## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs  
Several AI techniques are applied to handle the DC, aggregation and dissemination challenges in WSN. We discuss them as follows:  
A family of ACO based optimization algorithms introduced in [44] for data aggregation is called Data Aggregation ACO Algorithm (DAACA). It consists of three phases. First is initialization, followed by packet transmission and then, the operations on pheromones are done. They also designed evaporation and deposition of pheromones which take the advantage of both local and global merits. Four AC heuristic algorithms are implemented in this work, Basic-DAACA, Maximum and Minimum based DAACA (MM-DAACA), Elitist Strategy based DAACA (ES-DAACA), Ant Colony System based DAACA (ACS-DAACA). Basic-DAACA is the elementary algorithm on the basis of which other three algorithms are designed. The other three algorithms are designed for improving the Basic-DAACA. ES-DAACA attaches extra global pheromone concentration into Basic-DAACA. MM-DAACA restricts the pheromone range in ES-DAACA. ACS-DAACA includes both local as well as global pheromone modifications to Basic-DAACA. Basic-DAACA eliminates the repetition of creating and preserving the WSN topology which results in preventing computational overhead and prolongs network lifetime by saving energy. DAACA family has no additional energy expenditure for creating and maintaining of the topology of the network. The pheromone adjustment will not cost much energy. The average energy cost of these four methods follow the order: ACS \(<\) MM \(<\) ES \(<\) Basic. The enhanced versions of DAACA improve the quality of output of the Basic DAACA and produce data aggregation routes which are more energy conservative. The success ratio of one hop communication for DAACA is higher when compared to other algorithms. Time complexity of DAACA is lower than others for maintaining the network topology. DAACA is scalable, robust and fault tolerant. Time complexity for constructing the topology of DAACA is equivalent to ACA, and the complexity is \(O(n^{2})\). The researchers have applied different swarm intelligence algorithms for solving optimization problems. One such designed technique that imitates the dynamics of river systems is the intelligent water drops algorithm. Each drop symbolizes a solution in this population-based method, and the sharing of drops during the search results in better drops (or solutions). The Intelligent Water Drops (IWDs) technique is used in [45] to generate the best data aggregation trees for WSNs. They improved the fundamental IWD method by altering the selection probability for ideal aggregation nodes, which improved the tree creation. An ant inspired solution is proposed in [46] for Minimum-power Multiresolution Data Dissemination problem. They proposed a meta-heuristic solution known as ACO based Minimum Incremental Dissemination Tree (AMIDT), with an aim to optimize the energy consumption of WSN. This algorithm includes: (1) an online tree formation algorithm and (2) Two lightweight tree adaption heuristics named path-quality and reference-rate based heuristic. Depending on the local information of the path, path-quality based heuristic was estimated, while reference based heuristic is based on aggregation rates and decreases the required count of tree reconfigurations. AMIDT exhibits improved performance in terms of energy and cost for searching a path when compared to other approaches. ABC algorithm is another class of algorithms that showed superior performance on many kinds of optimization problems. In [47], the concept of Sparse WSN is introduced, which a special type of network characterized by the node's geographical sparsity in which the nodes cannot directly interact with each other or with other nodes, i.e., sink. One of the important concern in this paper is to reduce energy utilization on mobile robot data gathering. Therefore, the travel path length of mobile robot has to be determined to ensure high energy efficiency.