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 …

 Cognitive radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use. Upon the result of the detection, the system instantly moves into vacant channels while avoiding occupied ones. To improve the capacity of CR systems, the unused sub-channels by primary user (PU) could be aggregated. However, when secondary user (SU) system aggregates more channels, more collisions may occur. This collision occurs due to the PU backto reuse its channels during SU transmission on them. Moreover, there are other reasons for collisions as misdetection which happens when wrongly detected absence of PU may occur as well.

In this paper a Proposed Channel Aggregation Algorithm is introduced in which the CR data rate is maximized by aggregating multiple channels for the SU while satisfying the minimum collision requirements. Moreover, channel capacity is maximized under collision constraints. Matlab simulation is conducted to implement the proposed algorithm and the rustles showed that, very low collision probability was observed as compared to previous work. The capacity of CR was also improved as well.

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.

Traditional building energy audits are both expensive, in the range of USD $1.29/m 2-$5.37/m 2, and inconsistent in their prediction of potential energy savings. Automation to reduce costs of evaluating the energy effectiveness of buildings is strongly needed. A key element of such automation is a means to estimate the building envelope energy effectiveness. We present a method that addresses this need by using infrared thermography to characterize building wall envelope effectiveness. To date, thermal imaging approaches for estimating wall R-Values, based upon thermal-physical models of walls, require additional manual measurements and analysis which prohibit low-cost, large-scale implementation. To overcome this implementation challenge, a machine learning approach is used to predict wall R-Values for a set of residences with known thermal resistance by utilizing the measured wall imaging temperature, prior weather conditions, historical energy consumption data, and available building geometrical data. The developed model is shown to predict wall R-Values with a maximum test-set root mean squared error of 7% using as few as nine training houses. This result has significant implications for low-cost large-scale envelope energy effectiveness characterization.