The unprecedented growth of today’s cities together with increased population mobility are fueling the avalanche in the numbers of vehicles on the roads. This development led to the new challenges for the traffic management, including the mitigation of road congestion, accidents, and air pollution. Over the last decade, researchers have been focusing their efforts on leveraging the recent advances in sensing, communications, and dynamic adaptive technologies to prepare the deployed road traffic management systems (TMS) for resolving these important challenges in future smart cities. However, the existing solutions may still be insufficient to construct a reliable and secure TMS that is capable of handling the anticipated influx of the population and vehicles in urban areas. Along these lines, this work systematically outlines a perspective on a novel modular environment for traffic modeling, which allows to recreate the examined road networks in their full resemblance. Our developed solution is targeted to incorporate the progress in the Internet of Things (IoT) technologies, where low-power, embedded devices integrate as part of a next-generation TMS. To mimic the real traffic conditions, we recreated and evaluated a practical traffic scenario built after a complex road intersection within a large European city.

The Internet of Things (IoT), as expected infrastructure for envisioned concept of Smart City, brings new possibilities for the city management. IoT vision introduces promising and economical solutions for massive data collection and its analysis which can be applied in many domains and so make them operating more efficiently. In this paper, we are discussing one of the most challenging issues - municipal waste-collection within the Smart City. To optimize the logistic procedure of waste collection, we use own genetic algorithm implementation. The presented solution provides calculation of more efficient garbage-truck routes. As an output, we provide a set of simulations focused on mentioned area. All our algorithms are implemented within the integrated simulation framework which is developed as an open source solution with respect to future modifications.

As LoRaWAN is one of the most popular long-range wireless protocols among low-power IoT applications, more and more focus is shifting towards security. In particular, physical layer topics become relevant to improve the security of LoRaWAN nodes, which are often limited in terms of computational power and communication resources. To this end, e.g., detection methods for wireless attacks improve the integrity and robustness of LoRaWAN access. Further, wireless physical layer techniques have potential to enhance key refreshment and device authentication. In this work, we aim to provide a comprehensive review of various vulnerabilities, countermeasures and security enhancing features concerning the LoRaWAN physical layer. Afterwards, we discuss the impact of the reviewed topics on LoRaWAN security and, subsequently, we identify research gaps as well as promising future research directions.

In recent years, the Industry 4.0 paradigm has accelerated the digitalization process of the industry, and it slowly diminishes the line between information technologies (IT) and operational technologies (OT). Among the advantages, this brings up the convergence issue between IT and OT, especially in the cybersecurity-related topics, including new attack vectors, threats, security imperfections, and much more. This cause raised new topics for methods focused on protecting the industrial infrastructure, including monitoring and detection systems, which should help overcome these new challenges. However, those methods require high quality and a large number of datasets with different conditions to adapt to the specific systems effectively. Unfortunately, revealing field factory setups and infrastructure would be costly and challenging due to the privacy and sensitivity causes. From the lack of data emerges the new topic of industrial testbeds, including sub-real physical laboratory environments, virtual factories, honeynets, honeypots, and other areas, which helps to deliver sufficient datasets for mentioned research and development. This paper summarizes related works in the area of industrial testbeds. Moreover, it describes best practices and lessons learned for assembling physical, simulated, virtual, and hybrid testbeds. Additionally, a comparison of the essential parameters of those testbeds is presented. Finally, the findings and provided information reveal research and development challenges, which must be surpassed.