## 3 MADM Methodologies  
In the C-DGNSS framework, the target vehicle's low-cost GNSS receiver strives to acquire GNSS corrections from the neighbor vehicles in the vicinity. Each neighbor vehicle transmits both PVT data (NMEA sentences) and corrections (Radio Technical Commission for Maritime (RTCM) Services messages). For latency-sensitive applications and critical ITS in general, it is best to rank the moving vehicles in the neighborhood and select the optimal one with which to cooperate. The ranking of moving neighbor vehicles will be estimated using only PVT data that is transmitted, while the PVT solution status of each vehicle will vary; it may be RTK-fixed or a standalone solution. After selecting the best neighbor vehicle, the target car selects to receive the corrections only from this particular neighbor vehicle and tries to estimate the integer phase ambiguities in order to improve the standard deviation of its solution. A MADM module is proposed to aid the neighbor selection using a variety of criteria, weights, and alternatives and finally rank higher a neighbor vehicle with RTK fixed solution than another standalone. MADM algorithms are computational decision-making methods that decide upon the optimal alternative or rank a specified set of alternatives [22,23]. The performance of each alternative to a specific criterion/attribute is called criterion value, and it is stored in the decision matrix of size (\(P\times Q\)), where \(P\) is the number of alternatives and \(Q\) is the number of criteria [22]. The derivation of the criteria weights vector is a complicated task; hence a variety of objective (e.g., entropy method) and subjective (e.g., direct rating) weighting techniques are available [22]. Additionally, the MADM algorithms are founded on linear aggregation and causality, which implies that strong inter-dependencies between criteria may disrupt the ranking outcomes. Finally, the normalization of the criteria values is a pre-processing conversion to derive a common scale and comparable input data. It is necessary to apply the right normalization techniques to facilitate the decision-making process. Given certain weighted criteria, normalizedcriteria values are fed as input to the MADM module, and the sorting of the alternatives is outputted. The arrangement of the decision matrix (\(D.M.\)) is illustrated in (1):  
\[D.M.= \begin{pmatrix}C_{1}&C_{2}&C_{3}&\ldots&C_{Q}\\ \par a_{1,1}&a_{1,2}&a_{1,3}&\ldots&a_{1,Q}\\ \par a_{2,1}&a_{2,2}&\vdots&\vdots&a_{2,Q}\\ \par a_{3,1}&\vdots&\ddots&\vdots&a_{3,Q}\\ \par\vdots&\vdots&\vdots&\ddots&\vdots\\ \par a_{P,1}&a_{P,2}&a_{P,3}&\ldots&a_{P,Q}\end{pmatrix}\begin{pmatrix}A_{1} \\ A_{2}\\ A_{3}\\ \par\vdots\\ A_{P}\end{pmatrix} , \tag{1}\]  
where \(A_{1}\), \(A_{2}\), \(A_{3}\), \(\ldots\), \(A_{P}\) are the alternatives to be employed for decision making, \(C_{1}\), \(C_{2}\), \(C_{3}\), \(\ldots\), \(C_{Q}\) are the decision criteria, and \(\alpha_{ij}\) is the performance value of the \(i_{th}\) alternative with respect to the \(j_{th}\) criterion. Currently, a wide variety of MADM methodologies and normalization techniques are available [22; 23]. They can be divided according to the decision process and their common characteristics. The MADM algorithms typically employ score functions, outranking relations, hierarchy structures, and others for optimal selection. Table 3 exhibits some well-accepted and employed MADM methodologies [22; 23]. The Simple Additive Weighting (SAW), Complex Proportional Assessment (COPRAS), Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA), and Grey Relational Analysis (GRA) belong to the scoring-based MADM family. The scoring class is regarded to have the minimum complexity, cost, and latency and is quite readable and interpretable by the decision-makers. Moreover, the intake criteria values from the formulated decision matrix are generated, and then a scoring function is employed to output a single value for each alternative.