In this paper, we propose a speed prediction model using auto-regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time-series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto-correlation function (ACF), and partial auto-correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.
SummaryVehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance‐based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition‐based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.
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