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MANETs is a type of Mobile Ad Hoc Networks and it contains a set of nodes that do not need central management to communicate with each other because each node can act as a sender, receiver and router at the same time. All these nodes can perform self-configuration. The ad hoc stationary or mobile networks face many challenges such as limited bandwidth, congestion and data transfer speed. Therefore, it is extremely important to develop an intelligent routing protocol to encounter these challenges. An evaluation study is also important to perform the backoff contention window effectively in order to know the obstacles and avoid them. This paper concentrates on the performance of backoff contention window using the main routing protocols such as DSR, DSDV and AODV for MANETs networks in crowded areas with random movement. The important parameters which include network throughput …

Cooling accounts for 12-38% of total energy consumption in schools in the US, depending on the region. In this study, stacking learning is utilized to predict chiller running capacity for four school buildings (regression) and to predict the chiller status for four another schools (classification) using a collection of interval chiller data and building demand. Singular and multiple measurement periods within one or more seasons are considered. A generalized methodology for modeling building energy systems is posited that informs selection of features, data balancing to attain the best model possible, ensemble-based stacked learning in order to prevent over-fitting, and final model development based upon the results from the stacked learning. The results show that ensemble-based stacked learning improves the model performance substantially; providing the most accurate results for both regression and classification. for both classification and regression. For, classification, the balanced accuracy is 99.79% while Kappa is 99.39%. For regression, the R-squared value, the mean absolute error (MAE) error, and the root mean squared error (RMSE) are 1.78 kW, 2.77 kW, and 0.983 respectively.