## 4 Edge Computing Offloading Method in Internet of Vehicles
### Edge Service Node Selection  
Fuzzy control is an intelligent control method based on fuzzy set theory, fuzzy language variables, and fuzzy logic reasoning. It is an intelligent control algorithm that imitates human fuzzy reasoning and decision-making process in behavior. In fuzzy control, the experience of operators or experts are composed of fuzzy rules, and then the real-time signals from sensors are fuzziness; the fuzziness signals are used as the input of the fuzzy rules to complete fuzzy inference, and the output obtained after inference is added to the actuator. Fuzzy inference systems include both Mamdani and Sugeno models, both of which are used to achieve decision processing in complex systems. The latter has high complexity but little performance improvement, making it not the best choice for fuzzy inference. Therefore, a service node selection algorithm based on the fuzzy logic control principle (SNFLC) is suggested in this part, and its structure is shown in Figure 2.  
Load ratio (LR) determines whether it has enough room to store vehicle tasks. By introducing the three indicators, task mobility (TM) depicts the node's task-forwarding effectiveness, continuous connectivity probability (CCP) gauges the node's dependability,  
Figure 2: Fuzzy logic decision maker. and performance of the service node (PSN) is determined, as shown in Figure 3. It is important to note that the network architecture of the internet of vehicles is constantly evolving. The topology structure is assumed to be constant throughout each time slot segment in order to efficiently complete the duties that come after. 1. LR  
Load ratio describes the load balance of RSU at a certain time slot \(\tau\). The load ratio at any node is defined by the task queue length \(Q(\tau)\) and cache capacity threshold \(Q_{max}\) of the time slot \(\tau\). The expression (8) shows that the smaller the load ratio is, the larger the remaining storage space is, and the more likely it is to successfully store vehicle tasks. \[LR_{j}(\tau)=\frac{Q_{j}(\tau)}{Q_{\max}}\] (8)
2. TM  
Vehicle tasks arrive at the service node in the form of data packets, and task mobility is a metric to measure the task processing rate of the node. The larger the mobility rate, the faster the task can be migrated to the MEC server for processing. The task arrival rate can be represented by using the sliding exponential average algorithm to mimic the packet arrival rate \(Arrive(\tau)\). \[Arrive(\tau)=\frac{dQ(\tau)(1-\mathrm{e}^{-\Delta/\wp})}{\Delta}+\mathrm{e}^{ -\Delta/\wp}Arrive(\tau-1) \tag{9}\]  
where \(\wp\) is a constant, reflecting the correlation between the arrival rate of the task before and after and the time. Usually, the value of \(\mathrm{e}^{-\Delta/\wp}\) is 0.1, which is used to represent the instantaneous rate. \(dQ(\tau)=Q(\tau)-Q(\tau-1)\) indicates the number of tasks that reach the node in a time slot \(\tau\). \(Service(\tau)\) is the node forwarding service rate, so the task migration rate can be expressed as the ratio of the two. \[Tan_{j}(\tau)=Arrive(\tau)/Service(\tau) \tag{10}\]
3. CCP  
The benchmark used to assess the node's dependability is the constant connection probability between the vehicle and the node. The higher the probability is, the longer  
Figure 3: Performance counter interaction. the connection will not be forced to terminate the task due to the signal terminal, and the connection will always exist until the end of the task processing. The Poisson distribution with density as \(\ell\) is satisfied by the system's car distribution. On a road with as area denoted as A, the number of vehicles is a Poisson random variable.