## 2 Literature Review  
In recent years, IoT has been used for a wide range of applications, including smart healthcare, smart homes, and many others. Due to the limited capabilities of sensor nodes, the utilization of available resources efficiently is the main challenge for IoT-based systems. Therefore, several researchers are motivated to design energy-efficient approaches for IoT-based WSN. In [14], a joint reliable and energy-efficient technique is presented, where a game-theoretical approach is used to provide secure communication in a wireless sensor network. The overhead associated with the trust-based technique is mitigated by employing a game-theoretical approach. The results show that the presented technique is suitable for IoT-based applications in terms of security and energy efficiency. In [15], an under-water channel model is presented. The proposed method does not consider channel-aware energy saving (i.e., duty cycle) for IoT-based smart healthcare. In [16], a joint product life-cycle and IoT management algorithms are presented to save power and extend battery life. The proposed approach is based on duty-cycle management and power transmission control to optimize battery charge consumption. The researchers did not consider energy parameters, wireless channels, and clustering techniques at physical and network layers. In [17], a delay balancing approach is presented for transmission of data and energy utilization for wireless sensor networks. Besides, a work-load management policy is also considered for the network. In [18], a sensing layer-based technique is presented to analyze the power dissipation of numerous nodes in the network. Additionally, a novel architecture is presented based on base-station, sensing, and control layers. The study did not entertain duty-cycle and channel-aware energy saving. In [19], a novel framework is presented for IoT-based applications. The study focuses mainly on three layers, e.g., sensing, information processing, control, and presentation. However, they did not consider energy-saving strategies based on clustering and the use of numerous energy-optimizing parameters across the network. In [20], a Low Energy Adaptive Clustering Hierarchy (LEACH) is proposed, known as a node selection and data transmission protocol of wireless sensor networks. In this protocol, a random technique is used to save CH energy in the network. CH selection is based on standard rules that how many times a node can be a CH. In [21], a k-mean clustering approach is presented for wireless sensor networks. In this approach, the main issue is discovering the centroid vector, which leads to nodes group separation, guaranteeing the disconnection of connectivity. However, the benefits of these approaches give an overhead of cluster designing and selection of CH [22; 23; 24]. The overhead issues can be addressed with the help of medium access control (MAC). MAC introduces the concept of sleeping nodes when they have no data for transmission. MAC is defined by two categories: contention-free and contention-oriented [25]. These techniques initiate a part of collision when trying to use the concurrent channel. Additionally, packet loss is also increased when the network and sensor density are increased. Subsequently, the protocol that has conflict in route selection is not suitable for a dense network. Therefore, time-division multiple access (TDMA) is proposed to solve this issue [26; 27]. TDMA uses a time slot schedule that assigns an individual slot to each cluster member, resulting in increased network energy efficiency. Although the clustering algorithms described in [28; 29] are based on TDM, they do not take into account the potential of data failure in the network. In addition, deploying clustering involves a planning overhead, which has an impact on network resources. In [30], an interference aware self-optimizing (IASO) is proposed, which minimizes the interference of the network. The technique has the ability of multichannel sensing and the capability of control gain.