## 5 Conclusions and Future Directions
Most problems stem from incompatibility between layers and high human interaction. Self-Adaptivity is required for setting and adjustment of solutions. Hybrid approaches that optimize the resource utilization in WSN need to be developed [27]. Rather than specialized solutions, learning platforms and prototypes are required. In future, further research to devise efficient distributed data mining solution for WSN with improvements in noisy data filtering process have to be encouraged. The layered structure and other typical features of DNN makes it a favorable option for application in such scenarios. The main challenges include training of distributed multi layered DNN and better tradeoff between power consumption during processing and transmission. A  
Figure 5: AI methods with respect to various challenges in WSN. further interesting area of study for AI methods in WSNs is known to be parameter learning and optimization. Many efforts have been made to fix the interference classification challenge in WSNs. The steadily increasing utilization of license-free frequency bands make proper wireless interference identification and reliable management a crucial challenge. Interference mitigation techniques, deployment planning and other potential applications opens up numerous possibilities for future research. DL classification model is a choice. Traffic flow control in WSN is a potential future direction. Mobility is another important open research topic that can be supported by head nodes, normal nodes, and BS. The most significant issues to be addressed are changes in WSN architecture and control packet overhead caused by node mobility. Only a few researches have been conducted to improve the intelligence of mobile agent for better route planning and data gathering. To accommodate the node changes in a dynamic WSN, an integration of DL and RL using multiple mobile agents is a desirable solution. The use of RL can make mobile agents more intelligent in making decisions to perform best action according to the present situation. Moreover, determining optimal number of mobile agents and their route by evaluating multiple parameters that could provide efficient results require further investigation in future. Moreover, most of the current algorithms didn't properly consider the security and privacy of the data in multiple mobile agent scenarios. The utilization of compression techniques to compress the data collected by each mobile agent also require more research attention. It can be expected that the future researches will give better consideration to these issues. Future development of more robust and efficient mobile agent learning methods to solve the real-world challenges are necessary. The application of AI optimization methods to overcome the challenges of MWSNs is a potential future direction. Some research challenges still remain relatively unresolved which include transmission delay, balancing the power consumption, reliability and safety of MWSNs. A combination of SI with other optimization techniques have to be encouraged in future. The cross-layer optimization model challenges must to be treated in a better way during the optimization of MWSNs. The results from the study of human-related biological features can be incorporated in future for further improvement of solutions to such problems. Distributed and real time application of algorithms in light weight form could be a question for further studies in order to solve the challenges of dynamic MWSNs in future. As the study shows, most of the existing solutions based on AI are only simulation based. The AI techniques should be implemented and analyzed in a real-time environment [26; 28]. This should be encouraged in future. More investigations are required to show how AI methods can be adapted. Cross-layer approaches using AI methods are rarely applied to challenges and are still a vital open research area. Moreover, hybrid AI methods are less applied and need to be discussed deeply.