Transfer learning is widely used as a solution to overcome the huge amount of time required to train a deep learning model. However, a number of challenges are associated with transferring the knowledge between the same and different Target Domains (TDs). For instance, designing efficient mapping functions, correlating the source and target datasets, identifying necessary parameters for enabling transfer learning, etc. In this article, we, therefore, present a solution to enable transfer learning for Home Energy Management Systems (HEMS) in smart homes consisting of multiple residents and appliances. This led to eliminating the training time and data requirements associated with training home appliances. A mapping function is designed based on correlating the source and target datasets before transferring the knowledge. Further, a training model is designed to iteratively train a source model i.e., Expert Home (EH) and Expert Appliance (EA), with source dataset and feedback knowledge from the target model i.e., Learner Home (LH) and Leaner Appliance (LA). An extensive set of simulations is carried out to evaluate the performance of the proposed scheme. Simulation results show that transferring knowledge in both the same and different domains significantly reduces the energy consumption of individual home appliances and smart homes. In the future, we will expand the research using new models to promote transfer learning effectiveness.

In heterogeneous networks (HetNets), the vertical handover (VHO) is an essential process for mobile users (MUs) aiming to secure ubiquitous connectivity and maintain the highest quality of service (QoS) across various types of radio access technology (RAT), such as wireless fidelity (Wi-Fi), the global system for mobile communication (GSM), and the universal mobile telecommunications system (UMTS). In the literature, many recent VHO research works have been proposed in which a number of critical issues still arise when performing seamless vertical handover, including VHO packet loss, VHO delay, and VHO throughput. Moreover, the security aspect of triggering VHO has not been carefully considered, particularly when an MU intends to use a payment application. In this paper, we present a comprehensive performance evaluation for a new secure VHO algorithm, taking into account all the above issues, for co-located Wi-Fi and UMTS networks. The simulation results are compared with the media independent handover (MIH) IEEE 802.21 standard, which facilitates the seamless transfer of connection among heterogeneous networks. We show that the proposed algorithm outperforms the MIH standard in terms of VHO delay, VHO packet loss, and VHO throughput in providing uninterrupted connection to security-constrained applications.

Due to the rapid advancement of social media, a huge amount of data is generated daily. Due to this great spread and expansion of the data at the social or professional level, the risks of securing the information become a challenging job. In this regard, we conducted an in-depth interview to gather specific information about how infected users may be provided with information about recovering their hacked social networking accounts. Further, we have introduced a complete solution to help social network users to improve the idea of using different applications from one appropriate platform. In order to build this secure platform for accessing the security applications such as bank accounts, etc., we set various security methods to access social network websites, such as sending an OTP to their respective mobile devices, email or by fingerprint. Further, we also added a camera to identify the wrong or fake registration process of an intruder. The camera captures an image of the intruder registering to a social network website using the legitimate user’s information. In addition, the application also has a solution for forgetting the password or security questions that are sent to the user via email. Finally, the application saves the password, which can be recovered when the user forgets it.