## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
In [78], a clustering based fuzzy C-Means algorithm is proposed for similarity-aware data aggregation in clustered WSNs by investigating the temporal and spatial correlation of data and the local detection of abnormal events. Then, for further outlier detection, a support degree function is defined. Finally, the aggregation is performed according to the statistical analysis of the outlier or non-outlier sensor data. Ref. [79] proposes an Optimal Partial Aggregation (OPA) approach for balancing energy and latency utilizing several optimization techniques. The Modified multi-fruit Fly Optimization Method (MFOA) optimizes the time-varying node lifespan, while the modified Time on Task optimization algorithm minimizes the aggregation delay. The Non-dominated Sorting Gravitational Searching Algorithm is then used to calculate the routing route to the destination (NSGSA). In [80], the nodes are arranged in a tree topology, with data aggregation taking place at intermediary nodes at tree branch junctions. To build an effective data aggregation tree, the River Formation Dynamics (RFD) method is used. The Improved RFD (IRFD) technique is then used to build a better aggregation tree. IRFD can generate more efficient aggregation trees with a reduced tree size. In the context of assuring network transmission reliability for WSN, the challenge of energy-efficient data collecting using in-network aggregation is studied in [81]. To increase energy efficiency while ensuring request transmission reliability, a ring and fuzzy rule-based data aggregation architecture is proposed. The count of unicast packet copies is adaptively changed using FL. Based on the request transmission reliability and node energy cost imbalance, the suggested approach adaptively unicasts a variable number of aggregated packet copies in a window. In [82] a Fuzzy-based Mobile Agent Migration (FuMAM) approach to find an appropriate mobile agent path is introduced. Three characteristics are taken into account by FuMAM: distance, remaining energy, and the count of neighbors. FuMAM improves the round-trip rate of successful mobile agents. FuMAM further extends the life of the network by choosing the node with the highest residual energy as the next hop for mobile agent migration. To address the problem of energy consumption and packet delivery ratio in WSNs, a fuzzy-based on-demand energy efficient clustering technique is developed in [83]. It applies ACO for mobile collector movement. PSO was utilized to optimize the membership functions of the FL controllers. In terms of network longevity and packet delivery, the results have improved. A approach to efficiently regulate the Mobile Base Station (MBS) mobility and locate the optimum destination in real time is presented in [84]. Type-2 Fuzzy Controllers control MBS movement (T2-FLCs). T2-FLCs use WSN local information to prioritize which virtual clusters should be accessed. As a result, the MBS dynamically determines which cluster is the most important to visit. In [85], an energy-efficient Voronoi Fuzzy multi hop Clustering (V-FCM) technique for WSN data aggregation is discussed. It combines the Voronoi diagram and a modified Fuzzy C-Means algorithm. To limit the quantity of data transfers, it uses data aggregation algorithms including MAX, MIN, and AVG, which are computed in each CH. By combining Voronoi diagrams into distributed clustering approaches, V-FCM achieves its purpose. The use of Voronoi diagrams is robust, and its major advantage is that it reduces the count of Euclidean distance computations in the fuzzy clustering method and identifies the best path for data transfer. In path identification for data transfer, the Euclidean least spanning tree along with Delaunay triangulation is applied. Energy-efficient routing algorithm to prolong lifetime (ERAPL) of WSN is proposed in [86]. In this work, a data gathering sequence (DGS) is constructed to eliminate mutual transmission and loop transmission among nodes, whereby each node proportionally forwards traffic to its neighboring node.