Search

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain.

The Drone Forensics (DRFs) field is a branch of digital forensics, which involves the identification, capture, preservation, reconstruction, analysis, and documentation of drone incidents. Several models have been proposed in the literature for the DRF field, which generally discusses DRF from a reactive forensic perspective; however, the proactive forensic perspective is missing. Therefore, this paper proposes a novel forensic readiness framework called Drone Forensics Readiness Framework (DRFRF) using the design science method. It consists of two stages: (i) proactive forensic stage and (ii) reactive forensic stage. It considers centralized logging of all events of all the applicants within the drone device in preparation for an examination. It will speed up gathering data when an investigation is needed, permitting the forensic investigators to handle the examination and analysis directly. Additionally, digital forensics analysts can increase the possible use of digital evidence while decreasing the charge of performing forensic readiness. Thus, both the time and cost required to perform forensic readiness could be saved. The completeness, logicalness, and usefulness of DRFRF were compared to those of other models already existing in the DRF domain. The results showed the novelty and efficiency of DRFRF and its applicability to the situations before and after drone incidents., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Fahad Mazaed Alotaibi et al.)

Over the past years, carbon-based materials and especially graphene, have always been known as one of the most famous and popular materials for sensing applications. Graphene poses outstanding electrical and physical properties that make it favorable to be used as a transducer in the gas sensors structure. Graphene experiences remarkable changes in its physical and electrical properties when exposed to various gas molecules. Therefore, in this study, a set of new analytical models are developed to investigate energy band structure, the density of states (DOS), the velocity of charged carriers and I-V characteristics of the graphene after molecular (CO, NO 2 , H 2 O) adsorption. The results show that gas adsorption modulates the energy band structure of the graphene that leads to the variation of the energy bandgap, thus the DOS changes. Consequently, graphene converts to semiconducting material, which affects the graphene conductivity and together with the DOS variation, modulate velocity and I-V characteristics of the graphene. These parameters are important factors that can be implemented as sensing parameters and can be used to analyze and develop new sensors based on graphene material.