## 3 Related Works
[30] presented a survey related to the use of FL in WSN. FL is considered as a promising approach to evaluate diverse parameters in an efficient manner. It improves decision making, reduces resource consumption and is suitable for resolving WSN issues such as routing, data aggregation, security, localization as well as deployment. FL can tolerate unreliable or imprecise readings easily. They presented it to be an easier and efficient technique. The disadvantage of using this technique is that the rules count grows exponentially with the variables count and storage requires much extra memory. They showed that the FL approach can solve the shortcomings of most of the algorithms. As this is a rule based approach and due to constant traversal of rules, it may slow down the event detection and decision making process. To solve this problem they also presented rule based reduction techniques which are efficient, but none of them can be considered as a general solution. A key property that must be kept in mind is that the reduction techniques employed should not affect the application accuracy. So, selection of the best reduction technique is very challenging. Future work that needs to be done is to implement a software based FL to enhance the speed of the system. The work in [31] focuses on the most recent advancements in data aggregation techniques for WSN concerning terrestrial, underground, underwater areas by providing a review of different data aggregation strategies and protocols. However, the role of AI approaches in resolving data aggregation challenges in WSNs has been described in a very limited way, without deep discussion. Ref. [12] gives a methodical literature review of data aggregation in the field of WSNs. The study covers data aggregation strategies, tools, methodology, and challenges. However, the focus of this study was not on the role of AI methods in data aggregation, but rather on data aggregation methodologies using different criteria. Ref. [32] focuses on data aggregation approaches based on routing protocols.The work in [33] reviews the important contributions to data aggregation security solutions that primarily employ soft computing methodologies. Protocols are categorized according to soft computing approaches such as FL, SI, GA, and ANNs. Discussions on metaheuristics and how to employ them to solve the deployment problem are addressed in [34], and how to use them to solve lifetime problems of WSN are covered in [35]. Multi-Objective Optimization techniques in the context of WSNs are presented in [36] to introduce the development efforts for surveillance and monitoring. Other sophisticated optimization strategies were explored together with the needs of different optimization approaches such as mathematical programming and heuristics/meta-heuristics based optimization. A survey of ACO techniques for static and mobile WSNs is proposed in [37]. An overview of a set of aggregation techniques ranging from Space Filling Curves, to Q-digest, Wavelets, Gossip aggregation, and Compressive Sensing presented in [38]. In [39] a study reviews various data aggregation techniques such as clustered aggregation, tree-based aggregation, in-network aggregation, and centralized data aggregation with focus on energy consumption of sensor nodes. In [40] data aggregation approaches systematically reviewed, examined and categorized into Flat Network and Hierarchical Networkbased on different factors (e.g., types of networks and algorithms, node heterogeneity, and the aggregator mobility). In-network data aggregation algorithms are surveyed and analyzed in [41] based on various parameters like energy efficiency, aggregation rate, network lifetime, number of alive nodes, throughput, energy-saving techniques, scalability, network security, route overlap, and route repair mechanisms.