## 2 Related Works  
One of the most important challenges in implementing IoT applications is the power management of hardware platforms. Nowadays, researchers are using many new technologies to minimize radio power consumption so that they can pave the way for IoT. The communication to sending and receiving data on the network is typically the most energy-consuming task in IoT applications [9]. Since the communication of the nodes is in the radio WakeUp state, so a lot of effort has been devoted to designing energy-efficient radio WakeUp models in the last decades. Different methods based on hardware and software have been suggested to control the radio WakeUp mode. [PERSON] et al. [10] have considered the combination of energy harvesting WakeUp receiver and LoRa radio technology to design a new IoT node for long and short-range networking. In another method in [11], BLE technology and WakeUp radio are integrated with energy harvesting. The design of both proposed methods is hardware-based so that a dual-radio mechanism using separate components is used in Node radio structure on a single chip that is expensive for IoT devices. When implementing WSN on a large scale, small hardware size and low cost are very important factors. CTP-WUR [12], [PERSON] et al. [13], and WUR-MAC [14] have introduced other methods that are more responsive to channel changes using protocol-based design. Recently, cognitive radio has been used as a solution to the opportunistic spectrum access in WSN [15]. ZIPPY [16] reduces latency and power consumption as a synchronization method using WakeUp radio and the MAC synchronization protocol. Node mobility is one of the important challenges in designing a wake-up schedule (e.g., [17]). Failure to consider node mobility, where node mobility is likely, leads to excessive overhead and as well as this results in poor schedule performance. In addition, the design of WakeUP-based radio systems should also address the challenges associated with WakeUp radio signal propagation in the forest, industrial, or indoor body environments. One of the important issues in low-power radios under WakeUp states is the coexistence with unrelated radio. For example, in-home automation systems and medical technologies, 802.15.4 radios are commonly used but the interference of these types of radios with waves of radio like Wi-Fi leads to a decrease in network performance. Another issue of discussion among researchers is the coexistence between 802.15.4 radios and unrelated radios that lead to the classification, detection, and reduction methods [7]. Airshark [18] and WiFiNet [19] get spectrum information using powerful Wi-Fi hardware and detect nonWi-Fi interference. Another method based on interference information is DOF [20] that provides the local wireless information plane. In the other method in [21], 16 ZigBee channel is scanned to classify spectrum characteristics. The authors, in [22], design a framework to scan the 2.4 GHz band. The beacons are periodically detected to be identified as Wi-Fi signals in ZiFi [23] and ZiFind. This method is dependent on long-term sampling. SoNIC [24] enables resource-limited sensor nodes to detect the type of interference they are exposed to and select an appropriate mitigation strategy. The key insight underlying SoNIC is that different interferers disrupt individual 802.15.4 packets in characteristic ways that can be detected by sensor nodes. [PERSON] et al. [25] proposed a CCA threshold adaptation method to reduce the impact of interference on packet loss in WSN. Under Wi-Fi interference, the proposed method results showed that increasing the CCA threshold has the effect of reducing the CCA collision and, consequently, improving the WSN packet delivery rate. In the proposed method, the CCA threshold is adjusted adaptively based on the transmit buffer overflows rate in the node. Since there is only one CCA check before transmitting in Zigbee, this approach is only Zigbee-based and is not the case for MAC protocols that are based on multiple CCA per transmission.