## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### Open Research Issues and Challenges
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**AI Paradigms** & **Algorithm** & **Ref.** & **Optimization Criteria** & **Simulation/ Real-Deployment** & **Centralized/ Distributed** & **Mobility** & **Performance Parameters** \\ \hline \multirow{3}{*}{Q-learning} & \multirow{3}{*}{Q-DAEER} & Maximize the lifetime and & & & & Network-level energy consumption, number of dead nodes, network lifetime, average hop count and decrease in data size. & & Network-level energy consumption, number of dead nodes, network lifetime, average hop count and decrease in data size. & & Network-level energy consumption, number of dead nodes, network lifetime, average hop count and decrease in data size. & \\ \hline \multirow{3}{*}{FL} & \multirow{3}{*}{FAJIT} & \multirow{3}{*}{[91]} & \multirow{3}{*}{[92]} & parent node selection and improving energy efficiency in WSN. & & & & Schedule length, Average aggregation factor,Control overhead, Average energy consumption,,and Number of transmission slots. & & \\ \hline \multirow{3}{*}{FL} & \multirow{3}{*}{F-LEACH} & \multirow{3}{*}{[93]} & maximize the network lifetime. & & & & Average residual energy and percentage of dead nodes. & \\ \hline \multirow{3}{*}{Nature inspired} & \multirow{3}{*}{DA-MOMOA} & \multirow{3}{*}{[93]} & reducing the cost of transmission & & & & Network efficiency, Scalability,Throughput,Data delivery, Packet drop. \\ \hline \multirow{3}{*}{A Hybrid of natural artificial neural network and Evolution Computation Computation} & \multirow{3}{*}{FM-LA with NN} & \multirow{3}{*}{[94]} & Optimal data aggregation & MATLAB simulation & Centralized & Static & latency, throughput, and data freshness. \\ \hline \multirow{3}{*}{Q-learning} & \multirow{3}{*}{MWCSGA} & \multirow{3}{*}{[95]} & improve the life span of the network, energy efficiency enhancement & & & & energy efficiency, energy consumption, end to delay, packet drop, and throughput and packet delivery ratio. \\ \hline \multirow{3}{*}{Q-learning} & \multirow{3}{*}{RNA} & \multirow{3}{*}{[97]} & Increase aggregation ratio & OMNet++ simulation & & & Network scalability, Events, Communication range, Communication failure, Aggregation ratio, Network load. \\ \hline \multirow{3}{*}{Q-learning} & \multirow{3}{*}{STAC} & \multirow{3}{*}{[98]} & Reduce the data redundancy & MATLAB simulation & & & & Network lifetime and error of predicted data. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)to evaluate the performance results. We hope this can help in realizing the best possible solution to this challenge. Aside from ensuring secure and reliable information exchange between the CH and BS, researchers can also experiment with improving WSN QoS parameters (such as availability, reliability, and serviceability) by combining various optimization algorithms to achieve improved data dissemination and gathering efficiency. The researchers can investigate about the integration of different powerful data fusion algorithms into other system research areas, such as big data systems, key-value stores, data compression, and blockchains. The optimization of different algorithm dependent parameters can be studied further to explore the behavior and to test the performance under WSNs coupled with different node mobility approaches. The use of hybrid compressive sensing can be encouraged with different optimization strategies. Performing real-time implementation for related applications are of great significance as efficient implementation of general real-time applications remains by far an open challenge. Additional research is necessary to enhance sample data noise filtering and data mining using deeper DNN layers. Moreover, improved and flexible protocols should be developed to deeply evaluate the traffic on different layers using complex functions. Further studies can be performed to optimize the algorithm using different parameter choices to adapt them for use in large scale heterogeneous networks, and simplifying the data fusion task.