Search

The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments. [ABSTRACT FROM AUTHOR]

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively. [ABSTRACT FROM AUTHOR]