**3. Conclusion**

Due to the numerous ways that wireless sensor networks differ from regular networks, there is a need for protocols and tools that address particular problems and constraints. This chapter provided an empirical study of the wireless sensor network infrastructure, including its architecture, applications, and limitations. These networks consequently need novel approaches to routing, security, scheduling, localization, node clustering, data aggregation, fault detection, and data integrity that are both energy conscious and real-time. After that, the Chapter provided the taxonomy of ML algorithms that were applied to WSNs and performed a quantitative analysis of the algorithms that helped WSNs to overcome those limitations. Furthermore, varieties of methods are offered to improve a wireless sensor network's capacity to adjust to the changing behaviour of its environment. We also highlighted each ML algorithm's benefits and drawbacks.

However there is still a lot of work being done to address numerous outstanding issues because applications of machine learning methods to wireless sensor networks are still a relatively new area of study.
