# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 1 Introduction  
WSNs have become popular as model-based ambient sensing solutions for various applications. Due to the advancements in microelectromechanical logic circuits, semiconductor logic circuits, growing computing power and communication capabilities, wireless sensor networks have recently become more popular. A WSN is mainly composed ofsensor devices from different geographical locations. The sensor node may perform some functionalities, including accumulating sensed information and communicating with other interconnected sensors [1]. Various WSN development efforts aim to address sensor nodes' design, implementation, and deployment issues depending on a specific need for monitoring and sensing in real-time applications. A WSN type casting depends on the type of environment in which it is applied, like environment monitoring [2], industrial applications [3], surveillance [4], military applications, automation in transportation, and healthcare systems [5]. It has been observed that a WSN comprises of many detecting nodes and a base station (BS). The detecting nodes must transmit data to the base station through diverse areas. Source and sink are other names for the BS and the sensing node, respectively. The sink must gather and analyze the data from all source nodes in the network. The WSN may be used to connect to the base station, which may not have any energy restrictions. On the other hand, the sensor nodes are completely dependent on their batteries, and they become inactive when they run out of power. Effective battery backup is critical for any WSN strategy based on the difference between WSN protocols and conventional wireless systems. By incorporating diverse clustering techniques, various techniques/methods have been proposed to effectively allocate nodes' energy in WSNs [6, 7]. Non-stationary sensors gradually replace individual smart sensors, which sense, process, and transmit significant information. The merger of several sensors, processors, and other communication devices into a single sensor node component has allowed this to happen. Sensor nodes in large numbers are distributed to form a sensor network. WSNs are extensively used in several sectors viz. green agriculture, healthcare monitoring systems, environmental surveillance [8, 9], smart homes [10], air purifiers [11], and disaster management systems [12, 13], due to their easy integration, self-organization, and real-time tracking characteristics. Sensor nodes are usually deployed in vulnerable conditions, making battery replacement and node restoration difficult. Moreover, enhancing the battery performance of nodes is very expensive. As a result, researchers have paid more attention to improving network lifetime and stability using existing network protocols [14]. In WSNs, one of the conventional relaying strategies is flat architecture or hierarchical architecture. Flat architectural techniques experience enormous amounts of information as a network's hubs grow, resulting in inefficient power generation and a lack of flexibility. Due to this, hierarchical routing algorithms have become more prevalent. Low-Energy Adaptive Clustering Hierarchy (LEACH) [15] is the hierarchical protocol for WSNs. LEACH tends to be the most prominent and commonly used protocol compared to the other protocols. Moreover, it is a challenging task to obtain information from the interconnected network environment. While transmission will occur, receiving this data at the BS simultaneously is impossible. Using techniques like time division multiple access, node information synchronizes in LEACH-Fuzzy Clustering (LEACH-FC) [16] to acquire the remaining energy information across networks. Along with making a range of information accessible, LEACH-FC also increases the BS's computing capability compared to the nodes. Consequently, such a centralized process can be used to enhance clustering. Energy consumption depends on their connection while data is transmitted from a node to a BS.