Conditional Training Based GM and GMOPELM Data Fusion Schemes in Wireless Sensor Networks As a key infrastructure of Internet of Things (loT), wireless sensor networks (WSN) can be utilized in a wide range of applications. The prediction based data fusion methods provide effective tools to reduce the amount of data transmissions while maintaining prediction accuracy. Recently a grey prediction model (GM) combining optimallypruned extreme learning machine (OPELM) data fusion method has been proposed and shown to have good performance. However, the existing GM OPELM method performs model training and broadcasting before each prediction, resulting in high complexity and energy consumption. In this paper the conditional training based GM (CTGM) and GMOPELM (CTGMOPELM) are proposed. By introducing an error threshold, the algorithms only perform model training when the prediction error is beyond the threshold. Compared with existing GM and GMOPELM methods, the CT GM and CTGMOPELM methods not only can achieve the higher rate of acceptable prediction and better time efficiency but also has significant reduction in the energy consumption on model training and transmissions.

Constrained StatePreserved Extreme Learning Machine Reducing the training time for neural networks is a primary focus of research in the field of machine learning. Currently, the LevenbergMarquardt (LM) method is one of the fastest backpropagation methods. A recently popular alternative to LM backpropagation is the Extreme Learning Machine (ELM), which produces a closed form optimization of a Single Layer Feed Forward Network for an initially randomized input weight matrix. In this study, we further extend the performance of an ELM by incrementally building on the state of the art, the StatePreserved ELM (SPELM), to produce a Constrained SPELM (CSPELM). To do so, we introduce a constraint, , which randomly perturbs the input weight matrix after each training cycle, providing a honing mechanism during the search for a better local optimum. We evaluated CSPELM against 13 benchmark datasets, both categorical and continuous. For 8 of the 13 benchmark datasets, CSPELM outperformed, with respect to average accuracy and RMSE, the ELM, SPELM, and LM methods. Further, the results show that in 8 of the 13 benchmark datasets used, CSPELM was the best performing model and only reached a maximum of 195.10 seconds total training time in one example. The results show a more consistent and higher accuracy than the ELM and SPELM and competitive or better results with LM with training time being only approximately 10% of traditional LM backpropagation training time.