Electric vehicles represent a global endeavor towards environmentally friendly transportation, and such a green transition is promoted worldwide, being deployed in many regions nowadays. As the number of electric vehicles has been increasing rapidly for more than a decade, how to meet the need for charging their batteries appears as an important research topic, having received remarkable attention in both industry and research community. Uncoordinated charging of many electric vehicles may lead to congestion at charging stations and unbalanced load of the power supply grid. To address this problem, optimized charging schemes which consider available energy resources and user requirements are required. This paper offers an overview of state-of-the-art charging solutions covering two main categories of approaches, namely, centralized and decentralized charging. In addition to addressing the potential challenges that arise in charging schedule optimization, we cover various optimization techniques that have been proposed for optimizing charging schedules. Furthermore, this paper analyzes the current solutions and identifies their limitations and gaps. Open research issues are identified and several potential research topics are suggested.

This paper focuses on the performance evaluation of consensus algorithms used in a blockchain system for Internet of Things (IoT). In such systems the time necessary to achieve consensus should be small. Three most used consensus algorithms (modified proof of work, practical byzantine fault tolerance and binary consensus) are evaluated in different situations - type of motes, number of nodes participating in consensus algorithm and radio propagation model. An integrated solution is proposed to adapt an IoT node to different consensus algorithm. The simulations in Contiki IoT operating system show good performance (time to achieve consensus less than seconds).

Identification of ecosystems and Business Models (BM) is an important starting point for new complex system development. The definition of actor (or stakeholder) roles and their interactions (at both business and technical levels), together with target scenarios and use cases, provide essential input information for further system requirement collection and architecture specification. The powerful and flexible Fifth Generation (5G) network slicing technology, which is capable of creating virtually isolated and logically parallel networks, enables a large range of complex services and vertical applications. Although various terminologies and models have been proposed in recent years for BMs in the 5G domain by many studies, projects, standards, and technical specifications from dedicated organizations, they are not always consistent with each other. This study presents an overview and comparison of different BMs for 5G sliced systems, followed by an example on BM definition for a 5G system in a novel ongoing European research project. While a general ecosystem and business model could involve a large range of organizations (including, e.g., regulation and standardization bodies), the scope of this article will be limited to primarily 5G operational BMs, with a focus on those actors or stakeholders who are active and interacting during real-life system operation. Within the project, we perform a selection among some tailored BM configurations, adapted for dedicated slices with different service requirements, aiming to serve Vehicle to Everything (V2X) and Internet of Vehicles (IoV) applications as well as Internet of Things (IoT) for maritime vertical applications. The final part of this article presents our proposed IoT connectivity solutions for various maritime scenarios with and without involving satellite links. Furthermore, we shed light on future challenges and directions for network slicing in beyond 5G systems.

To enable emerging applications of fifth generation (5G) systems for vertical scenarios like mobile broadband and Internet of things (IoT) for vehicles and maritime sectors, a new trend towards softwarization and establishment of private networks is gaining momentum. Different from earlier generations of mobile systems where network functions are traditionally operated based on dedicated hardware, 5G infrastructures can be virtualized and services can be provided based on software, common hardware and servers. In this paper, we present an open-source based prototype private network which has been implemented in the framework of an EEA research project SOLID-B5G. The prototype network is a 5G non-standalone (NSA) network with a fourth generation (4G) evolved packet core (EPC) connecting both evolved NodeB (eNB) and next generation NodeB (gNB) to commercial off-the-shelf (COTS) user equipment (UE). The prototype is implemented based on two popular open-source software platforms, namely OAI RAN and srsRAN. Based on our implementations, extensive experiments have been conducted to assess the performance of the prototype network in terms of downlink/uplink throughput and latency between different parts of the system.

In the coming years, autonomous driving will be the primary focus of the automobile industry. The great majority of accidents are caused by human mistakes, and autonomous cars can help to lower this number significantly, thus improving road safety. Object identification plays a critical part in autonomous vehicle driving, and deep learning techniques are used to implement it. YOLO is one of the most common methods for recognizing and identifying things that emerge on the road. Its popularity has developed as a result of its superior performance in terms of speed, high accuracy, and learning capabilities when compared to other object recognition approaches such as Retina-Net, fast R- CNN, and Single-Shot MultiBox Detection (SSD).