# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 1 Introduction  
### Background  
Smart technologies are widely used in fields like building, health, ecological monitoring, security, home, vehicles, planes, or shipboard. However, smart environments rely first on sensory data from the real world like it is done by sentienti organs. Smart environments are possible due to the recent evolution of wireless communication technologies, digital electronics, and MEMS technology which have seen the apparition of sensors. They are small in size and are able to collect information on its environment like temperature, pressure, humidity, water content, gas presence, or luminosity. In spite of the large amount of applications offered by WSN, sensor nodes are designed with resources constraints such as a restricted computing capacity, reduced memory size and storage, weak range of communication, low bandwidth, and a limited amount of energy. To efficiently cover areas, a singlesensor is not sufficient due to its limited communication range. In order to cover a more consequent space, several sensors are deployed and connected to each other, thereby forming a Wireless Sensor Network (WSN) [1].  
By designing a WSN, the most important parameter to be taken into account is the energy consumption because it defines the lifetime of a sensor node and thereafter the lifetime of the whole network [2]. A trade-off between the energy restriction and the resources constraints of sensors is imperative for the performance of the network. Nevertheless, when the amount of nodes within the network is extensive, the usual direct routing consumes more energy and may reduce the network lifetime considerably [3]. From the original wireline networks, hierarchical or cluster-based routing are widely used for large WSNs because they are techniques with advantages related to scalability, efficient communication, and fault tolerance [4]. In hierarchical architectures, the whole network is divided into sub-networks called clusters. Each cluster is led by a special node named Cluster Head (CH) which is responsible for gathering or fusion data from nodes that belong to the same cluster [5]. In this kind of routing technique, the inter-cluster and intra-cluster communications may act in a multihop manner. Thus, a sensor node communicates only with its nearest neighbor in order to preserve its remaining energy and not to waste its energy by trying to communicate with a neighbor which is far away [4]. Other clustering approaches aim at finding a trade-off between the reliability of sensing and communication overhead based on unsupervised learning process [6].  
Meanwhile, traditional hierarchical based algorithms are mostly probabilistic clustering approaches [7]. However, like the authors of [8], for example, the clustering on WSN remains a Non-deterministic Polynomial (NP) hard optimization problem which cannot be solved efficiently by traditional approaches. Thus, a more accurate resolution of the NP optimization of the clustering is made possible by using approaches based on recent research on Computational Intelligence (CI) and Machine Learning (ML). These recent clustering paradigms are known as optimized clustering algorithms.  
The clustering solutions based on CI or ML consider environmental and biological behaviors and outperform most of the traditional clustering solutions in terms of scalability, reliability, fault tolerance, amount of data delivered, energy consumption, better coverage of the experimental field, and the increase of the network lifetime [7; 8; 9; 10; 11]. Meanwhile, according to the type of application, the choice of the appropriate approach is very important both on the cost of the network deployment and its lifetime.