Modern society is increasingly permeated by realities parallel to the real one. The so-called virtual reality is now part of both current habits and many activities carried out during the day. Virtual reality (VR) is, in turn, related to the concept of augmented reality (AR). It represents a technology still in solid expansion but which was created and imagined several decades ago. This paper presents an overview of augmented reality, starting from its conception, passing through its main applications, and providing essential information. Part of the article will be devoted to hardware and software components used in AR systems. The last part of the paper highlights the limitations related to the design of these systems, the shortcomings in this area, and the possible future fields of application of this extraordinary technological innovation.

Six years after the adoption of the standard 4, the Bluetooth Special Interest Group (SIG) has officially released the main features of Bluetooth 5. It is a major development in short-range wireless communication technology. As stated by the SIG, the new standard will change the way people approach the Internet of Things (IoT) significantly. In this article, the future IoT scenarios and use cases that justify the push for Bluetooth 5 are introduced. A set of new technical features that are included in Bluetooth 5.0 are presented, and their advantages and drawbacks are described.

Smart grids are an evolution of the existing electric distribution systems due to the growing demand of energy, the expansion in the use of renewable energy sources, and the development of novel and innovative information and communication technologies (ICT). The installation of systems based on wireless networks can play a key role in the extension of the smart grid toward smart homes, that can be deemed as one of the most important components of smart grids. In fact, monitoring and control applications, energy harvesting, and innovative metering methodologies through smart wireless devices are becoming increasingly important. This paper proposes a novel energy management approach for smart homes that combines a wireless network, based on bluetooth low energy (BLE), for communication among home appliances, with a home energy management (HEM) scheme. The proposed approach addresses the impact of standby appliances and high-power rating loads in peak hours to the energy consumption charges of consumers. Simulation results show that the proposed approach is efficient in terms of reducing peak load demand and electricity consumption charges with an increase in the comfort level of consumers.

With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance.

Mobile network operators (MNOs) allocate computing and caching resources for mobile users by deploying a central control system. Existing studies mainly use programming and heuristic methods to solve the resource allocation problem, which ignores the energy cost problem that is really significant to the MNO. To solve this problem, in this article, we design a joint computing and caching framework by integrating deep deterministic policy gradient (DDPG) algorithm. Especially, we focus on the Internet of Vehicles scenario, which needs the support of mobile network provided by MNO. We first formulate an optimization problem to minimize MNO’s energy cost by considering the computation and caching energy costs jointly. Then, we turn the formulated problem into a reinforcement learning problem and utilize DDPG methods to solve this problem. The final simulation result shows that our solution can reduce energy costs by more than 15%, while ensuring the tasks can be completed on time.