# A New Centralized Clustering Algorithm for Wireless Sensor Networks
## 2 Related Work  
Clustering in wireless sensors networks has recently been the objective of deep research [6] because of its suitability for this type of network as it was stated by [PERSON] in [2]. Moreover, clustering allows for many other important performance improvements in a WSN, such as scalability, reduction of the traffic load, decrement of the energy consumption, load balancing and fault tolerance. In [7] the taxonomy presented for cluster head election establishes three categories: preset, random and attribute-based. However, preset election relies on a predefined node deployment that usually uses a previous selection based on an attribute-based algorithm so this category can be included in the last one. Therefore, in general terms, we can state that the method to select the CHs can be classified in two main groups: stochastic and attribute-based. Moreover, taking into account recent publications, attribute-based methods are commonly based on computational intelligence. Specifically, they use a fuzzy logic system because of its simplicity. Alternatively, stochastic protocols are those in which nodes convert into CH basing on a certain random value or a determined mathematical function related with a value provided by the nodes (energy, distance to the base station, etc.). This value is then compared with another random value to finally decide about being CH or not. In Fuzzy Logic Based algorithms, the mathematical function is replaced by computational intelligence. This taxonomy is presented in Figure 1.  
Additionally, it is common that the methods to elect CHs are also classified into centralized and distributed ones [7]. In centralized methods, a central node, called base station or sink, which usually has improved computation capabilities, accomplishes all the process and calculation to select the CH in the network based on information gathered from all the nodes. In contrast, in distributed methods, each node decides whether it should be a CH or not. For this, the nodes relies on an internal process with only local information. One of the most important and referenced stochastic algorithm is Low-Energy Adaptive Clustering Hierarchy or LEACH [4]. LEACH is also a distributed method in which nodes convert themselves into CH if a probability value \((p)\), is lower than a random value generated by the nodes internally. That threshold value is computed in each round, based on a simple equation of \(p\). This probability increases with the number of rounds in which the nodes has not been selected as CH and that probability is reset only if the node is selected as CH. The probability reaches 1 after a constant number of rounds, which ensures that all the nodes become CHs within a given interval. This interval is based on the parameter \(k\), which defines the  
Figure 1: Simple taxonomy of clustering in wireless sensor networks. number of desired CHs in the network. The value of \(k\) is defined in [4] as \(n*p\) where \(n\) is the total number of nodes in the WSN and \(p\) is set to 0.05. This means that 5% of the nodes play the role of CH. There is also a centralized version by the same authors. This is called LEACH-C [8]. In LEACH-C, an artificial intelligence technique (simulating an annealing algorithm) is used to choose the optimal clusters from those with the highest energy. Another centralized technique is the one known as Base Station Controlled Dynamic Clustering Protocol (BCDCP) [9]. It is designed to have the same number of nodes attached to each CH. To do so, the CHs must communicate among them in some phases of the protocol. A different stochastic method is the one presented in [10], Multi-Threshold Long Lifetime Protocol (MTLLP), in which each node computes a probability threshold to be elected as a CH in a similar way that LEACH does. These thresholds are based in four variables related to the energy and distance. Nodes with a low energy level are not allowed to be CH. One of the first clustering works that proposed a Fuzzy-I logic-based and centralised algorithm is described in [11].