The proliferation of mobile devices and user applications has continued to contribute to the humongous volume of data traffic in cellular networks. To surmount this challenge, service and resource providers are looking for alternative mechanisms that can successfully facilitate managing network resources in a more dynamic, predictive and distributed manner. New concepts of network architectures such as Software Defined Network (SDN) and Network Function Virtualization (NFV) have paved the way to move from static to flexible networks. They make networks more flexible (i.e. network providers capable of on-demand provisioning), easily customizable and cost effective. In this regard, network slicing is emerging as a new technology built on the concepts of SDN and NFV. It splits a network infrastructure into isolated virtual networks and allows them to manage resources allocation individually based on their requirements and characteristics. Most of the existing solutions for network slicing are computationally expensive because of the length of time they require to estimate the resources required for each isolated slice. In addition, there is no guarantee that the resource allocation is fairly shared among users in a slice. In this paper, we propose a Network Slicing Resource Management (NSRM) mechanism to assign the required resources for each slice in an LTE network, taking into consideration isolation of resources among different slices. In addition, NSRM aims to ensure isolation and fair sharing of distributed bandwidths between users belonging to the same slice. In NSRM, depending on requirements, each slice can be customized (e.g. each can have a different scheduling policy).

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information.

Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.