IoT, Volume 5, Issue 3 (September 2024) – 5 articles

In the age of fast digital transformation, Docker containers have become one of the central technologies for flexible and scalable application deployment. However, this has opened a new dimension of challenges in security, which are skyrocketing with increased technology adoption. This paper discerns these challenges through a manifold approach: first, comprehensive static analysis by Trivy, and second, real-time dynamic analysis by Falco in order to uncover vulnerabilities in Docker environments pre-deployment and during runtime. One can also find similar challenges in security within the Internet of Things (IoT) sector, due to the huge number of devices connected to WiFi networks, from simple data breaches such as brute force attacks and unauthorized access to large-scale cyber attacks against critical infrastructure, which represent only a portion of the problems. In connection with this, this paper is calling for the execution of robust AWS cloud security solutions: IoT Core, CloudWatch, and GuardDuty. IoT Core provides a secure channel of communication for IoT devices, and CloudWatch offers detailed monitoring and logging. Additional security is provided by GuardDuty’s automatized threat detection system, which continuously seeks out potential threats across network traffic. Armed with these technologies, we try to build a more resilient and privacy-oriented IoT while ensuring the security of our digital existence. The result is, therefore, an all-inclusive work on security in both Docker and IoT domains, which might be considered one of the most important efforts so far to strengthen the digital infrastructure against fast-evolving cyber threats, combining state-of-the-art methods of static and dynamic analyses for Docker security with advanced, cloud-based protection for IoT devices. Full article

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies. Full article

Home care and telemedicine are crucial for physical and mental health. Although there is a lot of information on these topics, it is scattered across various sources, making it difficult to identify key contributions and authors. This study conducts a scientometric analysis to consolidate the most relevant information. The methodology is divided into two parts: first, a scientometric mapping that analyzes scientific production by country, journal, and author; second, the identification of prominent contributions using the Tree of Science (ToS) tool. The goal is to identify trends and support decision-making in the health sector by providing guidelines based on the most relevant research. Full article

This study investigates communication between UAVs using long range (LoRa) devices, focusing on the interaction between a LoRa gateway UAV and other UAVs equipped with LoRa transmitters. By conducting experiments across various geographical regions, this study aims to delineate the fundamental boundary conditions for the efficient control of a UAV fleet. The parameters under analysis encompass inter-device spacing, radio interference effects, and terrain topography. This research yields pivotal insights into communication network design and optimization, thereby enhancing operational efficiency and safety within diverse geographical contexts for UAV operations. Further research insights could involve a weather analysis and implementation of improved solutions in terms of communication systems. Full article

The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks. Full article