# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
## 4 Proposed CBF for IoT
### The IntraLayer Clustering (IALC)
The false positive and detection rate is considered as two utility functions for the inspecting mote. The aim of the flooder utility is to stop the successful transmission of route query packets and decrease the network throughput and QoS by attempting unwanted and redundant query strikes. The utility function (F\({}_{\mathrm{u}}\)) is set as follows, where detection rate and flooding attack gains are denoted as F\({}_{\mathrm{u}}\)1 and F\({}_{\mathrm{u}}\)2, respectively:

Figure 5:5

A reactive flooding attack strategy is formulated based on Equations (2), (6), and (7) for Ic and Ip shown below. Where F\({}_{\mathrm{d}}\) represents the duration of the flooding attack, F\({}_{\mathrm{dg}}\) stands for the gain of flooding detection, t denotes the I\({}_{\mathrm{p}}\) interval of time, F\({}_{\mathrm{ag}}\) symbolizes for the gain of a successfully launched flooding attack, the payoff of the reactive flooding is denoted by P\({}_{\mathrm{refa}}\), and both (P\({}_{\mathrm{c}}\), P\({}_{\mathrm{p}}\)) are the payoffs when (Ic, Ip) are used to sense the flooding attack. Payoff refers to the cost of initiating or detecting an attack:

Figure 5:6

Figure 5:7

The strategy for the reactive flooding attack is expressed in Equations (7)-(7b) for both (Ic, Ip). The flooder mote will immediately trigger an attack if it detects any activity in the communication link. All mathematical symbols used in Sections 3 and 4 are defined in Table 1 of the Appendix A. ## 5 Implementation Facts

This section elaborates the implementation details of the proposed approach. A cutting edge simulator known as Contiki Cooja is used to form redundant flooding scenarios [35] under realistic scenarios with a number of malicious motes and different intervals of traffic. A detailed specification of the simulation parameters are listed in Figure 4, in accordance with the IEEE 802.15.4 radio regulation. We used random topology because of the heterogeneous nature of IoT devices to present connectivities of sensor mote capabilities. The network size of the simulation model is 1-16 motes and are randomly deployed in an area of 100 m\({}^{2}\), where all motes are active transmitters and receivers. Furthermore, we evaluate the simulation based on three scenarios (i.e., varied intervals of traffic, varied number of malicious motes, and realistic scenarios) in all cases the inner arrival timing is exponential. Regarding traffic intensity, we explore different levels of saturation in the network by varying the traffic intensity, which may vary from low saturated networks (p\({}_{\text{max}}\) = 0.1) up to highly saturated networks (p\({}_{\text{max}}\) > 1). Values mentioned in Table 1 provide the foundation for an appropriate assessment of reactive flooding [36; 37]. The radio medium used for simulation is the Unit Disk Graph Medium (UDGM: distance loss) having a transmission range of 50 m and interference range of 100 m with an initial energy of 100 joules (J). Each device moves randomly with startup delay of one millisecond (ms). Each node consumes 31 milliwatt (mW) energy in an ideal state and 15 microwatt (\(\upmu\)W) in the sleeping state. A total of 35 and 31 mW energy is consumed during data receiving and sending states, respectively. Moreover, the transmission delay for high-speed links is insignificant. For example, a 1500-byte packet transmitted over a 155 Mbps STM-1/OC-3 link would take only 0.08 ms.

## 6 Results and Discussion

The performance estimation and evaluation of the proposed technique against up-to-date DnC, SLA [2; 3], and Hy-IoT [32] methods for tracing and mitigating the unwanted and redundant reactive flooding are described in this section. The routing protocol and MDP protocol [24] are ad hoc routing and Contiki, respectively.