Main Article Content
Mobility has recently sparked a lot of interest, as users' demand for more reliable connections and higher service quality has risen. In mobile networks, effective estimation of consumer mobility allows for efficient resource and handover management, preventing undesirable loss of perceived efficiency. As a result, predicting mobility in wireless networks is important, and several studies have been conducted on the subject. The importance of mobility prediction is discussed in this paper, as well as its inherent attributes in terms of predictability of the node movement, outputs of the prediction, and evaluation metrics. Furthermore, the learning perspective of mobility prediction solutions has been explored.This work outlines a similarity estimation based approach to mobility prediction. To obtain a time series of past measurements, each node tracks the Signal to Noise Ratio (SNR) of its wireless connections with the other nodes. When a prediction is requested, the node uses the collected training data to calculate the normalized cross-correlation function of the recent past in order to identify situations similar to the current one in the background of its relations. The matched records are then utilized as the prediction's basis.