Home automation systems have attracted considerable attention with the advancement of communications technology. A smart home (SH) is an Internet of Things (IoT) application that utilizes the Internet to monitor and control appliances using a home automation system. Lack of IoT technology usage, unfriendly user interface, limited wireless transmission range, and high costs are the limitations of existing home automation systems. Therefore, this study presents a cost-effective and hybrid (local and remote) IoT-based home automation system with a user-friendly interface for smartphones and laptops. A prototype called IoT@HoMe is developed with an algorithm to enable the monitoring of home conditions and automate the control of home appliances over the Internet anytime and anywhere. This system utilizes a node microcontroller unit (NodeMCU) as a Wi-Fi-based gateway to connect different sensors and updates their data to Adafruit IO cloud server. The collected data from several sensors (radio-frequency identification, ultrasonic, temperature, humidity, gas, and motion sensors) can be accessed via If This Then That (IFTTT) on users’ devices (smartphones and/or laptops) over the Internet regardless of their location. A set of relays is used to connect the NodeMCU to homes under controlled appliances. The designed system is structured in a portable manner as a control box that can be attached for monitoring and controlling a real house. The proposed IoT-based system for home automation can easily and efficiently control appliances over the Internet and support home safety with autonomous operation. IoT@HoMe is a low cost and reliable automation system that reduces energy consumption and can notably provide convenience, safety, and security for SH residents.

The promise of low latency connectivity and efficient bandwidth utilization has driven the recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC). This paper presents an advanced deep learning-based computational offloading algorithm for multilevel vehicular edge-cloud computing networks. To conserve energy and guarantee the efficient utilization of shared resources among multiple vehicles, an integration model of computational offloading, and resource allocation is formulated as a binary optimization problem to minimize the total cost of the entire system in terms of time and energy. However, this problem is considered NP-hard and it is computationally prohibitive to solve this type of problem, particularly for large-scale vehicles, due to the curse-of-dimensionality problem. Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep neural networks are used in parallel. Finally, simulation results show that the proposed algorithm can exhibit fast convergence and significantly reduce the overall consumption of an entire system compared to the benchmark solutions.

Daily traffic accidents increase annually, causing a significant number of death and disability cases. Most of fatalities occur because of the late response to these emergency cases. The time after the traumatic injury is called the golden hour, where providing essential medical and surgical aid at that time increases the probability of saving human lives by one-third an average. Thus, the focus of this paper was to develop a system based on IoT for accident detection and classification. The system detects and classifies vehicle accidents based on severity level and reports the essential information about the accident to emergency services providers. The system consists of a microcontroller, GPS, and a group of sensors to determine different physical parameters related to vehicle motion. In addition, different types of machine learning classifiers were examined with the developed system to determine the most accurate classifier for the system. The classifiers are the Gaussian Mixture Model (GMM), Naive-Bayes Tree (NB), Decision Tree (DT), and Classification and Regression Trees (CART). The implementation of the system showed that GMM and CART models were better in terms of precision and recall. It was also shown that the severity of accidents depends mainly on the g-force value and fire occurrence.

Smart cities development relies on information and communication technologies (ICTs) to improve all urban aspects, including governance, economy, mobility, and environment. The development is usually associated with several challenges and negative effects. This study relies on revealing ICTs challenges by firstly conducting a comprehensive literature review to identify the challenges that are most associated with ICTs. Then, a questionnaire survey was distributed among the Saudi population to study their expectations, perceptions, and concerns on the smart city concept and services. The questionnaire also investigated ICTs challenges identified from the literature review, including information security risks, privacy violation, incompatibility, and digital skill gaps. Consequently, semi-structured interviews were conducted to perceive the reasons for the incompatibility between different systems and digital skill gaps between the public. The findings show that the most likely challenges are information security risks and privacy violations, which are due to the increase in vulnerability, potential attacks, and lack of public awareness regarding personal data protection. The incompatibility between different systems and services in smart cities arouses worries among the public due to the expected high cost and difficulty of adaptation and utilization. Moreover, digital skill gaps arises between members of the population that have a low education level or are elderly persons.

In the last few years, most of the data such as books, videos, pictures, medical and even the genetic information of humans are moving toward digital formats. Laptops, tablets, smartphones and wearable devices are the major source of this digital data transformation and are becoming the core part of our daily life. As a result of this transformation, we are becoming the soft target of various types of cybercrimes. Digital forensic investigation provides the way to recover lost or purposefully deleted or hidden files from a suspect’s device. However, current man power and government resources are not enough to investigate the cybercrimes. Unfortunately, existing digital investigation procedures and practices require huge interaction with humans; as a result it slows down the process with the pace digital crimes are committed. Machine learning (ML) is the branch of science that has governs from the field of AI. This advance technology uses the explicit programming to depict the human-like behaviour. Machine learning combined with automation in digital investigation process at different stages of investigation has significant potential to aid digital investigators. This chapter aims at providing the research in machine learning-based digital forensic investigation, identifies the gaps, addresses the challenges and open issues in this field.