# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Common WSN Routing Protocols for Data Gathering
#### 5.1.1 Cluster-Based Routing Protocols
This widely used swarm intelligence method mimics swarming behavior in bird flocking and fish schooling to guide its members to search for globally optimal solutions for cluster-head location selection. [PERSON] et al. [112] extend LEACH to a 3D setting by employing a method based on fuzzy clustering and particle swarm optimization (PSO). [PERSON] et al. [113] suggest a variant of the bat algorithm, which simulates bat prey echolocation behavior, to optimize the cluster-head selection for LEACH protocol. A routing path selection using an ant colony optimization algorithm is presented by [PERSON] and [PERSON] [114]. Clustering by mimicking groups of yellow goatfish is discussed by [PERSON] et al. [115]. The authors claim that the presented meta-heuristic is more efficient in avoiding local minima. An extension of LEACH for an IoT-designated industrial environment is presented by [PERSON] and [PERSON] [116]. This work provides uniform CH selection, uniform CH dispersion over the industrial grid of IoT-based sensors, and tree-based routing selection that promises to be more energy efficient than known counterparts. The energy exploitation is claimed to be equal among nodes. [PERSON] et al. [117] devise a dynamic-size cluster-based routing protocol for WSN comprising a large number of sensors that are spread over a large area (the paper suggests pollution monitoring as a candidate application). The primary objective of the presented scheme is to effectively select CHs to be responsible for the main communication with BS and additionally defined chief nodes (CNs). Specifically, the sensor topology is divided into groups, where CNs collect the updated energy indications of other sensors within a group. There are also border nodes responsible for communication between groups. If their energy value drops below a threshold, the CNs can be reelected. The candidates for CNs and CHs are provided by an artificial neural network (ANN), which takes as inputs remaining energy, neighboring node count, the amount of outstanding data, signal-to-noise ratio (SNR), distances between nodes, CHs, CNs and the BS, traffic load, and so on. The simulations show a better lifetime than other selected known protocols; hence, the scheme is a better fit for pollution monitoring. Clustering and routing for a wind turbine system monitored by WSN are introduced by [PERSON] and [PERSON] [118]. The discussed environment is unique because sensors placed at wind turbines can have their energy replenished by the turbine itself; hence, these sensors are always assumed to be charged. However, the distances between turbines and the BS are too large, and the grid is augmented by ground sensors that relay the measurements. In many cases, these ground sensors have to act as CHs. The authors propose system partitioning and clustering methods that may be hierarchical to specifically address this scenario. An interesting algorithm that also employs partitioning, by hierarchical grouping of sensors based on early knowledge of geographical transmission patterns in mobile WSN, is presented by [PERSON] and [PERSON] [119].