## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
To set the parameters of the linear decision fusion model, the constrained PSO method is used, and the typical penalty function is used to tackle the constrained PSO issue. Sharing the WSN infrastructure to accomplish concurrent requests is becoming a trend today, where a comparatively complicated request can be met by aggregating complementary features provided by contiguous nodes in a certain area of the network. As a solution, a mechanism namely Multi-Requests Services Optimization (MRSO) is presented in [53]. MRSO is a combinational optimization approach for aggregating multi-requests. The PSO method is used in MRSO. MRSO discovers the best solutions by sharing global best solutions across several requests. This approach improves the shareability of WSN resources across concurrent requests while also lowering network energy consumption, especially when there is a lot of temporal, geographical, and functional overlap between concurrent requests. A cluster based data aggregation and routing technique is presented in [54] for improving lifetime and throughput of a network. A heuristic algorithm called Ant Lion Optimization (ALO) is introduced to deal with the clustering problem. Improper clustering often leads to individual nodes and when such an isolated individual node transfer information to BS, it requires high transmission power that will affect the network lifetime. ALO is applied over the selected CH to yield an optimal path. This reduces the overall traveling distance of MS and also extends the network lifetime. A Hybrid approach combining ACO and PSO for clustering and tree based data aggregation is introduced in [55] named as ACO and PSO based energy efficient clustering (ACOPSO). In this, the initial clusters are created based on residual energy, then ACOPSO is applied to enhance inter-cluster data aggregation. In ACOPSO, ACO based path selection is implemented as the primary step in which shortest path is decided with reference to the least cost based spanning tree that is created between CH and BS/ sink node. Then PSO is applied to reduce the path cost further. The performance is evaluated according to stability period, residual energy, network lifetime, and throughput. Throughput of this method is significantly better than others. WSN lifetime is improved and the energy dissipation is also consistent and balanced. The comparison is done with ABC and GA. Residual energy and throughput is considered for comparison and the results indicate a significant improvement in throughput and energy efficiency. A mobile data acquisition technique based on clustering which uses PSO and Space Division Multiple Access (SDMA) is proposed in [56]. The proposed approach is developed to cover energy hole and offer high efficiency, minimized latency, lower energy utilization and buffer overflow reduction. SDMA is utilized for data gathering. PSO algorithm is then used to select anchor points for scheduling visiting locations for Mobile data collector. Performance parameters that are used for evaluation include: packet delivery ratio, delay incurred, throughput, and average consumed energy. Mobile Data Transporter (MDT) is a special MS node that is introduced in [57]. MDT visits and gathers information from each WSN node and transfers the required data to BS. A discrete firefly algorithm based optimization is introduced to shorten the travel distance of MDT. The scheme is then analyzed and compared with other tree based schemes and it shows that this strategy is better than others in terms of minimizing tour lengths under different conditions. In [58], Two-Tier Distributed FL Based Protocol (TTDFP) is used to prolong multi-hop WSNs lifetime by making use of the efficiency of both clustering and routing stages together. TTDFP is an adaptive distributed protocol that operates efficiently for WSN applications and is scalable. Moreover, they utilized an optimization model to adjust the parameters of fuzzy clustering phase so as to optimize the WSN performance.