# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 1 Introduction
### Related Work  
In the field of mobile ad hoc networks, numerous experts are dedicated to researching how to address the issue of network storms caused by the broadcast of control messages. This section primarily focuses on previous work in this area, exploring how different environmental factors have been utilized to tackle the problem, as well as the remaining challenges. In [7], an optimized protocol called ND-AODV is proposed, which primarily considers utilizing the number of neighbor nodes to determine whether a node exists in a high-density environment. If it is determined that the node is in a high-density environment, the protocol uses a ratio between a threshold and the number of neighbor nodes to calculate its forwarding probability. This algorithm aims to reduce network overhead, but it does encounter some problems. One of the problems is the lack of precise information about the specific positions of neighbor nodes. This can lead to potential route loss if the node happens to be on a critical route towards the destination node. In [8], a routing approach called PAODV (probability AODV) is proposed, which also takes into account the node density. The idea behind PAODV is that as the network density increases and each node has more neighbors, the probability of finding multiple routes from the source to the destination also increases. Therefore, limiting the number of RREQ (route request) packets that are rebroadcast by intermediate nodes can reduce the overall network overhead. If the node density is sparse, a higher forwarding probability (p1) is used for forwarding. Conversely, if the node density is high, a lower forwarding probability (p2) is used for controlling the forwarding of control messages. In [9], an analysis is provided to determine the optimal values of forwarding probabilities for both high-density and low-density scenarios. However, these forwarding probabilities are set before running the simulations and do not consider the specific network dynamics. Therefore, in situations where the node density is either too low or too high, there may still be issues of routes not being discovered or excessive network overhead, and there is not good scalability. In [10; 11], a routing metric optimization method called AODV-ETX (AODV with expected transmission count) is proposed. It selects routes by estimating the optimal route to neighbor nodes based on the expected transmission count (ETX) rather than the hop count metric used in the original AODV. By optimizing the routing metric, the goal is to reduce network overhead. In this method, ETX is primarily based on the reciprocal of the success probability of new control information called link probe packets (LPP) exchanged between nodes. The optimal ETX value from the source to the destination is determined by the best new metric obtained from the nodes along the route. However, this method introduces new control information (LPP), which can lead to increased control message overhead in networks with rapidly changing topologies. As a result, AODV-ETX is more suitable for mobile ad hoc networks with relatively stable topologies. In [12], an optimization method for ETX is proposed, consisting of three variants: L-ETX (light ETX), LR-ETX (light reverse ETX), and PLR-ETX (power light reverse ETX). These three methods also optimize the routing metric but do not specifically address the issue of rapidly changing topologies. In [13], a self-adaptive multipath routing algorithm called GA-AOMDV (genetic algorithm AOMDV) is introduced. This algorithm selects nodes with high remaining energy as routers, reducing energy consumption during the data packet communication process. In [14], another adaptive algorithm called RLLMR (reliable low-latency multipath routing) is proposed. It dynamically adjusts the parallel routing multipath algorithm based on service requirements to meet different services' latency demands while conserving routing resources.