# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
## 2 Related Works
Reference [22] proposed a node-level energy efficiency protocol for IoT devices to improve the energy efficiency in an IoT network. The proposed algorithm uses transmission count makes the decision for finding the shortest hop count. However, the approach does not explore other metrics of QoS. Ref. [23] proposed a QoS architecture for IoT and cloud computing platforms to enable public/users to have easy access over diversified smart applications and services distributed in the cloud with one IoT-enabled Intelligent Smart Card (ISC) through mobile devices with assured quality of service. The architecture lacks the following metrics of traffic filtering, queue scheduling, congestion management, load balancing, E2E delay management for real-time service, and resource allocation across multiple networks. In addition, modeling QoS in IoT applications was proposed by [24]. The model used a push-out buffer management scheme and preemptive resume (PR) service priority to analyze a finite capacity queue in an IoT network to evaluate the performance of smart devices under varying traffic conditions to ensure preferential treatment of highest priority delay-sensitive data. Long processing time and delays in exchanging information with other devices are major drawbacks in the model. Ref. [25] discussed network architecture and QoS issues in the IoT for a smart city. The authors focused on the communications and networking aspect of the IoT. They identified and proposed a variety of network architectures for smart city applications, and also define their corresponding performance metrics in order to maintain QoS guarantees. However, hardware failure leads to issues of fault tolerance. Ref. [26] proposed a discrete component circuit implementation model together with its computational simulations using [PERSON]'s system. It is an endogenous nonlinear and inseparable cycle that is electronically implemented in circuits. Additionally, a novel countermeasure technique for detecting and curing reactive jamming attacks in IoT networks was proposed in [3]. The proposed technique is called the countermeasure detection and consistency algorithm (CDCA), which is based on threshold values and assumptions to fight reactive jamming attacks. In accordance with the snags, observed from the current available methods, a QoS-enabled CBF solution is proposed for flooding attacks in IoT networks. The proposed technique is an interoperable solution both for physical layer and network layer devices. CBF divides the whole network into different clusters; local query information is proactively maintained by the IALC, while IELC is responsible for reactively obtaining the routing queries to destinations outside the cluster. CBF is a hybrid approach, having the potential to be more efficient against traditional schemes in terms of query traffic generation. However, in the absence of appropriate redundant query detection and termination techniques, the CBF may generate more control traffic compared to standard flooding techniques. Interlayer clustering (IELC) is composed of advanced query detection and termination techniques (QCM) that link signal strength and QueryLimitThreshold (QLT) values for detecting flooding and it is capable of minimizing the energy consumption, network flooding, and identifying and eliminating unwanted and redundant routing queries in IoT networks. During flooding attacks this technique is accountable for checking more locality and query detection strength in an IoT network. The strength of query detection is observed for verifying, any variation concerning the signal strength of query packet, and the QLT. The position stability of the more is based on the location information of the legitimate mode neighbors with its fixed QLT, and observed continuously at different intervals of time.