## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
This approach is similar to traveling salesman problem (TSP) with neighborhoods. So to address this, they designed an ABC-based route planning technique. Proposed ABC algorithm involves four main phases: initialization, the employed Bee, the onlooker Bee, and the scout Bee phase. The performance evaluation is done by comparing average path length with other algorithms. ABC-based approach is highly stable than other greedy approaches and converges rapidly. In case of multiple mobile robots, this approach will not work well, so this must be considered in future by implementing new scheme with joint consideration of mobile robots and travel path determination. As the data collected by the WSN grew exponentially with increase in the number of sensors, the centralized data mining approach at the fusion center face difficulties in reducing the load and saving the WSN transmission power usage. To handle this challenge, the work in [48] proposed a distributed method of data mining for WSNs using Deep Neural Network (DNN). Unlabeled data collected from dispersed WSN nodes can be used to train the scheme's internal representations. DNN improves data mining efficiency owing to characteristics like self-learning, internal data representation and analysis, and multilayer perceptron creation with more than one hidden layer. The DNN is split into various layers distributed among the WSN nodes. This lowers the computation cost and energy consumption when compared to centralized processing. The results show an improved performance in data mining and is suitable for application in large scale WSNs. Another memetic meta-heuristic designed for finding solution to combinatorial optimization problems is the Shuffled Frog-Leaping Algorithm (SFLA). The work in [49] adopted SFLA for the selection of optimal clusters in WSN. Cluster Head (CH) is selected based on residual energy level of the WSN nodes. Shuffled frog algorithm shows good performance, high efficiency and stronger search capability. This technique uses the shuffling method of frogs for information exchange among local searches, to achieve global optimal output. Simulations are performed by changing the nodes count to measure the count of clusters formed, energy usage, average packet loss, end to end delay, and lifetime computation. Ref. [50] proposes a collaborative data aggregation technique that can address real-time needs in large-scale and complex networks. The PSO method is used to find the best transmission path that minimizes the maximum hop count while reducing the maximum next hop distance between two nodes. This contributes to the reduction of overall WSN energy usage. To calculate the best routing tree and meet the least energy consumption constraint, the developed PSO approach employs a multi-objective adaptive function. A method named Hybridized Pareto-Glowworm Swarm Optimization and Authenticated Data Communication (HPGSO-ADC) is presented in [51] for reliable and secure communication of data. The CH election is based on mobility and nodes' energy. After the clustering process, the data is aggregated from various nodes to minimize the bandwidth usage and forwarded to BS using ABC approach. Authentication is done between CH and BS to guarantee the security. Various performance parameters are examined. It includes packet delivery ratio, bandwidth, energy consumption, end to end delay, throughput, WSN lifetime, availability, reliability, loss probability, and serviceability. Every node in the WSN is expected to gather local measurements that are likely to be influenced by noise, formulate a local decision regarding the presence or absence of an event, and then send that decision to the fusion center. The fusion center then makes an ultimate decision based on specific local decisions as well as the fusion rule, therefore a successful fusion rule decision is critical. A linear decision fusion model for WSNs is suggested in [52].