# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History  
[PERSON]  
1 Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain; [EMAIL_ADDRESS] (P.M.); [EMAIL_ADDRESS] (C.A. ); [EMAIL_ADDRESS] (C.M.) [PERSON]  
1 Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain; [EMAIL_ADDRESS] (P.M.); [EMAIL_ADDRESS] (C.A. ); [EMAIL_ADDRESS] (C.M.) [PERSON]  
1 Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain; [EMAIL_ADDRESS] (P.M.); [EMAIL_ADDRESS] (C.A. ); [EMAIL_ADDRESS] (C.M.) [PERSON]  
2 Department of Computer Architecture, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain  
23 Department of Computer Science, Universidad de Chile, Santiago 8370456, Chile; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Computer Science, Universidad de Chile, Santiago 8370456, Chile; [EMAIL_ADDRESS]  
Bahia Blanca 8000, Argentina; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel. : +34-93413710235454  
important number of topology propagation (TP) messages to keep the delivery of regular messages under control. The exchange of TP messages helps reduce the probability of overflowing the network with regular messages that do not reach the destination, rendering the communication system useless at supporting the coordination of the emergency response activities. Provided that many of these communication systems involve mobile ad hoc networks, the routing protocols used to support interactions among nodes (i.e., the first responders) must be simple, efficient and reliable and have the capability of quickly adapting themselves to changes in the network topology [7; 8; 9; 10]. Thus, these protocols intend to maximize the reachability of the target nodes, by consuming as little energy from the network as possible. Regardless of the extensive research done in the area, addressing this communication challenge, considering the previous constraints, is still an open issue. Some recent proposals try to deal with this challenge using deep learning in static or quasi-static networks [11]. Although deep learning techniques have shown positive results, these approaches are quite limited when used in mobile scenarios, like emergency responses. Moreover, they require a previous training stage to generate the network model. Novel solutions are therefore required to reduce the number of TP messages exchanged among the network nodes, and consequently, decrease the network traffic and energy consumption on the participating devices. The potential solutions should address this challenge, while keeping the routing capability of the used protocol. In a previous work the authors proposed a history-based approach to help address the stated communication challenge [12]; however, the validation of such a proposal was based on an analysis of the number of TP messages that can be potentially predicted by such a mechanism. This paper extends that previous work by presenting the implementation of a real predictor, and also by presenting a study that evaluates its prediction performance in highly mobile scenarios--this shows the feasibility of using the proposal in practice. Moreover, this article presents two prediction algorithms, one for each type of node involved in the propagation of the network topology information. The proposed history-based predictor (HBP) acts as a transparent communication intermediary, located between the routing and network layers, therefore, no changes to routing protocols are required for using it. In order to quantify its benefits, the predictor was implemented over the optimized link state routing (OLSR) protocol. The resulting implementation, named OLSR-HBP, was evaluated using simulations that involved several mobile scenarios. The obtained results were highly positive, showing that this prediction approach represents a tangible alternative to reduce traffic and energy consumption generated by the exchange of topology propagation messages.