Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). The introduction of Reinforcement Learning (RL) in ATSC as tackled those types of congestions by using on-line learning, which is based on the trial and error approach. Furthermore, RL is prone to the dimensionality curse related to the state–action space size based on which a non-linear quality function is derived. The Deep Reinforcement Learning (DRL) framework uses Deep Neural Networks (DNN) to digest raw traffic data to approximate the quality function of RL. This paper provides a comprehensive analysis of the most recent DRL approaches used for the ATSC algorithm design. Special emphasis is set to overview of the traffic state representation and multi-agent DRL frameworks applied for the large traffic networks. Best practices are provided for choosing the adequate DRL model, hyper-parameters tuning, and model architecture design. Finally, this paper provides a discussion about the importance of the open traffic data concept for the extensive application of DRL in the real world ATSC.
The concept of Internet of Things (IoT) is based on a layered architecture. Each of the layers includes the application of a range of diverse technologies for the data transmission, processing and storage. This paper will explore the vulnerabilities and threats in IoT environment and protection methods that can be implemented within such an environment due to the hardware limitations of the existing equipment and technology used for data transfer. Based on the results of the research, classification of security risks of the architectures' particular layer, as well as security risks depending on the type of use of IoT concept will be proposed. The classification of risk will provide the opportunity to direct further research on the most vulnerable layers of the architecture and implementation of appropriate methods of protection, depending on the application of this concept. This research was conducted in order to provide accurate information for visually impaired people.
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