## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
The method favors nodes with high residual energy and load, which will help to efficiently balance the load and conserve energy. To avoid node buffer overflow difficulties, they included traffic load as an extra parameter to ACO and devised an efficient MS trajectory. This enables DC without traveling great distances, resulting in a better balance between DC delay and energy consumption, extending the WSN lifetime. To create a two-hop load-balanced data aggregation routing tree in the network, Ref. [66] proposed an effective method called Cuckoo-search Clustering with Two-hop Routing Tree (CC-TRT). CC-TRT employs an improved energy-aware cuckoo-search algorithm to identify the best CH for each cluster. The cuckoo-search algorithm allows for round-by-round rotation of the CH job across various sensors. The CC-TRT model is then modified to provide two new techniques: Cuckoo-search Clustering with Multi-Hop Routing (CC-MRT) and Cuckoo-search Clustering with Weighted Multi-hop Routing Tree (CC-WMRT). As an advantage, the suggested schemes offer better energy balancing among nodes in WSN. The problem of optimizing data dissemination and routing is examined in [67]. In the first instance, the suggested scheme provides a clustering mechanism focused on the functional characteristics of the nodes based on their past behavior. Watchful Node takes the responsibility of coordinating the nodes and monitors the activities. This strategy organizes nodes into different clusters and chooses CH based on a fuzzy and location-based approach. Moreover, for each cluster, the collective behavior of the wise and sharp-minded optimizer inspired by [PERSON] hawks pick at least one watchful Node. Then, data dissemination and routing mechanism work according to a query/event-based hybrid scheme. This maintains the stability and reliability of the WSN. The lightweight diffusion mechanism is assured by the iterative and repetitive event-based strategy, which isolates the event zone to regulate the amount of copies of data packets. Ref. [68] suggested Group Search Optimization technique with ANN for the development of a new query-based framework for data aggregation. The scheme includes a Querying Order model that uses the Query Order ranked in terms of throughput and latency. The objective of the paper is to develop a Querying Order model to increase throughput and reduce delay, thereby improving existing schemes for data aggregation. It will allow the administrator of the network to acquire information about the suitable queries to increase the sink performance. A data fusion scheme for Heterogeneous WSNs based on the PSO and Extreme Learning Machine (PSO-ELM) has been suggested in [69]. It optimizes the input weight matrix and hidden layer bias of the extreme learning machine using PSO. The performance weight matrix is intended to minimize the number of hidden layer nodes and improve the generalization capabilities of the model. The sensor nodes' original data is merged via the data fusion model. The extreme learning machine was used to analyze the data obtained by the nodes in the hierarchical routing using the spatial temporal similarity between the data acquired by the heterogeneous WSN nodes. A fuzzy-oriented geographic routing protocol called FGAF-CDG, based on the hexagonal virtual grid architecture of the GAF protocol, is proposed in [70]. FGAF-CDG divides the area into virtual hexagonal grid cells first, then places the cells according to their geographic location. A FL algorithm is used to choose the CH sensor in each grid cell during each sample round. In the compressive DC phase, CH readings would be delivered to the sink through a multi-hop path formed using a fuzzy-based routing mechanism. At the WSN fusion center, a Deep Learning Based Data Mining (DDM) model is employed to accomplish energy saving and effective load balancing, according to [71]. The RNN-LSTM recurrent neural network (RNN) based Long-Short-Term Memory (LSTM) in the provided DMM model splits the network into layers and places them in the nodes.