Residual Link Lifetime Prediction with Limited Information Input in Mobile Ad Hoc Networks

We study the problem of predicting the residual link lifetime (RLL) in MANETs, where the nodes are able to measure the relative distances between them (e.g., by using the UWB technology). We propose a mobile-projected trajectory (MPT) algorithm, whose input is periodically sampled, noisy range measurements between the two nodes of a link. It estimates a projected trajectory, which is then used to compute the predicted RLL. An enhancement technique, which we call incremental sampling, is proposed where the estimated trajectory is further refined to improve the accuracy of the RLL prediction. We have evaluated the performance of the MPT algorithm with two different mobility models and for different parameters, and have shown that MPT yields robust performance; i.e., the main strength of the MPT algorithm lies in its capability to accurately predict the RLL with limited range input data. For example, after a measurement-acquisition time equal to 25% of the link lifetime, the algorithm yields 90% prediction accuracy; 80% accuracy is achieved after 20% of the link lifetime. After only 15% of link lifetime, the algorithm still achieves 60% prediction accuracy.