The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications.
Drone technology has the potential to revolutionize various sectors, including healthcare, food, and everyday goods, enabling efficient and convenient deliveries. However, in an expected competitive airspace environment with multiple companies, it becomes crucial to establish organized and coordinated access for drones. The Internet of Drones (IoD) aims to ensure equitable airspace utilization. Despite significant advancements in drone delivery, it remains essential to understand the circumstances where drone delivery offers advantages in terms of why, where, and when it should be employed. This study introduces a comprehensive guideline for developing decision systems for drone delivery within the IoD. Specifically, we present a systematic approach to the drone delivery decision problem that tackles those questions (why, where, and when). To provide practical insights, we examine a real delivery scenario using actual data from motorcycle deliveries in Brazilian cities. Additionally, we propose an algorithm for distributing points to serve as depots or droneports. Lastly, we conduct a case study, implementing the proposed guideline and distribution algorithm. The results demonstrate a 49% reduction in delivery time compared to actual delivery durations.
User privacy may remain vulnerable when using encrypted communication protocols, such as HTTPS, if DNS queries are sent in cleartext over UDP port 53 (Do53). In this study, we demonstrate the possibility of characterizing the mobile application a user is using based on its Do53 traffic. By analyzing a dataset of traffic captured from 80 Android mobile apps, we can identify the app being used based on its DNS queries with an accuracy of 88.75%. While modern operating systems, including Android since version 9.0, support encrypted DNS traffic, this feature is not enabled by default and relies on the DNS provider's support. Moreover, even when DNS traffic is encrypted, the DNS service provider still has access to our queries and could potentially extract information from them.
When designing smart cities' building blocks, mobility data plays a fundamental role in applications and services. However, mobility data usually comes with unrestricted location of its corresponding entities (e.g., citizens and vehicles) and poses privacy concerns, among them recovering the identity of those entities with linking attacks. To address the privacy of users' identity, Location Privacy Protection Mechanisms (LPPMs) based on anonymization have been proposed, such as mix-zones. Once the data is protected, a comprehensive discussion about the trade-off between privacy and utility happens. However, issues still arise about the application of anonymized data to smart city development: what are the smart cities applications and services that can best leverage mobility data anonymized by mix-zones? To answer this question, we present a methodology that evaluates the utility in many aspects with metrics related to privacy, mobility, and anonymized trajectories produced by mix-zones. The results showed that the proposed methodology identifies application domains of smart cities in which anonymized data can have more or less utility. Additionally, different datasets present different behaviors in terms of utility. These insights can contribute significantly to the utility of both open and private data markets for smart cities.
The advent of mobile technologies has led to the development of novel services for end-users, with stringent needs and requirements. In the scope of 5G and Beyond 5G systems, non-terrestrial networks are being considered to meet such demands, with special emphasis on high-speed communications and low latency. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers and the ns-3 simulator to emulate different connectivity characteristics. The results evince that an appropriate stream scheduler can indeed yield lower delays for strict time-sensitive applications by up to 36% under unreliable conditions.
