Adaptive HELLO interval based on neural network and multicriteria for UANET

Unmanned Aerial Vehicle Ad-hoc Network (UANET) are gaining extensive attention in fire monitoring, communication relay and other fields. Because of mobility of UAVs, network topology changes frequently. In OLSR routing protocol, each node broadcasts HELLO packets at regular intervals for link sensing and neighborhood detection. However, if the HELLO interval is too small when the node speed is slow, unnecessary traffic will appear in the network. If the HELLO interval is too large when the node speed is fast, the performance will be degraded too. This paper proposes a routing protocol that adaptively adjusts the HELLO interval. Large amount of simulation results of NS-3 is used as samples, the neural network is trained by GA-BP (Genetic Algorithm, Back Propagation) algorithm, and the chosen network performance metrics under different HELLO intervals are predicted according to the speed of nodes. The MADM (Multiple Attribute Decision Making) method is used to comprehensively evaluate these metrics and select the optimal HELLO interval. Our simulation results show that compared with the original OLSR and other schemes, the proposed scheme can achieve a large performance improvement at a very small cost.