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Video content and streaming services have become integral to modern networks, driving increases in data traffic and necessitating effective methods for evaluating Quality of Experience (QoE). Accurately measuring QoE is critical for ensuring user satisfaction in multimedia applications. In this study, an optimized adaptive neuro-fuzzy inference model that leverages subtractive clustering for high frame rate video quality assessment is presented. The model was developed and validated using the publicly available LIVE-YT-HFR dataset, which comprises 480 high-frame-rate video sequences and quality ratings provided by 85 subjects. The subtractive clustering parameters were optimized to strike a balance between model complexity and predictive accuracy. A targeted evaluation against the LIVE-YT-HFR subjective ratings yielded a root mean squared error of 2.9091, a Pearson correlation of 0.9174, and a Spearman rank-order correlation of 0.9048, underscoring the model’s superior accuracy compared to existing methods.

Modern traffic-monitoring systems increasingly rely on supplemental analytical data to complement video recordings, yet such data are rarely integrated into video containers without altering the original footage. This paper proposes a lightweight audio-based approach for embedding road-condition information using a Least Significant Bit (LSB) steganography framework. The method operates by serializing sensor data, encoding it into the LSB positions of synthetically generated audio, and subsequently compressing the audio track while preserving imperceptibility and video integrity. A series of controlled experiments evaluates how waveform type, sampling rate, amplitude, and frequency influence the storage efficiency and quality of WAV and FLAC stego-audio files. Additional tests examine the impact of embedding capacity and output-quality settings on compression behavior. Results reveal clear trade-offs between audio quality, data capacity, and file size, demonstrating that the proposed framework enables efficient, secure, and scalable integration of metadata into surveillance recordings. The findings establish practical guidelines for deploying LSB-based audio embedding in real traffic-monitoring environments.

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.

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.

The concept of IoT (Internet of Things) assumes a continuous increase in the number of devices, which raises the problem of classifying them for different purposes. Based on their semantic characteristics, meaning, functionality or domain of usage, the system classes have been identified so far. This research purpose is to identify devices classes based on traffic flow characteristics such as the coefficient of variation of the received and sent data ratio. Such specified classes can combine devices based on behavior predictability and can serve as the basis for the creation of network management or network anomaly detection classification models. Four generic classes of IoT devices where defined by using the classification of the coefficient of variation method.

The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user’s preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets. This paper defines the taxonomy of communication technologies, devices, and sensors in the environment of the user’s smart home and shows the interaction of all elements in the environment of the smart home. Through the integration of assistive technologies, it is possible to adapt the home to users with diverse types of disabilities and needs. The smart home environment with diverse types of sensors whose data are part of sets defined by a mathematical model is also evaluated. The significance of establishing data sets as a foundation for future research, the development of ML models, and the utilization of AI is highlighted in this paper.

The everyday needs of people with disabilities represent a challenge in designing new and innovative services. The capabilities provided by modern communication technologies designed by assistive technology models can help people with disabilities perform their work tasks. The user environment has to be adapted to the guidelines of the universal design and services should be available regardless of user requirements. This paper proposes a conceptual model for informing the people with disabilities regardless of the disability degree using a smart wearable device (smart wristband). All relevant information is integrated through the warehouse management system to efficiently perform all the appropriate processes. Activity of informing users is viewed from multiple scenarios, the work environment and work tasks. For the purpose of real-time informing, industrial internet of things environment and appropriate sensors were used. The presented model aims at raising the quality of life for the people with disabilities in the work environment and rising business efficiency based on industry 4.0 concept.

Assistive technologies nowadays have an increasing application in overcoming the everyday needs of visually impaired persons. Going to the grocery store for groceries is a challenge for any visually impaired person who moves without a personal assistant. Creating a perception of the space in which the person is located and the possibility of orientation in such an environment is significantly reduced. The solutions and services currently available are based solely on reading products using some form of assistive technology. In this research, user needs were analyzed to define the necessary information to master the shopping process. The research aims to define a conceptual system architecture to deliver InnIoTShop information service to users moving through smart stores. The proposed architecture is based on the IoT concept, and an InnIoTShop service taxonomy has been created for this service. For data collection, processing, and fusion, the concept of Fog/Cloud Computing and M2M and M2H technology was used, which enables the adaptation of information to visually impaired persons. The paper also validated specific elements of the system architecture, which confirmed individual service functionalities' operation.

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.

The number of people who use the Internet daily is steadily increasing. It makes daily chores easier and faster to do, but it also increases the danger of cyberattacks. Web-based solutions are frequently used to connect with manufacturing process monitoring, management, and supply chain communication in contemporary manufacturing systems and under Industry 4.0. Cross-Site Scripting assaults are one of the most widespread cyberattacks (XSS) forms. XSS attacks are examined in this study to provide a good foundation for attack simulation. The simulation was carried out with the help of the BeEF XSS framework. A basic HTML web page was developed to construct the malicious script for the simulation. The simulation data were gathered and evaluated to provide guidelines for preventing XSS attacks on end-users and Industry 4.0-like systems. This study provides reliable recommendations for improving end-user resilience against XSS attacks, which can help to mitigate the harmful impact of such attacks on Industry 4.0 systems.