# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 4 ReBLOM-Based Crowdsourcing for EVs Routing Problem with Recharging Station
### Crowdsourcing Task for Road Conditions  
As mentioned, the process of solving crowdsourcing tasks mainly includes distribution, execution and recovery. Task distribution refers to the release of road condition requests, the execution means the computing process of miners, and recovery consists of verification and reputation evaluation. Judging the decisive factors of crowdsourcing tasks can help analyze the difficulty level based on URC, and maximize the incentive effect. Suppose the crowdsourcing task \(\mathcal{T}_{u}=(\mathcal{T}_{1},\mathcal{T}_{2},\ldots)^{T}\) with the distance attribute \(d\) could be divided into \(u\) sub-tasks according to ingredients, the benefits and reputation bonus are divided as follows. \[ \begin{array}{l}C^{d}_{\mathcal{T}_{1}}=\omega_{1,1}V_{I}+\omega_{1,2}V_{II} +\omega_{1,3}V_{III}+\omega_{1,4}V_{IV}\\ C^{d}_{\mathcal{T}_{2}}=\omega_{2,1}V_{I}+\omega_{2,2}V_{II}+\omega_{2,3}V_{ III}+\omega_{2,4}V_{IV}\\ \ldots\\ C^{d}_{\mathcal{T}_{u}}=\omega_{u,1}V_{I}+\omega_{u,2}V_{II}+\omega_{u,3}V_{ III}+\omega_{u,4}V_{IV}\end{array}  \tag{17}\]  
where the weight factors are  
\[ \begin{array}{l}\omega_{1,1}+\omega_{1,2}+\omega_{1,3}+\omega_{1,4}=1\\ \omega_{2,1}+\omega_{2,2}+\omega_{2,3}+\omega_{2,4}=1\\ \ldots\\ \omega_{u,1}+\omega_{u,2}+\omega_{u,3}+\omega_{u,4}=1\end{array}  \tag{18}\]Then we have  
\[C^{d}_{T_{u}}=V_{I}\sum_{\eta=1}^{u}\omega_{\eta,1}+V_{II}\sum_{\eta=1}^{u}\omega_ {\eta,2}+V_{III}\sum_{\eta=1}^{u}\omega_{\eta,3}+V_{IV}\sum_{\eta=1}^{u}\omega_{ \eta,A} \tag{19}\]  
where \(V_{I}-V_{IV}\) are the subjective (it mainly refers to subjective road conditions, such as driving status, vehicle status, etc. ), objective (the mainly objective road conditions factors), interaction (historical interaction evaluations) and stochastic factors, respectively. In addition, \(V_{I}-V_{IV}\) are the determining factors for the difficulty of the crowdsourcing task. Rational miners can choose their individual task preference according to the vehicle status and configuration. In this way, the system can also maximize the reward for miners to join the blockchain. To study the preference effect of multiple factors/weights, we formulate the following schemes:  
* Based on the _Fuzzy Evaluation Theory_[37], the factors \(V_{I}-V_{IV}\) (i.e., subjective, objective, interaction and objective factors) that determine the difficulty of the task can be effectively evaluated. First of all, by constructing a multi-level analysis structure model through the AHP method, the factor universe of the evaluation object can be determined; secondly, determine the comment level universe set, where a set of various total evaluation results in the evaluator creating the object to be evaluated; thirdly, establish the fuzzy relationship matrix and then determine the fuzzy weight vector of the evaluation factors, that is, assign the corresponding weights to each factor; finally, the fuzzy comprehensive evaluation model is obtained. Fuzzy evaluation deals with the fuzzy evaluation objects by precise digital means, and can make a scientific, reasonable and realistic quantitative evaluation for the data that contains the ambiguity. * _Entropy Value Method_[38]. According to the definition of information entropy, for a special index (e.g., \(V_{I}-V_{IV}\)), we can use the entropy value to judge the degree of dispersion of an index: a smaller entropy value will lead to a greater degree of dispersion of the index, and a greater impact (i.e., weight) of the index on the comprehensive evaluation [39]. The scheme is as follows: first, the index system for evaluation needs to be determined; then, the maximum/minimum values in each index are eliminated to reduce the influence of extreme value data on the entropy; thirdly, the critical value method or Z-score method can help quantify each index, that is, converts the actual value of the index into an index evaluation value that is not affected by the dimension; finally, calculates the index entropy and weight, and obtains the index weighted calculation score.