## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
The MDF-FBCH technique of [59] performs data fusion based on the network path parameters instead of performing fusion on the data gathered by the nodes. It includes a Multisensor Data Fusion (MDF) scheme which executes fusion on the gathered parameters of the network in collaboration with Fuzzy based model for CH Selection (FBCHS). The MDF method utilized ANFIS fuzzy model to decide about the selection of network path. Instantaneous Channel State Information (I-CSI), bandwidth, Packet Loss Ratio (PLR), latency, centrality and Signal to Noise Ratio (SNR) are the crucial parameters applied to the ANFIS-based MDF model. Each parameter has its own significance in selecting the route from CH or nodes to the BS. The used parameters are then trained using multi-objective PSO algorithm. The fusion on network parameters is performed by MDF-FBCHs in mainly four steps using: a normalizer, fuzzifier, MDF engine and a defuzzifier as shown in Figure 4. The authors highlight that network factor fusion for route selection also plays a significant role in selecting an acceptable data forwarding path. Chicken Swarm Optimization (CSO) Algorithm is applied to optimize the Compressive Sensing matrix in [60] that is to enhance data aggregation in cluster-based WSNs. In [61], a Simulated Annealing (SA) based algorithm for tree construction and scheduling (SATC) is proposed which aggregates data from nodes with a collision-free schedule. SATC considered average time delay in delivering the aggregated results to sink as SA's fitness function and hence provided enhanced efficiency in minimizing average latency in data aggregation than the existing scheduling techniques. DC without delay is a significant process in WSN and is important for a variety of applications where precise actions depend on the appropriate timeline, such as event-based mission-critical applications. The authors of [62] proposed a GA based algorithm (ETDMA-GA) for efficient scheduling to minimize latency in collecting data. ETDMA-GA minimizes the communication latency by using a 2D encoding scheme for slot allocation and a fitness function to minimize the overall network latency. A hesitant fuzzy entropy based technique for data fusion in WSN proposed in [63]. The work aims to minimize the gathering of redundant data from the WSN nodes, and exploits the information obtained from redundant data to enhance data reliability. Hesitant  
Figure 4: FBCHs fuzzy model for CH selection [59]. fuzzy entropy method is utilized to combine the data collected from cluster nodes at the sink for gaining better data quality. In [64], a cross layer mechanism is presented for congestion management which is enabled by FL and Oppositional ABC Optimization scheme (FCOABC). The proposed cross layer technique adopted FL with fuzzy descriptors in terms of neighboring nodes count, communication link reliability, and nodes residual energy, for selecting the CH. The WSN is arranged to form dissimilar sized clusters to address hot spots issue. In this method, smaller clusters are taken into consideration, as CHs of such clusters are at proximate locations from master station. The advantage is that they experience only reduced intra-cluster congestion and hence the relay traffic can be efficiently transferred using their preserved amount of energy. The benefit of using Oppositional ABC is that it can perform inter-cluster routing efficiently in multi-hop manner from CH to BS. Therefore, this method helps to achieve energy aware and reliable data delivery. The performance is analyzed on the basis of energy consumption by CHs, amount of data transferred to master station and total consumed energy. The work in [65] presented a technique called GTAC-DG for DC using MS in WSN. To achieve a better trade-off between power consumption and transmission delay in the WSN, they proposed an effective rendezvous point selection process using the distributed game theoretical approach (GTBRP) and specify the suitable efficient route for sink node using the modified ACO based algorithm.