Research on grey model-based node trajectory prediction in WBAN

AbstractIn wireless body area networks WBANs, node trajectory prediction is the basis of routing, power controlling, lifetime prolonging and connectivity maintaining. The grey model is adopted and improved in node trajectory prediction in this paper. A novel variable weight buffer for the grey model is introduced to solve the problem of inconsistency between qualitative analysis and quantitative calculation. The relationship between variable weight and its regulation degree is then discussed. Furthermore, an input data feature-based self-adaptive strategy is proposed to address the restrictions of nodes' calculation capacity and limited storage space. This feature allows the user to determine the variable weight automatically. The proposed algorithm holds the capability to identify high-quality forecasting over the database of MSR Daily Activity 3D, which is captured by Microsoft Research and contains 16 daily activities, and it outperforms existing methods significantly in terms of effectiveness and adaptability.