## 2 Related Work
The proposed hybrid algorithm considers multiple objective functions such as link lifetime, intra-cluster distance, mobility, delay, energy and inter-cluster distance. However, consideration of inter-cluster distance in the CH selection is inefficient since it can be computed only after all CHs are selected, and the formation of equal size clusters leads to a hotspot problem. In [21], the authors propose a duty cycling method for improving delay and energy efficiency in WSNs. To achieve this objective, this paper presents a dynamic duty cycle (DDC) scheme in the WSN. In this paper, the interrelation between duty cycling and delay is analyzed deeply to frame optimal adaptive duty cycling. Here, the large duty cycle, i.e., the awake period, is assigned to the nodes which are positioned far away from the sink node. These nodes are known as the nodes in non-hotspot areas. The sleep slot is assigned for the nodes in the hotspot area on the basis of residual energy level. However, the hotspot problem is not fully mitigated. In [22], the authors improve the energy efficiency through multiple procedures. Chiefly, a ring-partitioning-based sleep scheduling protocol is proposed to achieve better energy efficiency. Initially, the network is divided into multiple clusters by a weighted Voronoi diagram. Then, a two-fold data aggregation scheme is enabled to minimize the energy consumption further. Intra-cluster routing is enabled by hybrid chicken swarm optimization while an inter-cluster routing algorithm is presented with position-based routing tree construction. Nevertheless, the weighted Voronoi diagram-based clustering process increases complexity, and the RP-MAC protocol assigns sleep slots for the nodes in a random manner which degrades the performance of the network. Authors in [23] focus on QoSensing enhancement in a WSN for water monitoring applications. Here, the QoSensing is enhanced by improving the coverage ratio of the network. The aim of this work is to detect the water contamination using sensor nodes. For that, the budget constrained optimal position of the sensor nodes is determined. The budget is minimized by positioning the sensor nodes in an optimal position. To find the optimal position, this work uses a genetic algorithm (GA) which selects the optimal position for the nodes. At last, the nodes are deployed in the optimal positions in order to detect the water contamination. In general, coverage holes are formed with the increase in data transmission. This work only considers the coverage holes at the initial phase; therefore, QoSensing is better in the initial stage and degrades over the time period. Hence, this work is unable to maintain better QoSensing in the network. Although nodes are placed in an optimal position, when the energy level of a node decreases then the node must be replaced. This leads to coverage holes which degrade QoSensing. Authors in [24] insist that the importance of reliability in the WSN and the connectivity between the nodes in the network is addressed. This is achieved by implementing a network topology-based approach in which the nodes in the network are constructed as a spanning tree which greatly reduces the link failure problem in the network. The transmission of data is facilitated through the nodes which are arranged in a tree-shaped topology. However, the energy consumption in the network is not addressed which will lead to the problem of coverage holes in the network, thereby affecting the QoS of the network. According to the authors in [25], the placement of the nodes in the network is carried out in an optimal manner. The optimization of node placement is performed in a regional manner based on coverage and energy consumption. Hence, the necessary nodes to be moved are reduced which further reduces the overhead in the network. This approach leads to experimentation, and the results show that it has better performance since it considers both coverage and energy consumption in the network.