The intrusion detection in the Internet of Things (IoT) network presents a number of difficulties, necessitating the skillful use of network device attributes for precise threat identification. With a multi-phase approach, this research provides a novel intrusion detection method. This technique takes advantage of the non-linear relationship identification capability of the Chatterjee correlation. The Artificial Bee Colony (ABC) approach is employed for optimizing the feature selection procedure. Lastly, a specialized Residual Neural Network (ResNet) is designed that can detect complex relationships from the data using minimal computational cost. The above design contains three types of residual blocks which are designed in a discriminatory fashion to enable the model to capture both the high-level and low-level features. At each layer, an increase in the number of filters is used to enhance the process of feature extraction. The convolutional layers are of great importance since they control the whole learning process. To ensure the effectiveness of the network in collecting complex relations, a method for converting 1D features into 2D images is provided to facilitate the ResNet input. Experimental results indicate that the suggested approach is exceptionally adept at identifying intrusions in the IoT networks, achieving accuracy of 98.5711%. The combination of ABC optimization, ResNet, and Chatterjee correlation leads in a strong and effective intrusion detection system, which shows promise for improving the cyber security of IoT environments.
Sudden user concentration in cellular hotspot environments can significantly degrade the quality of service (QoS) due to the limited capacity of conventional ground base stations (BSs). Although unmanned aerial vehicles (UAVs) equipped with base station functionalities have recently emerged as a promising solution for temporary capacity enhancement, most existing studies assume that UAVs continuously provide services while hovering in the air. Such an approach suffers from severe energy consumption and limited service duration because of the high propulsion power required for hovering. This paper proposes an energy-efficient dynamic cell capacity expansion scheme based on temporary UAV landing pads. The proposed approach divides a macrocell into multiple UAV sub-cells and deploys temporary landing pads at the center of each sub-cell. When a hotspot event occurs, a UAV equipped with a small base station or repeater function is dispatched to the corresponding landing pad and provides wireless services in a landed state. Since the UAV consumes propulsion energy only during movement and significantly reduces energy consumption during service provision, the proposed scheme substantially extends the service duration of UAV-assisted communications. Performance evaluations considering UAV battery constraints demonstrate that the proposed landed UAV-BS scheme achieves approximately 87.7% energy savings and extends the UAV service duration by approximately 8.8 times compared with the conventional hovering UAV-BS scheme. Moreover, the proposed approach effectively reduces the blocking rate and maintains higher average throughput during prolonged hotspot situations.
Point-of-interest (POI) recommendation is a good way to help Smartphone users discover new places by exploiting their preferences and social networks. However, this technique, which is often based on collaborative filtering, suffers from cold-start problem due to insufficient interaction with POIs (ratings) and the absence of declared friendship or trust relationships between users. To address this problem, one solution is to launch several Recommendations Systems (RS) simultaneously, using all available sources of information within the social network, especially in the case of a new user or a new POI. In this paper, a Multi-Agent System for Reconciling POI Recommendation Algorithms (MSRPRA) is proposed to exploit (1) the power of Pearson similarity deduced from POI ratings through the RatAg agent, (2) the effectiveness of Jaccard similarity derived from existing friendship relationships exploited by the FrAg agent and the contribution of trust scores declared by users using the TrAg agent. The system then uses a coordinator agent to merge, using the Borda voting method, the POIs lists generated by the RatAg, FrAg, and TrAg agents. The experimental results show that MSRPRA outperforms approaches based solely on Pearson similarity, Jaccard similarity, or explicit trust. On average, the model improves Precision by 7.38% and Recall by 7.73%, while achieving better precision in terms of RMSE, these results confirm the effectiveness of multi-agent fusion using Borda voting in improving the relevance of recommendations and mitigating the cold-start problem.
With the emergence of IoT, many aspects of our daily life are being transformed. Billions of smart devices are being connected globally to perform efficient decision making for individuals. This increases their requirement to qualify as dependable systems. Thus, the performance of such systems becomes crucial and inherently depends upon the proper functioning of its components and the underlying network. Conventionally network performance has been measured by measuring factors like packet loss, packet delivery ratio, throughput and delay, but these metrics have not provided a satisfactory measure of system dependability. In this contribution, we present analytical formulations that are used to analyze the behavior of the IoT networks, under different types of payloads, such as video, voice and text traffic. We also examine network resilience as an example of FTP (File transfer protocol) and CoAP (Constrained Application Protocol). These measures are systematically correlated with the quality characteristics that have been defined in ISO/IEC 25010, thus providing a cohesive framework of assessing the performance as well as the dependability of an IoT network. The approach suggested gives a greater understanding of those factors that influence the network connectivity and the overall robustness of the system in the case of IoT application.
This study investigated the incidence of cybersickness in engineering students exposed to a custom-designed virtual reality (VR) environment developed by applying mitigation techniques recommended in the prior literature. Conducted at Simón Bolívar University in Barranquilla, Colombia, the study involved 76 participants using a Lenovo ThinkStation with a Lenovo ThinkReality VRX headset to interact with a Unity-based game focused on stacking cubes. Preventive techniques were implemented, including the use of cool color schemes, movement restriction, high resolution and frame rates, and a maximum exposure time of 15 minutes. Participants completed the simulator sickness questionnaire (SSQ), and qualitative feedback was collected through interviews. Results showed that 25% of participants reported cybersickness, a rate consistent with prior studies (20-40%). Significant associations were found with visual and cognitive symptoms such as blurred vision, head pressure, and difficulty focusing, while eye strain and sweating were not significant. Contrary to expectations, age, gender, prior VR experience, and videogame total playtime did not predict cybersickness. However, videogame session frequency was strongly associated, suggesting exposure distribution is more influential than total duration.
