# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
## 2 Related Works  
To address flooding in IoT networks, various defense techniques have been design and developed. Safeguarding the IoT networks from redundant and unwanted flooding has been studied broadly and is not a new problem. As mentioned, DoS attacks flood the network, and may leads to increases in network queries. It also raises network resource utilization, and degrades QoS, along the communication link [15]. In a variety of domains, flooding attacks have been probed, such as a hybrid energy-aware clustered protocol for IoT heterogeneous networks using Hy-IoT proposed by [16]. Hy-IoT provides a real-world cyber IoT architecture based on clusters in managing the heterogeneous IoT network. It also provides an efficient manner of selecting cluster heads which boosts the utilization of the motes energy contents and consequently increases the network life time, as well as the packet transmission rate to the base station. Scalability issues in both the population of motes and the network density towards integrating IoT and SDN controllers is a major challenge in this approach. Ref. [17] proposed an adaptive meta-heuristic search for redundancy in IoT network using the AntClust technique. This approach reduces the cost of the sensor search process and provides an adaptive strategy to maintain the performance of IoT devices against dynamicity in the IoT environment. However, the performance of this approach is only limited to accuracy and no real-time implementation scenario is provided. Ref. [18] used process querying techniques to develop an enabling business intelligence for resource-constrained devices. This approach guides development for process querying methods and specifies a formal instruction to manage the given repository. The lack of good QoS is the major challenge in this approach. Reference [19] proposed a scalability mechanism for IoT devices. Since scalability has become an important aspect that needs to be considered in any IoT system, the proposed mechanism enables IoT devices to be adaptable to environmental change. The approach lacks QoS and no mechanism to detect redundant queries is provided. Additionally, a three-level framework for IoT redundancy control was proposed by [20]. The framework uses an explicit spatio-temporal data model to control service redundancy at three scales: macro, meso, and micro scales, respectively, in IoT networks. However, many other fundamental components, such as the functional architecture, the algorithms of redundancy data generation and their complexity, as well as the proactive redundancy control scheme of the framework are not be presented. Ref. [21] used a divide-and-conquer (DnC) method to develop an approach for improving energy efficiency in QoS-constrained WSNs (wireless sensor networks). The core idea behind the DnC method is to control the QoS parameters while providing adequate network lifetime. However, there is a need for a real testbed to evaluate the proposed approach in a more realistic manner. Moreover, the overhead of hierarchical MAPE adaptation architecture also needs to be analyzed. Reference [9] proposed an end-to-end (E2E) QoS specification and monitoring scheme for IoT networks. The authors used service-level agreements (SLA) to determine the specific flooding problems in various IoT devices, such as smart environments, smart cities, smart metering, smart water, smart farming, smart agriculture, industrial control, retail, logistics, domestic, home automation, and e-Health. SLA lacks unified/standard methods for collecting the required metrics across layers and from multiple providers for E2E. Similarly, Ref. [2] proposed a QoS aware resource scheduling for IoT using \"Load Balanced Particle Swarm Optimization\". The essence of this approach is to reduce application response time and ensures load balancing and provides a minimum response time in an IoT-cloud platform. However, the technique needs to extended to support multiple levels of QoS in IoT networks.