# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 2 Related Work  
The overall performance of a routing protocol can significantly improve when using a prediction mechanism that has a high percentage of success. However, the use of predictors increases the complexity of the routing protocols, because of the additional hardware and software required to make and validate predictions. Moreover, penalty mechanisms are usually introduced to the system when there is a high rate of mispredictions, which negatively affect the performance of these protocols. Prediction mechanisms have been embedded in routing protocols to foresee several aspects of a network, such as nodes mobility [13], reliability of its topology [14; 15] and quality of links and end-to-end paths [16; 17; 18; 19; 20; 21; 22; 23; 24]. These mechanisms have also been used to reach particular communication goals, e.g., to support message delivery in mobile ad hoc networks (MANETs) [25; 26; 27; 28], reduce the traffic [29; 30; 31] and keep the energy consumption in these infrastructures under control [32; 33; 34]. Prediction strategies have been used to predict nodes mobility in MANETs; for instance, [PERSON] et al. [15] developed a technique to foresee disconnection of nodes in these networks. Their approach is based on the Markov chain model, and it considers topology information and global mobility to indicate possible disconnections in the near future. [PERSON] et al. [13] assumed a local mobility approach to estimate the link expiration time between adjacent mobile nodes. This prediction helps reconstruct routes proactively, and therefore, forecast a fairly accurate topology for the network. Another local mobility prediction approach was proposed by [PERSON] et al. [14] to foresee movements of nodes, from its current position to the next location, based on recent history. This prediction approach facilitates making decisions on route maintenance and resource reservation. When prediction is used to support routing in MANETs, two main approaches arise: (1) dealing with classical routing algorithms but supported by metric-based prediction; and (2) dealing with algorithms based on machine learning or deep learning. Assuming the former approach, [PERSON] et al. [25] proposed a dynamic routing framework that deals with real-time traffic to understand the traffic behaviour, and allows generating message forwarding rules that are stored into the routing tables. [PERSON] et al. [26] introduced a mechanism that uses artificial intelligence to improve the performance of routing protocols. This mechanism chooses the best data transmission paths according to the best criteria and based on the network status. Finally, [PERSON] et al. [27] proposed a Quality of Service (QoS) aware adaptive routing method to enable adaptive packet forwarding, by applying reinforcement learning in multi-layer hierarchical software-defined network. In the latter approach, classical routing algorithms are considered, but they involve the use of metrics based on machine learning to make predictions. For instance, [PERSON] et al. [28] proposed a method that builds the network traffic matrix in real-time, by means of time series prediction, achieving high prediction accuracy in a very short training time. Concerning the prediction of topology propagation (TP) messages based on historical information, in previous works, the authors have shown that the traffic generated by a link-state routing protocol, like OLSR [35], grows almost exponentially with the number of nodes when different node densities are considered in the analysis [29; 30]. Therefore, a network with a large number of nodes requires the exchange of a huge amount of TP messages to inform the nodes about the topology changes. These messages not only overload the communication links, but also increase the energy consumption of the nodes. The authors have also shown that the problem of delivering so much control information through the network can be addressed using predictions [29; 31].