## 5 Related Work  
Techniques in computational intelligence (like heuristic methods) may be used to normalize the multifaceted, nonlinear dynamic behavior of a WSN. Such a heuristic method implements a \"rule-of-thumb\" function for a proposed scheme, and, consequently, it lacks structure and simplicity. Our proposed scheme is based on fuzzy set theory. Unlike most artificial intelligence techniques, fuzzy set theory calculates decisions in time commensurate with the needs of the WSN. This method is chiefly applied to control WSN behaviors that are difficult to model. Many of the queries regarding this method arise chiefly from its non-numerical nature, i.e., the stability of the whole scheme, completeness of the rules, etc. But, mathematical and analytical approaches have their own restrictions, whereas this study shows that heuristic programs may practically and effortlessly be used in WSNs to achieve desirable behavior in the WSN. Recently, computational intelligence techniques have been used to resolve issues relating to security, data aggregation and filtering, network deployment, location awareness optimization, and data routing in WSNs. Use of fuzzy logic is appropriate for achieving near optimization in data routing in cluster-based WSNs. Fuzzy logic is the ultimate choice for the optimization of filtering node and data-forwarding route selection due to the simplicity of the fuzzy membership functions [11; 48]. PVFS is a probabilistic data filtering scheme that has the objective of countering FRIA and FVIA attacks and achieving energy savings. PVFS lacks the ability to protect against compromised filtering nodes and does not utilize acknowledgements of the dropped reports [7]. In LEACH, cluster heads are chosen probabilistically; however, LEACH does not give importance to the distribution density of the sensor nodes and their energy status during their selection [58]. In the dynamic en-route filtering scheme (DEF) [12], filtering nodes are selected randomly within a predetermined hop count distance from the event detecting node before the routing paths are setup. Therefore, in DEF, the filtering power is mainly defined by the selection of the routing paths. It is very likely that some of the paths will not filter a single false report during data forwarding. Moreover, DEF does not counter the FVIA attack. FPMS in [31] was recommended to improve the detection of false data for the routing paths in the DEF based network. However, the performance is still determined by the number of filtering nodes on a path along with other elements, like the distance and energy of the nodes on the path. Since the number of filtering nodes on every path is not equal, it is very likely that favored paths may emerge and a path consisting of more filtering nodes is used excessively. Another anomaly is that a shorter path with a higher energy level may still not be able to filter out false reports since it has fewer filtering nodes. In SEF [5], verification nodes on the routing path authenticate the MACs of the report with a probability. However, SEF performs well when no more than a few nodes are compromised and the limited filtering capability of SEF is mainly decided by the detection probability of each node. The detection power of SEF is significantly influenced by the choice of the upstream routes. Several efforts have been made in an attempt to enhance the filtering and energy efficiency of SEF. [PERSON] et al. [59] suggested a path selection method that aims at refining the detection capability of SEF by considering the number of filtering keys on the upstream paths and their distances. However, in the presence of heavy traffic, favorite paths for data routing may emerge with heavy traffic on them, and the selection of paths leads to uneven energy consumption. The path renewal method in [60] proposes that an intermediate node on a routing path can renew its upstream path by considering its energy status and the degree of communication traffic it receives.