WSNs are of two types, namely, homogenous and heterogeneous [7]. In a homogeneous network, nodes are identical. Resource heterogeneity in WSN can be divided into three categories: Computational, Link, and Energy [8]. Computational heterogeneity describes those networks where nodes differ in microprocessor's power and storage capacity. Hence, the powerful nodes can perform complex data processing and long-term storage. When nodes differ with bandwidth, the networks come under the link heterogeneity category and are suitable for reliable data transmission. Both link and computational heterogeneity consume a considerable amount of energy, reducing the network lifetime. Hence, the type of WSN must be worked out, where nodes are differentiated in terms of battery power. The energy source consists of limited battery power, which is one of the major challenges in designing any sensor network [9]. The nodes may be deployed over a hostile location owing to the application that makes the battery recharging almost unmanageable [10]. Moreover, the nodes are expected to perform data acquisition for an indefinite time to achieve the application requirements. Hence, many researchers are currently engaged in exploring various techniques to extend the network lifetime to achieve high quality of service by balancing the energy consumption over the network [11]. When WSN is applied on larger platforms, topology control becomes an important parameter in balancing the network load to enhance the network lifetime and scalability. Clustering is an energy-efficient method for the hierarchical organization of sensor nodes in a network [12; 13]. However, each node in an ad hoc network communicating directly with the sink node leads to problems such as data collision, network congestion, and unnecessary drainage of power [14]. Low Energy Adaptive Clustering Hierarchy (LEACH) [15] is a classical cluster-based protocol proposed to minimize energy consumption by efficiently selecting cluster heads. Forming small clusters within the network helps overcome these crucial issues through efficient resource utilization. For each cluster, a cluster head (CH) is elected to act as a hop between the sensing nodes and the sink (as shown in Figure 1), thereby reducing the transmission distance. The CHs are elected dynamically after a certain interval to reduce the overhead. Once the CHs are elected, they broadcast an ADV message using the CSMA MAC protocol (see Figure 2a). Based on the RSSI of the ADV message, the nodes decide which CH wants to join for the current round and send a REQ message back to it using CSMA MAC protocol (see Figure 2b). Figure 1: Clustered-based WSN. The clustered network helps the system to maintain a longer life term by scheduling a duty cycle between nodes in a cluster without affecting the normal functionalities of the network [16]. The CH sets a time division multiple access (TDMA) schedule for data transmission to prevent any collision of messages. The non-CH node sends its data to the respective CHs with the DSSS (Direct Sequence Spread Spectrum) communication, in which each cluster has its unique propagation code to reduce interference. The CHs add received data and send it to BS through a fixed propagation code with CSMA. Energy-saving is the utmost priority of a sensor network [17]. Different methods have been adopted according to the requirement of the application and user. The characteristics of WSN-based IoT diverges from traditional network paradigms such as the Internet and even from ad hoc network [18]. Unlike ad hoc networks, WSNs designed for IoT applications confront a slew of additional hurdles, including node density, hardware, communication mode, battery capacity, and computational cost, to mention a few. In an IoT paradigm, nodes are given additional functions and must overcome new hurdles in terms of security, QoS (Quality of Service), and energy management [19]. Ring routing, as proposed by [PERSON] et al. [20], targets to reduce overhead by introducing mobile sink nodes.