## 7 Conclusions  
The 802.15.4 radios are low power radios that are used in many applications of the IoT and wireless sensor networks on a variety of platforms. Some operating systems offer varieties of radio duty cycle (RDC) mechanism for controlling 802.15.4 radios. One of the challenges in RDC protocols is WakeUps in idle listening and falseWakeUps (Section 4). The proposed method LW-CCA aims to reduce the wakeup time caused by false WakeUp and idle listening nodes. For this purpose, the performance of ContikiMAC is evaluated according to the time factors affecting each WakeUp. RSSI status check time is an important time factor to the radio. ContikiMAC considers a fixed time of 20 symbols (0.32 ms) to perform an RSSI check time per WakeUp. The proposed LW-CCA method, unlike ContikiMAC, considers two values of minimum and maximum 8 symbols (0.128 ms) and 28 symbols for the radio check. (Section 5). The value of eight symbols is the minimum value based on the CC2420 Datasheet. The LW-CCA nodes classify RSSI values based on CCA validation. LW-CCA considers eight-symbol radio check time if RSSI is detected as idle listening or false WakeUp otherwise it considers a 28 symbol. LW-CCA nodes, based on the simulations of Section 6 have reduced 18% of duty cycle compared to ContikiMAC. The based on simulation outputs of Section 6, by considering the power parameters and indicated scenario in Section 3, the LW-CCA is able to reduce approximately 0.12 mW in average power consumed in Rx mode. Therefore, it is able to lower the overall power consumption level about 8% in the nodes is considered high and low data transmission rates. Furthermore, the LW-CCA reduces energy consumption while it maintains packet delivery rate 99%, which is the same as ContikiMAC in sender nodes. The proposed method can be applied to all IoT platforms based on 802.15.4 radios such as Z1, Tmote-SKY, and Micaz. Since RSSI validation is based on CCA check, it can also be exploited in the real environment. The instantaneous categorization mechanism in the LW-CCA method can make this method flexible against interference caused by irrelevant radios or noise. According to Figure 9, the proposed method is not suitable for applications with high transmission data rates. In fact, the efficiency of the proposed method decreases with \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**RDC** & **P\_CPU (mW)** & **P\_LPM (mW)** & **P\_Rx (mW)** & **P\_Tx (mW)** & **P (mW)** & **PDR (\%)** \\ \hline ContikiMAC (low rate) & 0.477 & 0.149 & 0.603 & 0.152 & 1.381 & 99 \\ ContikiMAC (high rate) & 0.498 & 0.148 & 0.823 & 0.277 & 1.746 & 99 \\ LW-CCA (low rate) & 0.471 & 0.149 & 0.490 & 0.150 & 1.260 & 99 \\ LW-CCA (high rate) & 0.515 & 0.147 & 0.679 & 0.281 & 1.622 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average of power consumption in CPU, LPM, Rx, Tx, total power consumption, and PDR. Figure 9: Average improvement of LW-CCA energy consumption at different data transmission rates. increasing interference in the network. The more balanced the node distribution, the better the proposed method will perform in terms of network lifetime. Conceptualization, modeling, data curation, data analysis, and analysis of results, M.A.N., S.S., A.T.C., A.M., and N.N. ; IoT, machine learning, and soft computing expertise, M.A.N., S.S., A.T.C., A.M., and N.N. ; Mathematics expertise, M.A.N., S.S., A.T.C., A.M., and N.N. ; Management, database, writing, administration, and methodology M.A.N., S.S., A.T.C., A.M., and N.N. ; Visualization, M.A.N., S.S., A.T.C., A.M., and N.N. ; Supervision, resources, software, revision, and verification of the results, A.T.C., A.M., and S.S., funding, A.M. All authors have read and agreed to the published version of the manuscript. We acknowledge the financial support of this work by the European Union and the Hungarian State under the EFOP-3.6.1-16-2016-00010 project and the 2017-1.3.1-VKE-2017-00025 project.