Pattern matching based link quality prediction in wireless mobile ad hoc networks

As mobile devices are getting more ubiquitous, the paradigm of wireless mobile ad hoc networks (MANETs) is gaining popularity. However, MANETs impose new challenges because of their self-organizing, mobile and error-prone nature. Mobility prediction can mitigate the problems emerging from node mobility.In this paper, we propose an approach called XCoPred to predict link quality variations based on pattern matching which can be exploited for mobility prediction. XCoPred doesn't require the use of any external hardware or reference point. Each MANET node monitors the Signal to Noise Ratio (SNR) of its links to obtain a time series of SNR measurements. When a prediction is required, the node tries to detect patterns similar to the current situation in the history of the SNR values of its links by applying the normalized cross-correlation function. The found matches are then used as the base of the prediction. Simulations have shown that fairly accurate predictions around $2~dB$ of absolute average prediction error can be achieved with XCoPred in case of appropriate parameter settings and scenarios showing clear node mobility patterns.