An intelligent transportation system (ITS) leverages interrelated data, advanced algorithms, and real-time communication to enhance urban mobility and safety. However, the integration of diverse communication technologies and computing architectures, including fog and mobile edge computing (FMEC), introduces substantial security and privacy challenges. This paper surveys security and privacy issues specific to ITS, focusing on vulnerabilities in vehicle-to-everything (V2X) communication, the privacy implications of large-scale data collection, and the role of FMEC. We present key security mechanisms, privacy-preserving techniques, and industry best practices to build resilient ITS infrastructures capable of withstanding cyber threats and safeguarding user privacy.
The increasing number of IoT devices has significantly raised the demand for real-time data processing in fogcloud environments. These environments are crucial for supporting latency-sensitive applications such as autonomous vehicles. However, fog nodes are resource-constrained, making it critical to optimize their utilization. Various heuristic-based approaches, including heuristic and hybrid-heuristic algorithms, have been explored, but they still face limitations in effectively addressing resource allocation challenges. Hyper-heuristic methods have emerged as a promising alternative, yet their application in fogcloud environments remains underexplored This survey reviews state-of-the-art resource allocation approaches, analyzing their strengths and limitations while identifying key research gaps. Our findings highlight the need for learning-based hyper-heuristic algorithms to improve efficiency and scalability in fog-cloud environments.
The study aims to explore image upload and download services, to provide readers an insight into how they are built from scratch. In the process, the paper discusses about Google Cloud Services, CDN and Docker runtime environment. It helps readers understand the importance of modularity. The paper dives deep into the architectural details of an iOS application built using the SwiftUI framework and MVVM design pattern. The readers will understand how professional mobile applications are built, emphasizing architecture. Readers will understand the difference between MVC and MVVM design pattern and Docker vs. non-Docker systems. The proposed system is also implemented, and its code is shared with readers via a public Github repository.
As cloud computing is the backbone of the IT world, cloud security has been a major concern to protect it against attackers, failures, or even misconfiguration. Trust evaluation becomes highly important in mission-critical cloud operations. Therefore, in this research, we focus on infrastructure as a service (IaaS) based cloud environment to evaluate the trust of Virtual Machines (VMs) to enhance the environment's security. In this work, we apply a comprehensive trust model that integrates a smoothed risk scoring mechanism tailored for virtualized cloud infrastructures while incorporating prior behavior via Perfect Bayesian Equilibrium (PBE). Trust values are iteratively updated using a combination of observed VM behavior, calculated risk scores (derived from CVSS, EPSS), and a reward-penalty system defined by consistent or malicious actions. Using game theory, multiple simulations were performed to derive trust metrics and enable the identification of reliable VMs. The findings highlight the impact of specific metrics on trust evaluation, with results indicating that historical behavior patterns and risk factors are critical in determining VM reliability. The proposed trust model effectively supports the selection of VMs that maintain higher security standards, improving overall cloud system reliability.
The rapid proliferation of Small Unmanned Aircraft Systems (sUAS) and RFID technologies introduces new threats to national security. While sUAS revolutionizes business operations, it also becomes a tool for malicious actors due to its wireless vulnerabilities. Many Intrusion Detection Systems (IDS) have been proposed to address this, but they often require offline training with heavy processing, making them unsuitable for dynamic mission changes. This paper presents a novel framework that leverages IoT infrastructure (e.g., smart cities) to support rapid, in-field adaptive model training and parameter estimation for IDS-equipped sUAS. To handle the computational demands, we propose a cluster-oriented, distributed training algorithm using LSTM with mini-batch gradient descent, allowing hundreds of IoT devices to collaboratively perform model parameter estimations. The model was implemented on an IoT platform (NXP-Kinetis K64-120 MHz), achieving high prediction accuracy with minimal power consumption and training time, even with contaminated datasets.
