The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed.

The Internet of vehicles (IoV) is increasingly being used to realize the vision of intelligent transportation systems with the rapid development of computation and communication technologies. However, numerous IoV applications rely on a central unit for storing and processing information and mediators for wireless transmission. This can lead to the leakage of sensitive data and high costs and delays. To address these issues and improve the efficiency of data storage, processing, and sharing in the IoV, we propose a vehicle-based secure blockchain consensus (VBSBC) algorithm. Our VBSBC algorithm overcomes the limitations and drawbacks of state-of-the-art approaches by leveraging blockchain technology and a consensus algorithm to ensure secure communication between vehicles. In addition, the algorithm includes an authentication process and a key distributing and request process, illustrated during vehicles' movement between different zones. In simulation results, our proposed VBSBC algorithm demonstrated high performance compared to existing approaches in terms of authentication delay, key processing time, attack detection rate, throughput, and packet loss rate.

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology - underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions.

New government regulations and incentives promote the deployment of commercial electric vehicles to reduce carbon emissions from gasoline-fueled vehicles. For commercial electric vehicles (CEVs) operating in a fleet, charging processes are often performed at the depot where they begin and end their daily driving cycles, as well as at public stations on their routes. With the large penetration of CEVs in depots, simultaneous charging increases peak demand, which in turn impacts the electric network and increases the demand cost of a facility. These depot charging conditions influence the charging schedules of CEVs along their routes and the total service cost of logistic companies. This paper investigates optimal charging problems for CEVs that are supported by charging stations at depot and on-route public charging stations. The optimal charging and routing problems of CEVs are modelled as an optimization problem and relevant solutions are provided. The charging variants considered in the optimization model are peak demand of depot charging, time of use tariffs during the day, partial recharging, waiting times and characteristics of public stations. The results indicate the effectiveness of the developed algorithm in achieving optimal routes that maximize the benefits of logistics companies provided all constraints are satisfied.

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

While the concept of ultra-dense small cell networks (SCNs) has brought a number of significant opportunities for the telecommunication industry, it has also introduced a major challenge for researchers, who must develop techniques to reduce the sharp increase in power consumption that will be required to backhaul traffic from SCNs to the core network. In this research, we investigate the green backhauling challenge for a fifth generation (5G) wireless communication network that uses both the passive optical network (PON) and millimeter wave (mmWave) backhauling. Our approach is based on the fact that the energy efficiency figures for the PON and the mmWave technologies are different under a given load condition. The PON technology is more energy efficient under heavy load conditions, whereas the mmWave technology offers better energy efficiency under low load conditions. We formulate an optimization problem that considers the estimated hourly traffic load and determines the most energy efficient backhauling strategy for various hours of the day. Considering the complexity of the optimization problem, we also propose an energy efficient heuristic solution to solve this problem. Simulation results indicate that the proposed solution provides up to 32% more energy savings than the existing solution.