## 1 Introduction  
The world is witnessing the increasing involvement of sensors in human life due to the technological advancements that have been achieved in recent decades. Various arrangements of sensors are serving humankind in the present scenario. Wireless sensor networks can be visualised as various sensing nodes sensing a specified domain, gathering information normally pertaining to environmental changes (movement of human beings, animals or vehicles; temperature changes, etc.) and forwarding it to a central control station, generally referred to as a sink or base station (BS), for decision-making purposes [1, 2, 3]. Figure 1 shows a typical hierarchical architecture, where sensing nodes collect the data from the environment and forward it to a leader node in the cluster, referred to as a cluster head (CH), which, after aggregation, further forwards it towards the base station. The analysis of data for decision-making purposes is generally performed at the BS level, but some basic computations can also be performed at the CH level or even at the normal sensing node level depending on the approach used [1, 4]. Inherent to the wireless networks are a few constraints such as limited battery life, bandwidth, memory, processing capabilities, abrupt sensor breakdown or abrupt behaviour, etc., which all need to be efficiently addressed. These networks may be deployed in friendly environments such as homes and offices or may be air-dropped into hostile environments where sometimes it is not possible to replace the batteries of the sensors or calibrate the sensors [1;3]. The sensing nodes of WSNs are generally fixed but they may have limited mobility as well, such as sensors floating on water bodies. Continuing advancements in micro-electro-mechanical systems (MEMS) technology have made it possible to fabricate smaller, inexpensive, energy-efficient sensors with increased memory and increased computational capability [1; 5; 6; 7] (e.g., Mica motes from Crossbow, Tmote Sky from Moteiv, the MKII nodes from UCLA and SunSpot from Sun), which have further boosted confidence in the use of WSNs. The integration of sensing capabilities, computation and communication into a single unit has also been much refined. This has tempted researchers to analyse the sensors' data for the purpose of efficient decision-making. Various methodologies have been adopted in relation to WSNs; of late, many researchers have been tempted to use machine learning (ML) in the context of WSNs [8; 9]. It provides generalised solutions through an architecture that can learn and improve its performance. The application of ML has led to great improvements in the performance of WSNs. Thus, advancements in machine learning have and are being applied to solve various WSN issues [10; 11]. Routing strategies also play a key role in the efficient working of WSNs. These strategies in WSNs, on the basis of the network structure, are broadly classified into flat network routing, location-based routing and hierarchical network routing. In flat network routing, all the sensors are uniformly deployed at the same level and each sensor serves as the other's peer. This can further be divided into proactive or reactive routing [12]. On the other hand, in the case of location-based routing, the sensors are clustered based on their location in the deployment, which is determined by the received signal strength [13; 14]. Amongst the three, hierarchical routing protocols have attracted the attention of many researchers as they ensure better energy efficiency [15; 16]. Here, the protocols arrange the deployed sensors into groups and each group has a designated cluster head (generally the sensor with the maximum energy). The cluster head coordinates with all the nodes inside the group, generally called a cluster, and with the activities outside the cluster, whether communicating with other CHs or the BS. One of the most prominent hierarchical routing protocols is Low-Energy Adaptive Clustering Hierarchy (LEACH).