, virtual machinery, sensors, human avatars, and control software, and provide much richer contents compared to virtual reality. XR and AI unlock many opportunities in various domains [192], such as mobile XR, which uses a combination of smartphones, AR glasses, and mobile VR headsets. XR solutions are also used in industries and educational institutions to offer innovative and safe training to employees based on the data collected by tracking the movement of humans and machines [193]. The healthcare industry leverages XR in medical procedures to improve surgical imaging [194]. Areas where XR solutions can be applied still need to be explored. These include 5G communication networks, public services, real estate, defense, and military applications. 6. Convergence of AI and 6G: The future 6G with AI and ML methods will optimize network performance, support diverse services, and build seamless connectivity. Many researchers have started focusing on 6G with the vision of transmission over THz and mmWave and integrating communication, sensing, and control functionalities toward building a sustainable ecosystem. Studies have shown that 6G integrated with UAV-enabled networks leads to frequent handovers [38]. One of the powerful AI techniques named DRL, which is a combination of DL and RL, is capable of taking on the decision-making tasks [195] and can be adapted to provide efficient handovers, intelligent mobility, and reliable wireless connectivity. Moreover, in some complex networks, fuzzy Q-learning and LSTM-based AI techniques can be used to avoid connectivity or handover failures and enable mobility management [196]. 7. Channel Coding: Intelligent communication techniques extract the meaning of the information [197]. This can fulfill two purposes. One is to reduce the amount of data transmitted, and the other is to protect the information from channel distortion and noise using error control coding.