## 4 Taxonomy of Clustering Schemes
### Classification of Clustering Attributes in WSNs
#### 4.1.3 Clustering Process  
_Control Manners_: Based on control manners of clustering, clustering routing methods in WSNs can be grouped into centralized, distributed and hybrid ones. In centralized methods, a sink or CH requires global information of the network or the cluster to control the network or the cluster. In distributed approaches, a sensor node is able to become a CH or to join a formed cluster on its own initiative without global information of the network or the cluster. Hybrid schemes are composed of centralized and distributed approaches. In this environment, distributed approaches are used for coordination between CHs, and centralized manners are performed for CHs to build individual clusters.  
_Execution Nature_: Considering the execution nature of cluster formation, clustering modes in WSNs can be classified into two classes: probabilistic or iterative ones. In probabilistic clustering, a probability assigned to all sensor nodes is used to determine the roles of the sensor nodes. In other words, each sensor node can independently decide on its own roles. Nevertheless, every node must wait until a certain number of iterations is achieved or for certain nodes to decide their roles before making a decision in iterative clustering manner.  
_Convergence Time_: Considering the convergence time, clustering methods in WSNs can be grouped into variable and constant convergence time ones. The convergence time depends on the number of nodes in the network in variable convergence algorithms, which accommodate well to small-scale networks. After a fixed number of iterations, constant convergence time algorithms certainly converge regardless of the scale of the networks.  
_Parameters for CH Election_: Based on the parameters used for CH election, clustering approaches can be categorized as deterministic, adaptive, and random ones. In deterministic schemes, special inherent attributes of the sensor nodes are considered, such as the identifier (ID), number of neighbors they have. In adaptive manners, CHs are elected from the deployed sensor nodes with higher weights, which includes such as residual energy, communication cost, and _etc_. In random modes, mainly used in secure clustering algorithms, CHs are elected randomly without regard to any other metrics like residual energy, communication cost, _etc_.  
_Proactivity_: According to the proactivity of clustering routing, clustering routing methods can be grouped into proactive, reactive, and hybrid ones. In proactive networks, all routes between source and the BS are computed and maintained before they are really needed regardless of the data traffic. Once a message arrives, it travels through a predetermined route to the BS. In contrast, no predetermined routes exist in reactive networks, in which the routing is chosen when a message needs to be delivered from source to the BS. Hybrid approaches use a combination of the above two ideas. For this kind of clustering routing, sometimes proactive clustering mode is adopted, but at other times reactive mode is used. For instance, APTEEN [28] is a classical hybrid approach. According to the needs of users, this protocol adjusts some parameters and switches between proactive and reactive modes to transmit data.  
_Objectives_: As discussed in the previous section, a few objectives have been pursued for cluster construction, such as data aggregation/fusion, load balancing, fault-tolerance, guarantee of connectivity, lifetime extension, quality of service, _etc_. Accordingly, clustering methods in WSNs can be classified into the above categories based on different objectives. It is worth mentioning that a clustering algorithm, generally, has more than one objective.