## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
The suggested model decreases the fusion center's overhead while also reducing the quantity of data transfers. The provided RNN-LSTM model is put to the test using a variety of different hidden layer nodes and signaling intervals. Simultaneously, the amount of energy required to transfer data by the RNN-LSTM model is far less than that required to transmit actual data. Cluster-Tree based approach (CTEEDG) of [72] enhances the throughput and lifespan of WSN. FL is used for CH selection based on locally obtained information. The tree topology from the clusters towards the BS is developed in the inter-cluster communication process, ensuring the usability of the best congestion-free route to BS. Another DC approach in disjoint WSN using a rendezvous selection scheme with mobile edge nodes is introduced in [73]. This paper analyzed the disconnected WSNs and suggested a rendezvous selection technique, called Divide-and Rule ACO (DR-ACO), for maximum network connectivity and low propagation delay in delay-harsh applications. The route segmenting method and the candidate grouping process are introduced, which collectively enforce the principle of divide-and-rule and thus ease the ACO algorithm's mission. The powerful pheromone strength and heuristic factor are built during the transition of the ant, which collectively decrease the route length. The work presented by [74] combines rough set theory with an optimized convolutional neural network and suggests a WSN data aggregation scheme. First of all, in sink node, a feature extraction model is developed and then trained, where the rough set principle is implemented to utilize knowledge efficiently and reduce the tagged dimension.Once the cluster nodes have extracted these properties of the data from the granular deep network, CHs send them to the sink node to reduce data transfer and prolong the WSN's lifetime. To maximize WSN lifetime and to ensure data aggregation with less energy usage for distributed WSN, the authors of [75] suggested a method integrating grid clustering and fuzzy reinforcement-learning. Initially, for cluster forming and CH collection, grid clustering is employed. In addition, depending on the parameters, such as size, algebraic connectivity and neighborhood overlap, a fuzzy rule system-based reinforcement learning method is utilized to pick the data aggregator node. Finally, the MS's hierarchical relocation is done using a fruit fly optimization algorithm within a grid-based clustered network area. 'Monkey Tree Search-based Location-Aware Smart Collector (MTS-LASC)' that utilizes fauna inspired Monkey Tree Search (MTS) behavioral model is developed in [76]. MTS-LASC is a highly dynamic and interesting phenomenon that involves dispersed smart collectors and a centralized MTS meta-heuristic engine to solve complicated and hard problems. With a client MTS module, the distributed smart collector is embedded. It can evaluate, categorize and aggregate the gathered data and disseminate them to the sink using the fuzzy inference process, whereas the centralized MTS engine uses meta-heuristic search to promote detailed understanding of the situation across multiple paths for selecting an energy-efficient path in serving critical decision-making of IoT-applications. In [77], a periodic multi-node charging and data gathering method using Mobile Device (MD) is proposed to provide a perpetual network service where the network is split into several cells and the MD passes through each cell periodically to gather data and to charge the nodes with the goal of enhancing the volume of data generated per MD's unit energy. The anchor optimization technique for optimizing the anchor point location in the cell is presented in order to realize multi-node charging. A Discrete Fireworks Algorithm (DFWA) based on population entropy is then proposed to solve the issue of mobile device route planning.