# Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
## 1 Introduction  
A Wireless Sensor Network (WSN) consists of hundreds of independent, tiny, constrained energy-sensor nodes with limited sensing, data-processing, and communication abilities [1, 2, 3, 4, 5]. Each node typically consists of a low power unit, a radio-sensing unit, and a processing unit [6, 7, 8, 9, 10, 11]. These sensor nodes are randomly deployed in a certain geographic area to monitor various environmental and physical conditions, such as motion, temperature, pressure, vibration, sound, or pollutants. The authors of [4] mentioned the importance of a WSN's different applications in detail, clarifying that these sensor nodes can be deployed in complicated environments and dangerous locations [12, 13, 14, 15, 16, 17, 18]. Water quality monitoring is one of its most important applications, as it strongly affects environmental life. Water is the main feeding and life-maintaining source for living creatures on planet Earth. The imbalance in water purity levels leads to disastrous results for the plant and human lives. Different sources of pollutants, such as hydrogen peroxide and its corresponding radicals, are toxic and severely affect the quality of water for living organisms. An important part that must be considered in designing a WSN-based decision-making system for monitoring phenomena that might be detected in remote areas is the routing of a vast amount of data [9; 10], and the limited power resource of sensor nodes (a small, irreplaceable battery power source) [19; 20; 21; 22]. Therefore, different works have been presented to study the effect of efficient hierarchical data-clustering approaches on optimizing data routing, forwarding processes, and reducing energy consumption in static and dynamic contexts. Clustering reduces the amount of data transmitted by grouping similar nodes together and selecting one node as a Cluster Head (CH), where data are aggregated to avoid congestion and communication loads generated by multiple neighboring nodes, then sending aggregated data to the next CH or Base Station (BS), where they are processed, stored, and retrieved [1; 4; 6]. The CH performs a variety of tasks in addition to sensing the environment, including data collection from all cluster members and transmission to the base station, transmission of other CHs' data to subsequent hops, the creation of fusion cluster data, and occasionally cluster control via the clustering technique [13; 14; 15; 16]. These research works were the motivation to use K-means in conjunction with LEACH to face challenges in WSNs with sensor nodes with limited power resources and the routing of a vast amount of data. K-means, an unsupervised learning approach, is usually adopted to enable multi-feature-based Cluster Head (CH) election and hierarchical clustering formation in WSNs. The CH election takes into consideration the remaining energy level and position of the CH relative to the sensor node. In WSNs, low-energy adaptive clustering hierarchy (LEACH) is the most efficient well-known hierarchical clustering technique that is used considering the energy constraints of sensor nodes [1; 16]. The authors of [17] mentioned the advantages and disadvantages of LEACH in detail, which has led to making it the efficient clustering technique in WSNs [19; 22; 23; 24; 25; 26; 27]. In a specific group or cluster of sensors, the election of the CH is repeated through a set of rounds and using a stochastic approach [2; 12; 21]. In each round, the residual energy level of each member is examined and the sensor within the cluster will be selected with a certain probability [17]. This clustering algorithm helps in reducing energy consumption, as mentioned in different research works [1; 5]. The authors of [12] mentioned different cluster-based routing protocols, such as LEACH, and studied their effect on WSNs. This study is an extension of our previous work in [5], where we adopted the usage of a modified K-means clustering algorithm with LEACH to enhance the network efficiency and increase network lifetime as much as possible.