# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 6 Conclusions and Future Work  
This work presents the implementation of the OLSR-HBP, a TP message predictor that can be located between the routing and the network layers of mobile ad hoc networks. Its main goal is to reduce the number of TP messages that must be delivered through the network, and thus to decrease its congestion and energy consumption. Improving these aspects are particularly relevant in dynamic ad hoc collaboration scenarios, like responses to urban emergencies or disaster relief efforts, since it allows increasing the availability of the supporting systems. Typically, in these scenarios first responders (e.g., firefighters, police officers and medical personnel) count on weak and dynamic digital communication links, and the availability of these links depends on the battery available in the devices participating in the network. Reducing message congestion and energy consumption in the supporting networks usually improve the network availability and throughput, by easing thus the response process. The performance of the OLSR-HBP predictor was evaluated involving communication scenarios with different complexity; all of them simulated urban emergencies and considered mobile ad hoc interactions among the nodes (e.g., first responders). The evaluation results show that, for mobile networks with low density of nodes, the potential prediction capability of OLSR-HBP is around 80% when a TP message has already appeared in the past. This percentage falls to 50% when considering a mobile network with a higher node density. This analysis better explained that just a few messages contribute significantly to reducing the total percentage of TP messages delivered through the network. This represents a good opportunity to predict TP information, and this prediction can just be focused on a small subset of messages. The implementation of the OLSR-HBP allows for the prediction of between 86% and 92% of the TP messages in static scenarios, and between 40% and 55% of them in high density mobile scenarios; which confirms the previous results. The network performance, in terms of throughput and latency, was similar to the original OLSR routing protocol, with \(-\)1.2% average throughput variations and \(-\)2.8% average latency variations. This means that the execution of the predictor did not affect the network operation. The resources used by OLSR-HBP, in terms of energy and memory, were also evaluated in this study. The obtained results indicate that memory requirements of OLSR-HBP can be considered minimal, by taking into consideration the amount of memory resources available on current mobile devices. These low memory requirements confirm the suitability of OLSR-HBP to be implemented in mobile devices that act as communication nodes in mobile collaborative systems, like those supporting emergency responses. In summary, the use of OLSR-HBP significantly decreases the signalling overhead, without disturbing the network operation, and requiring a small and affordable amount of resources. This prediction approach is deterministic and requires less computing resources than statistical approaches, such as machine or deep learning. The next steps in this research effort will be focused on analysing the performance of this history-based prediction approach in ad hoc and opportunistic networks, and thus accurately establish the limits of this proposal. This further analysis will allow developers of mobile ad hoc systems and designers of routing protocols to understand how to take advantage of this prediction approach. The use of weighted functions to manage large historical data windows will be one of the approaches to be explored and analysed in detail. **Author Contributions:** Conceptualization, R.M. and C.M. ; methodology, R.M., P.M. and C.M. ; software, R.M. and P.M.; validation, C.A., R.M., P.M., C.M., S.F.O. and R.M.S. ; investigation, R.M., P.M., C.M. and S.F.O. ; writing--original draft preparation, R.M., P.M., C.M. and S.F.O. ; writing--review and editing, C.A., R.M., P.M., C.M., S.F.O. and R.M.S.