## 3 Proposed Architecture
### Description of IFS-CMR Subsystems  
The input parameters of each subsystem of IFS-CMR do not correlate to one another, leading to an NP-hard problem. Problems with three or more uncorrelated parameters are  
Figure 4: Graphical representation of vehicles moving at different velocities and directions. classified as NP-hard because finding a mathematical model that can calculate an output in polynomial-time is practically impossible. Heuristic or meta-heuristic approaches are proven to provide adequate solutions for these kinds of problems, but each method has a limited scope to which it can be applied. For instance, genetic algorithms give good solutions for optimization and allocation problems. Neural networks can be applied to recognition problems and rule learning. FL, on the other hand, can be used to provide a solution for decision-making and control problems in real-time, especially when the system contends with high levels of imprecision and uncertainty [47; 48; 49; 50; 51; 52]. Making real-time decisions while dealing with imprecision and uncertainty is the advantage of IFS-CMR. The characteristics of the network created between vehicles change continually and rapidly; therefore, the resources must be managed in real-time. Moreover, because of the rapid changes, the decision is reached in presence of much imprecision. IFS-CMR is comprised of three integrated subsystems (FS-AQoS, FS-ANVPC, and FS-CFELS), each controlled by its respective FLC. Each subsystem has a key role in the system. They have their own input parameters and their output serves the subsystem that follows. The structure of IFS-CMR is shown in Figure 3. The way we have built IFS-CMR has allowed us to continually improve it by discovering the implementation flaws (contrary to many AI systems, which behave as black boxes that do not provide any feedback how they reach their decision, and consequently are difficult to improve). The term sets for the parameters of each subsystem are shown in Tables 1-3. The parameters are fuzzified using the membership functions shown in Figures 5-7. The number of terms for each parameter and the characteristics of each membership function are determined through the experience gained by running many simulations. From our experience, using less than three linguistic terms for an input parameter has the risk of inefficient control and making poor decisions, whereas using more leads to redundancies and increased complexity. The same holds true for the overlap of membership functions. Less overlap results in poor decisions, more overlap brings redundancies. Regarding the shape, we use triangular and trapezoidal membership functions as they are the most suitable ones for real-time operation. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Term Sets** \\ \hline Link Latency (LL) & Low (Lo), Medium (Me), High (Hi) \\ Radio Interference (RI) & Permissible (Pe), Acceptable (Ac), Harmful (Ha) \\ Effective Reliability (ER) & Not Effective (Nef), Medium Effective (Ref), \\  & Effective (Ef) \\ Update Info. for Vehicle Position (UIVP) & Few (Fw), Moderate (Mo), Many (Ma) \\  & Extremely Low (El), Very Low (VI), Low (Lw), \\ Quality of Service (QoS) & Moderate (Md), \\  & High (Hg), Very High (Vh), Extremely High (Eh) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters and term sets for FS-AQoS. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Term Sets** \\ \hline Available Computing Power (ACP) & Small (Sm), Medium (Me), Large (La) \\ Available Storage (AS) & Small (S), Medium (M), Big (B) \\ Predicted Contact Duration (PCD) & Short (Sh), Medium (Md), Long (Lo) \\ Quality of Service (QoS) & Low (Lw), Moderate (Mo), High (Hi) \\  & Extremely Low PC (ELPC), Very Low PC (VLPC), \\ Neighbor \(i\) Processing Capability (NiPC) & Low PC (LPC), Moderate PC (MPC), High PC (HPC), \\  & Very High PC (VHPC), Extremely High PC (EHPC) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters and term sets for FS-ANVPC.