# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
## 4 Proposed CBF for IoT
### The IntraLayer Clustering (IALC)
In Figure 11a, network throughput (QoS) of DnC and SLA are decreases up to approximately (44% and 61%), respectively at an interval of 1 s. While, with the proposed QCM the QoS boosts to approximately 85%, this result is 9% better as compared to existing Hy-IoT at the same time interval 1 s. According to the result as shown, the performance of network throughput (QoS) is inversely proportional to the time interval, as QoS decreases with the increase in the interval. Flooder motes keep engaged the transmission link, which generates unwanted and redundant routing request queries and directly affects the QoS of the IoT networks. Figure 11b shows the network throughput (QoS) of QCM, DnC, SLA, and Hy-IoT with increase in the number of malicious motes. Here, initially at malicious mote 2 the network throughput (QoS) of DnC and SLA is approximately (39% and 44%), and finally at malicious mote 15 this percentage gradually drops to approximately (30 and 35%), respectively. While with proposed QCM the (QoS) at malicious mote 2 is about 86% and gradually drops to approximately 53% at malicious mote 15. This results are approximately (9% and 6%) better as compared to the existing Hy-IoT at malicious motes (2 and 15), respectively. It is because; the proposed mechanism is capable of providing fast link access to the motes during redundant query attacks. Network throughput (QoS) of QCM, DnC, SLA and Hy-IoT is analyzed in realistic network conditions with increasing number of malicious motes as shown in Figure 11c. The simulation results revealed that the network throughput of DnC and SLA with respect to malicious mote 1 is approximately (34% and 37%), respectively, and this ratio drops to approximately (17% and 25%), respectively at malicious mote 15. The proposed QCM mechanism boosts the QoS throughput to approximately (80% and 49%) at malicious motes (1 and 15), respectively. This increase in result of network throughput by QCM is approximately (12% and 5%) better as compared to the existing Hy-IoT at malicious motes (1 and 15), respectively in a realistic condition. ## 7 Conclusions and Future Work

This paper has studied numerous defensive techniques against unwanted and redundant routing queries which may lead to heavy network traffic and flooding in IoT networks. In this study, we implement the reactive part Interlayer clustering (IELC) of cluster-based flooding (CBF), and proposed a query control mechanism (QCM) to detect and terminate the unwanted and redundant queries which is based on link signal strength, consistency of query packets, and a query limit threshold. Furthermore, it is evident from the results that proposed QCM appeared superior performance compared with the state of the art defensive techniques in terms of average consumption of energy, traffic delay, and QoS, which we referred to as network throughput. Thus, QCM drops the average consumption of energy to almost 6% compared to the DnC, SLA, and Hy-IoT that consume approximately (21%, 18%, and 13%) under varying intervals of traffic. The performance of QCM is also better regarding average consumption of energy with malicious motes against the traditional approaches by dropping the consumption at motes (2, 6, and 15), respectively to about (2%, 4%, and 20%). Additionally, QCM also exhibits dominant performance regarding network delay by decreasing the delay to about 10%, which is 4% less as compared to the state of the art. While in cases of malicious motes, the proposed QCM drops the network delay to approximately (7% and 37%) at malicious motes (2 and 15), respectively. Lastly, QCM enhances the amount of QoS to 85%, that is 9% greater as compared to Hy-IoT. The proposed QCM technique employs QueryLimitThreshold (QLT) for detecting and terminating the redundant and unwanted query request packets, and in this

Figure 11: QoS with respect to different interval of traffic (**a**), with malicious motes (**b**), and malicious motes with realistic condition (**c**).