## 1 Introduction  
WSN is a network of spatially dispersed tiny sensor nodes responsible for the collection of data from the physical environment. These sensor nodes are used for observing environmental parameters like pressure, moisture, temperature, wind speed, vibrations, heat, noise, etc., to transmit this acquired data to the sink or target node in the network. The recent improvements in the WSN technology have enabled the sensor nodes in the network, not only to capture real-time data but also to capture data in complicated real-life applications like a medical diagnosis of a heart patient, by implanting sensor nodes to record real-time data about the heart rate. These real-time applications demand real-time communication from the source node to the destination node. The WSN is composed of tiny nodes called sensors, shown in Figure 1[1]. These sensors form the basis of WSN because of their abilities to sense the environment, where they are deployed and also because of their ability to collaborate to form networks [2]. These sensor nodes are not of much use due to their limited possession of resources but once they create a network, they become very useful in the acquisition of environmental parameters, processing the acquired data and transmitting the processed data to a sensor node known as a sink in the WSN. A sensor node in WSN is capable of performing operations like sensing or acquisition, storage of acquired data, and transmission of data to different nodes in the network, so that the data reaches from the source node to the terminus node. It is important to understand that WSNs are subject to its own limitations and its implementation is a tough task, mainly due to their limited energy, poor communication range, very low storage capacity, and weak processing power [3]. Once the sensor nodes are deployed in the real environment, it is expected that they will remain operational for a stretched duration in unmanned sites. The whole scenarios demand efficient management of resources like processing, energy, storage, etc. The lifetime of the network is directly reliant on the amount of residual energy in the sensor motes, as it is nearly impossible to replace batteries in the sensor nodes. As sensor nodes are the nodes with a very limited amount of energy, it is very important to make optimum use of energy that is available with the sensor nodes. The maximum amount of energy is consumed during the communication operation. It is during communication when each node in the network behaves as a router and transmits the data packets from the source node to the next node, and then follows a route according to the protocol in use, so that the data packet reaches the destination. This task in the WSN demands maximum consumption of resources. With the ever-increasing use of term green computing, the energy efficiency of WSN has seen a considerable rise. Recently, an approach for green computing towards IoT for energy efficiency has been proposed, which enhances the energy efficiency of WSN [4]. Different types of methods and techniques were proposed and developed in the past to address the issue of energy optimization in WSN. Another approach that regulates the challenge of energy optimization in sensor-enabled IoT with the use of quantum-based green computing, makes routing efficient and reliable [5]. The problem of energy efficiency during the routing of data packets from source to target in case of IoT-oriented WSN is significantly addressed by another network-based routing protocol known as GreeDi [6]. It is imperative to mention here that IoT is composed of energy-hungry sensor devices. The constraint of energy in sensor nodes has affected the transmission of data from one node to another and therefore, requires boundless methods, policies, and strategies to overcome this challenge [7].