## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
ERAPL can improve network lifetime while expending energy efficiently through the construction of DGS and selection of optimal OTPs (outgoing traffic proportion (OTP) from node i to node j) for all the nodes to distribute packets to their respective neighboring nodes.genetic algorithms. GAs with compressed chromosome coding scheme are used to find the optimal OTP matrix. An efficient distributed intelligent data-gathering algorithm called DIDGA is proposed in [87] for the mobile collector. A mobile collector is employed to gather the sensed data from nodes by dividing the whole network into certain minimum connected dominating set (MCDS) that could minimize the number of hops in the network. A path formation optimized algorithm (PFOA) is also proposed which combines ant colony algorithm and evolutionary algorithm to satisfy the time-limit constraints. In [88] a meta-heuristic optimization technique, Cuckoo Search (CS), is used to aggregate data in the WSN. In the proposed technique, the least energy nodes are formed as subordinate chains (or) clusters for sensing the data, and high energy nodes as Cluster Head for communicating to the Base Station (BS). The modified CS is proposed to get enhanced network performance incorporating balanced energy dissipation and this results in the formation of optimum number of clusters and minimal energy consumption. Bees algorithm is employed and modified by implementing custom crossover operation during the global search step in Intelligent DC Technique [89]. The main target of the modified Bee is to form disjoint dominating sets that work as collectors in each round. Intelligent Proficient DC Approach (IPDCA) [90] utilizes public vehicles as the mobile data collectors (D-collectors) that read (or collect) data from multiple Access Points (APs) and send them back to the central Base Station (BS). IPDCA adopts a modified Bat algorithm for path finding of D-collectors, where the Bat algorithm is extended to solve a discrete optimization problem. Fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous WSN is proposed in [91]. In FAJIT, fuzzy logic is first applied to WSN, and then min-max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. A Fuzzy-based data aggregation scheme called F-LEACH is proposed in [92] with the aim to maximize the network lifetime by optimizing the aggregating node selection. Besides, in F-LEACH, the FIS membership functions are optimized, and the average of several executions are selected as the optimal parameters by modifying the network scenario. Cluster-based data aggregation method using multi-objective male lion optimization algorithm (DA-MOMLOA) for evaluating the network based on energy, delay, density and distance is presented in [93]. In DA-MOMLOA, the data aggregation method is employed with the help of cluster head wherein the data aggregated from similar clusters are forwarded to the sink node. Query ordering with data aggregation is the process of scheduling the nodes to receive the useful data from sensors. Ref. [94] proposes a novel query-based data aggregation model with the aid of intelligent techniques. The framing of the query order takes place and the frames are ranked on the basis of a multi-objective function. The newly developed multi-objective function includes Latency, Throughput, and Data freshness. Initially, the solution corresponding to query order is trained in NN using the proposed Fitness-Mated Lion Algorithm (FM-LA). The optimally generated query order from Neural Network (NN) is further given for second-level solution generation, which is again applied to FM-LA for subsequent query order optimization.