# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Clustering Techniques for WSNs  
WSNs consist of numerous small sensors deployed to gather data, and clustering is a technique used to group these sensors to conserve energy. By minimising communication and energy consumption, clustering can extend a network's lifetime; therefore, clustering also improves the manageability of a network and its scalability. Clustering in WSNs is crucial for energy conservation, minimising node communication, and extending a network's lifetime. Thus, clustering facilitates the scalability and manageability of large-scale sensor networks. Different clustering algorithms have been proposed for WSNs, and some algorithms aim to conserve energy while others aim to balance the workload among sensors. In contrast, others consider mobility or Quality of Service (QoS) because some advanced clustering algorithms utilise fuzzy logic or ML techniques for clustering decisions. The following points explain some clustering techniques for WSNs [6; 7; 8; 9]:  
* The load-controlled clustering method is an associate node primarily selected to assist a cluster head in performing its data collection and processing functions. This node is responsible for transferring data to the base station. The cluster head manages the collected data and forwards it to the associate node, which then relays this information to the base station. A multi-hop data transfer approach prevents the associated node from quickly depleting its energy. The multi-hop data transmission distributes the load, thus ensuring that all nodes share the load. It saves energy, so it increases the longevity of the network. * Low-Energy Adaptive Clustering is a technique that allows all nodes to become the cluster head. Since the cluster head consumes more energy, this method promotes balanced energy consumption among the nodes. This helps ensure that no single node exhausts its energy simply because it is repeatedly chosen as the cluster leader. The process consists of two operational phases: the setup and steady phases. * The Weight-Based Clustering approach is applied in Wireless Sensor Networks (WSNs) to conserve energy, and this approach increases the lifetime of Wireless Sensor Networks. It is a non-heuristic approach that applies to WSNs in various environments, so it can be used in different types of networks. In this approach, each sensor node is assigned to a \"weight\" value, which is used to select the optimal sensor node as a leader. Thus, the weight value plays a crucial role in leader selection. * The Fuzzy-Based Clustering algorithm is based on the Fuzzy Inference System (FIS). It uses the fuzzy inference system to select optimal nodes to be cluster heads based on various factors, including capacity, size, and node density. The fuzzy-based clustering algorithm is efficient and can balance the load, maintain stable connection quality, and improve the network lifetime. Therefore, it can effectively transmit key information to the sink. It can be used in heterogeneous WSNs because this algorithm is suitable for such networks, so it is a viable option for various applications. * The Hierarchical Clustering Energy-Efficient Algorithm is a modified version of LEACH for Wireless Sensor Networks that deploys a hierarchical multi-hop architecture to conserve energy. When a node gets selected as a CH, it notifies the neighbouring non-CH nodes to join it, so the non-CH nodes join the CHs based on the signal strength. After the formation of clusters, the non-CH nodes transmit the gathered data to their CHs, and the CHs also transmit data to the base station, either directly or through the other CHs. This reduces the energy consumption of all nodes. Therefore, this approach has some advantages, such as energy efficiency, longer network life, scalability, and better data transmission. There are some challenges in clustering in WSNs; sensors have limited energy, network topologies change, and networks can be extensive, so clustering also needs to deal with sensors with different capabilities and ensure each network is secure.