Long Short-Term Memory Recurrent Neural Network based Mobility Prediction in MANET
Keywords:ARIMA, LSTM, MANET, Mobility Prediction.
A Mobile Ad hoc NETwork (MANET) is formed by a gathering of wireless nodes without any infrastructure and the wireless nodes are not static but dynamic in nature. The mobile nodes can be connected together dynamically to route data packets from other mobile node present in the network. The mobility of the nodes is a significant feature in the MANET. The nodes follow one of the different mobility patterns which affect the connectivity among the other nodes. The dynamic changes in the connectivity decides the performance of the routing protocol and hence the performance of the overall network. When considering these characteristics of the MANET it becomes essential to predict the mobility of the nodes in both service-oriented and as well as application-oriented aspects of the dynamic networking. At the network level the mobility prediction is useful in pre-configuring services, provisioning of QoS and reservation of net-work resources and whereas in application level the end user can be provisioned with route guidance, information on network traffic and broadcasting important information to users. The mobility prediction will lead to the estimation of the stability of the routing paths and the distances between the neighboring nodes. This helps to improve the overall performance of the network by reducing the frequent link failures and increasing packet delivery ratio. In this paper, a novel mobility prediction method is developed using the historical time and positioning data. Auto Regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) with Recurrent Neural Network (RNN) are utilized for mobility prediction based on the sequence of time series data.
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