# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 2 Related Work
Both algorithms divide the entire sample space into groups of comparable nodes depending on the shortest distance among nodes and a central node, CH. In a k-medoids algorithm, the cluster's center is always a node, but in a k-means algorithm, it may or may not be. As the mobility of nodes in VANET is high, choosing a geographical place other than the node as the cluster center can induce clustering instability. Furthermore, any approximation made in this direction by choosing the closest node to a central place will reduce accuracy and increase processing overhead. Moreover, compared to a k-means algorithm, a k-medoids algorithm is much more tolerating of outliers [21]. The study in [23] presents the fuzzy logic-based technique for creating clusters with long lifetimes. Three parameters are taken into account in this approach. First, it combines node relative speeds with their associated CHs. Second, it examines a node's number of available links inside a cluster, and third, it considers node security. This method can still be used in uni-directional highway scenarios. In a bi-directional highway scenario, however, selecting member nodes with the same speed as its CHs may be ineffective because they could travel in different directions. This reduces the time it takes for the node to be associated with a cluster. Another similar clustering strategy was proposed in [24]; however, it lacks the potential to tolerate the variability of bi-directional road traffic. In addition, the authors of [25] considered connection dependability during clustering. However, this strategy assumes a fixed arrival rate for motorway nodes, which is unsustainable. The work of [26] presents a comparative distance-based clustering algorithm that performs poorly in real highway scenarios due to a lack of direction evaluation. Likewise, the authors of [27] adopted a grey wolf optimization-based clustering method to limit the number of clusters. On the other hand, the approach has a significant computational overhead when identifying weak and aging nodes. One well-known clustering protocol is Low-Energy Adaptive Clustering Hierarchy (LEACH) [28], which has various multi-hop and single-hop variations. LEACH Fuzzy Inference System (LEACH-FIS) [29] and Quadrature Low Energy Adaptive Cluster Hierarchy (FCM-QLEACH) [30] are two current VANET-specific variations. All LEACH techniques are energy-efficient, which is their main advantage, but significant clustering overheads limit their performance. Similarly, the authors of [31] presented a clustering technique in which a gateway node is introduced as a relay node between CHs. The gateway node provides the connection between two cluster heads to increase information range without needing roadside units. This scheme is best for uni-directional traffic, and suitable for urban VANETs and unsuitable for highway environments. EEMDS also ignores the node's direction, degrading the scheme's performance. The work of RBO-EM [32] presents a clustering scheme in which the nodes' numbers are reduced that can retransmit an emergency message, and linked condition reliability is employed to choose a trustworthy gateway. RBO-EM also ignores the node's direction, degrading the scheme's performance. Similarly, the authors of BRUP [33] presented a network that was hierarchically partitioned into numerous clusters on a roundabout in an urban scenario, each associated with the CH. Only the CH was accountable for the retransmission of WMs in every cluster to avoid redundant transmission and ensure reliable WM dissemination. Furthermore, ref. [33] employed a k-medoids algorithm for CH selection and Hamming distance for finding a node's movement direction to maximize the cluster's lifetime, and it has been proven to operate well in a roundabout in urban settings. BURP does not apply to highways environment where mobility is very high. This paper proposes OPRP as an extension of [33] to highway environments. The authors of [1; 34] described message dissemination using SND.