# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 3 Clustering Protocols in WSNs  
### Classification of Clustering Protocols in WSNs  
The clustering protocols in WSNs may be classified in multiple various ways and based on several parameters [13, 14]. Many researchers have contributed to the development of clustering algorithms in WSNs during the past years. In [13], the authors classified the clustering algorithms into two categories: probabilistic algorithms and non-probabilistic algorithms. The authors in [15] classify the cluster-based routing protocols in WSNs into two main classes based on the parameters used in the clustering process: macro parameters and micro parameters. The authors in [16] classify the clustering algorithms into three main categories: probabilistic, deterministic, and preset. In [17], the authors categorized the clustering algorithms in WSNs into seven categories: connected dominated set-based, mobility aware, energy-efficient, load balancing, dynamic, and homogenous/heterogeneous. In [18], the authors classify the clustering algorithms in WSNs into four classes: clustering algorithms for homogenous nodes, clustering algorithms based on fuzzy logic methods, and clustering algorithms based on heuristic methods. In this section, we propose a new comprehensive classification for the clustering algorithms in WSNs based on many criteria as follows:  
* Probabilistic/non-probabilistic clustering: each sensor node is assigned with a probability used as the main parameter to select the cluster head nodes [13, 19]. In non-probabilistic clustering, many of the basic and specific factors such as the location of the nodes, the number of neighbors, and security factors [20] are considered in the election process of CHs [13].
* Clustering in a homogeneous/heterogeneous network: In a homogeneous network, the nodes in the same level are fully equivalent to each other. The sensor nodes are equivalent in initial energy, sensing limits, and communication limits. Therefore, in the same conditions, the sensor nodes have a similar response and every node can be a CH. In homogeneous networks, clustering algorithms are divided into two types: energy-based and hybrid parameters-based [14]. In a heterogeneous network, the sensor nodes differ in their efficiency, resources, energy, and power. Hence, the sensor nodes are classified into two classes: super-node and normal node. A super-node is a sensor node that has advanced hardware and high processing capability. The normal node has a lower capability. The CHs are selected from super-nodes. Heterogeneous networks are also divided into two types based on the parameters of CHs selection: energy-based and hybrid parameters-based [14].
* Energy-based clustering: To select the optimal cluster head in a heterogeneous network, in this type of algorithm, the sensor node with the highest energy level has the priority to be CH [14].
* Hybrid parameters-based clustering: the clustering algorithms select CHs based on different parameters, such as the size of the cluster, the neighbors' information, the distance between the nodes and the base station, etc. [14].  
In the next paragraph, we will review some clustering algorithms and classify them according to the methodology and the parameters used in the clustering process.