Forensic Tracking and Mobility Prediction in Vehicular Networks

Most contemporary tracking applications consider an online approach where the target is being tracked in real time. In criminal investigations, however, it is common that only offline tracking is possible, where tracking takes place after the fact; that is, given an incomplete trace of a suspect, the task is to reconstruct the missing parts and obtain the full trace. With the recent proliferation of modern transportation systems, target entities are likely to interact with different transportation means. Thus, in this paper, we first introduce a class of mobility models that has been especially tailored for forensic analysis then propose several instances emulating different transportation means. We then use these models to build a full-fledged offline multi-modal forensic tracking system that reconstructs an incomplete trace of a particular target. We provide theoretical evaluation of the reconstruction algorithm and show that it is both complete and optimal

Forensic Tracking and Surveillance

Digital forensics is an emerging field that has uniquely brought together academics, practitioners and law enforcement. Research in this area was inspired by the numerous challenges posed by the increased sophistication of criminal tools. Traditionally, digital forensics has been confined to the extraction of digital evidence from electronic devices. This direct extraction of digital evidence, however, no longer suffices. Indeed, extracting completely raw data without further processing and/or filtering is, in some cases, useless. These problems can be tackled by the so-called ``computational forensics" where the reconstructs evidence are undertaken further processing. One important application of computational forensics is criminal tracking, which we collectively call ``forensic tracking" and is the main subject of this thesis. This thesis adopts an algorithmic approach to investigate the feasibility of conducting forensic tracking in various environments and settings. Unlike conventional tracking, forensic tracking has to be passive such that the target (who is usually a suspect) should not be aware of the tracking process. We begin by adopting pedestrian setting and propose several online (real-time) forensic tracking algorithms to track a single or multiple targets passively. Beside the core tracking algorithms, we also propose other auxiliary algorithms to improve the robustness and resilience of tracking. We then extend the scope and consider vehicular forensic tracking, where we investigate both online and offline tracking. In online vehicular tracking, we also propose algorithms for motion prediction to estimate the near future movement of target vehicles. Offline vehicular tracking, on the other hand, entails the post-hoc extraction and probabilistic reconstruction of vehicular traces, which we adopt Bayesian approach for. Finally, the contributions of the thesis concludes with building an algorithmic solution for multi-modal tracking, which is a mixed environment combining both pedestrian and vehicular settings

Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review

Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this paper, connected vehicles technology based vehicular congestion identification techniques are studied. Computing paradigms that can be used for the vehicular network are studied to develop a practically feasible vehicular congestion detection system that performs accurately for a large coverage area and multiple scenarios. The designed system is expected to detect congestion to meet traffic management goals that are of primary importance in intelligent transportation systems

Vulnerable road users and connected autonomous vehicles interaction: a survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

The development of a comprehensive and decisive drone defense integrated control system that can provide maximum security is crucial for maintaining territorial integrity and accelerating smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study explores recent developments in the design of robust drone defense control systems that can observe and respond not only to drone attacks inside and outside a facility but also to equipment data such as CCTV security control on the ground and security sensors in the facility at a glance. Also, it considered DDS strategies, schema, and innovative security setups in different regions. Finally, open research issues in DDs designs are discussed, and useful recommendations are provided. Effective means for drone source authentication, delivery package verification, operator authorization, and dynamic scenario-specific engagement are solicited for comprehensive DDS design for maximum security Received: 2023-03-07 Revised: 2023-04-2