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.

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Internet of Things (IoT) is expected to become a driver in an emerging era of interconnected world through the advanced connectivity of smart devices, systems, and services. IoT goes beyond a broad range of Machine-to-Machine (M2M) communication technologies and covers a wide variety of networking protocols. There exist solutions like MQTT or SIP collecting data from sensors, CoAP for constrained devices and networks, or XMPP for interconnecting devices and people. Also there is a plethora of standards and frameworks (OSGi, AllJoyn) bringing closer the paradigm of IoT vision. However, the main constraint of most existing platforms is their limited mutual interoperability. To this end, we provide a comprehensive description of protocols suitable to support the IoT vision. Further, we advocate an alternative approach to already known principles and employ the SIP protocol as a container for M2M data. We provide description of data structures and practical implementation principles of the proposed structures (JSON and Protocol Buffers are discussed in detail) transmitted by SIP as a promising enabler for efficient M2M communication in the IoT world. Our reported findings are based on extensive hands-on experience collected after the development of advanced M2M smart home gateway in cooperation with the operator Telekom Austria Group.

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In recent years,many efforts have been devoted to image segmentation. Although for a man general image segmentation is considered an easy task, for computers it is still considered to be difficult, computationally intensive and still unresolved task. This work presents an innovative algorithm combining theory of artificial intelligence and knowledge of human eye anatomy. The resulting algorithm has not ambitions to be universal like human brain but can be trained and perform on selected domain. The effectiveness of the algorithm is demonstrated on the selected examples.

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