# Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks
## 6 Performance Results
TTL is acquainted for performance and privacy increment as shown in Table 6.  
The comparative result of Time to Live (TTL) of proposed method and existing method is shown in Figure 8, where the \(x\)-axis illustrates the speed of vehicles, and the \(y\)-axis illustrates the time. The comparative results show the better performance with lower numeric values of proposed Time to Live (TTL) than the existing techniques found in the literature. Figure 9 shows the fuzzy sets model gradual properties to model a theoretical possibilities matter the degree of satisfaction, and the data provided with uncertainty and imprecision allows them to be used in a vast variety of applications (RSS) feature of the member function, which also represents the performance of received signal. The received signal leads to obtain the parameter and then it is measured with range of radio. The RSS member function express the total in percent of radio ranges. The values for the \(x\)-axis are expressed in the range between 0 to 100%; the calculated percentage tells the closeness between two vehicles in the radio range, usually the total radio range is 100 m. The 0% defines that the smart device is out of radio range and radio signals are unreachable to the smart device, while 100% defines that the vehicles are close to each other and radio signal frequency is very strong. The communication and data transmission can be done once the radio signal's receiving rate is greater than of 30%. Effective and strong communication takes place when the rate receiving radio signal is above 70%; this describes that the vehicles are very close to each other; however, there would be no packet or frame loss while communication takes place at a distance. Figure 10 defines the value of traffic between devices and this value can be high, low and normal. Here the ratio parameter of channel capacity and capture packets is defined. The value of traffic is normal if traffic is represented greater than 30% but less than 70% in the channel capacity. Moreover, the value of traffic can be seen low if capacity of radio signal is less than of 30%, finally the value of traffic can be high if capacity of radio signal is less than of 70%. Figure 8: TTL (seconds). Figure 11 defines the signal interference ration (SIR) parameter for the calculated range of every cluster, and this wanted a signal energy to interfere with the vehicles range. However, the interference is dedicated for each device on selected cluster, and this is satisfied once the value is greater than 1; however, cluster is unsatisfied for the vehicle if the value is less than 1. Once the member function has been defined for every parameter, the next step of definition is to define the heuristic rules. Figure 10: Fuzzy logic for traffic membership function in percentage (%). Figure 9: Really Simple Syndication (RSS) membership function. The channel assignment mechanism needs to select the cluster first and then take the value for the vehicle figure's rank. Figure 12 shows the output of the fuzzy logic of Node 1. The value of every vehicle's rank depends on the input parameters and set of heuristic rules described in FL fuzzy logic. The maximum value illustrates the effectiveness of channel of communication. Here the fuzzy logic runs on very device and in-fact the parameters can vary depending on the quantity of vehicles within the radio range. PU activity, traffic, and channel ranking support cluster selection.