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The Internet of Things (IoT) is a broad concept that encompasses a variety of technologies and applications. As a result, having a comprehensive understanding of the future developments and deployment of this concept in various lateral and vertical markets and following future trend projections is critical. In this way, future studies might focus on numerous issues in areas where the Internet of Things is most widely used. This study focuses on the smart home setting, one of the most prevalent and fastest-growing applications of the Internet of Things idea. This field's current and future growth tendencies have been proven in many categories and capabilities of IoT devices such as comfort and lighting, energy, security, monitoring, media, and household appliances. The number of devices in use, houses with at least one IoT device, total revenue, and market value are increasing. With the positive growth trend of the smart home, the concept of cybersecurity is becoming one of the most critical issues whose overview we included in this research. Research results indicate several cybersecurity challenges raised due to the fast-growing trend of smart home concept that needs to be addressed in the future to prevent users' privacy and usability of various end devices in such environment. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar. [ABSTRACT FROM AUTHOR]

The problem of detecting anomalies in network traffic caused by the distributed denial of service (DDoS) attack so far has mainly been investigated in terms of detection of illegitimate DDoS traffic generated by conventional terminal devices (PCs, laptops, mobile devices, tablets, servers). Technological development has resulted in the emergence of the Internet of Things (IoT) concept, whose implementation implies numerous terminal devices with a low level of implemented protection. The large growth and prediction of future growth is noticeable in the environment of a smart home and smart office. IoT devices in such environments are increasingly being used as a platform for generating DDoS traffic due to its numeracy and low level of protection. The aim of this research is to propose a novel approach for detection of DDoS traffic generated by IoT devices in a form of conceptual network anomaly detection model. Proposed conceptual model is based on device classes which are dependent on individual device traffic characteristics. [ABSTRACT FROM AUTHOR]

Movement and informing visually impaired persons is difficult because mobile application solutions and services are unable to work in indoors environments (location based service using GPS technology). Main precondition for active participation in the daily living and needs of visually impaired persons is increased degree of mobility. The aim of this research is to increase the quality of life of visually impaired persons and the degree of mobility in indoor environments by applying modern information and communication technologies (Cloud Computing, Fog, IoT, AAL /ELE platform). User requirement will be used to define functionalities of service for informing visually impaired persons in the example of large retail chains. A conceptual system architecture model will be proposed for providing information service with the aim to provide real-time information to users. This research will also show simulation testing of the proposed architecture with Arduino Uno and Raspberry Pi 3 platforms for collecting information and informing end users. [ABSTRACT FROM AUTHOR]