Discovery Mechanisms for the Sensor Web

This paper addresses the discovery of sensors within the OGC Sensor Web Enablement framework. Whereas services like the OGC Web Map Service or Web Coverage Service are already well supported through catalogue services, the field of sensor networks and the according discovery mechanisms is still a challenge. The focus within this article will be on the use of existing OGC Sensor Web components for realizing a discovery solution. After discussing the requirements for a Sensor Web discovery mechanism, an approach will be presented that was developed within the EU funded project “OSIRIS”. This solution offers mechanisms to search for sensors, exploit basic semantic relationships, harvest sensor metadata and integrate sensor discovery into already existing catalogues

Safety Sufficiency for NextGen: Assessment of Selected Existing Safety Methods, Tools, Processes, and Regulations

NextGen is a complex socio-technical system and, in many ways, it is expected to be more complex than the current system. It is vital to assess the safety impact of the NextGen elements (technologies, systems, and procedures) in a rigorous and systematic way and to ensure that they do not compromise safety. In this study, the NextGen elements in the form of Operational Improvements (OIs), Enablers, Research Activities, Development Activities, and Policy Issues were identified. The overall hazard situation in NextGen was outlined; a high-level hazard analysis was conducted with respect to multiple elements in a representative NextGen OI known as OI-0349 (Automation Support for Separation Management); and the hazards resulting from the highly dynamic complexity involved in an OI-0349 scenario were illustrated. A selected but representative set of the existing safety methods, tools, processes, and regulations was then reviewed and analyzed regarding whether they are sufficient to assess safety in the elements of that OI and ensure that safety will not be compromised and whether they might incur intolerably high costs

Two-Step Injection Method for Collecting Digital Evidence in Digital Forensics

In digital forensic investigations, the investigators take digital evidence from computers, laptops or other electronic goods. There are many complications when a suspect or related person does not want to cooperate or has removed digital evidence. A lot of research has been done with the goal of retrieving data from flash memory or other digital storage media from which the content has been deleted. Unfortunately, such methods cannot guarantee that all data will be recovered. Most data can only be recovered partially and sometimes not perfectly, so that some or all files cannot be opened. This paper proposes the development of a new method for the retrieval of digital evidence called the Two-Step Injection method (TSI). It focuses on the prevention of the loss of digital evidence through the deletion of data by suspects or other parties. The advantage of this method is that the system works in secret and can be combined with other digital evidence applications that already exist, so that the accuracy and completeness of the resulting digital evidence can be improved. An experiment to test the effectiveness of the method was set up. The developed TSI system worked properly and had a 100% success rate

Digital construction and management the public’s infrastructures

The purpose of the present paper “Digital construction and management the public’s infrastructures” is to propose an interconnected development approach, in the management of public infrastructure asset, that through of digital modeling (BIM*) and interoperability provides tools to support decision-making processes. In detail, this work analyzes the innovative process of developing digital tools for the institutional tasks of supervision and support for the management of land transport infrastructure in the Italian national system. Therefore, trough of one assumed a georeferenced network of “digital twins” have been valued the scenarios obtainable whit the digitalization of the public works and of the territory’s surveys. The principles for managing information flows for Italian’s public transport infrastructures have been developed in accordance with national legislation and the reference UNI standards. The assumed flow is on the exchange of data between the managing subjects with the owners’ authorities and surveillance bodies, taking as pivot element the Index public work (IOP) code attributed to each public work. Finally, a conceptual model has been proposed for the energy analysis of the road section and the identification of the best areas to create the “green islands” to produce renewable energy, for the management of infrastructure and for the recharging of electric vehicles

Scalable Incident Reporting Framework: A Sensor and IoT Research

The Internet of Things (IoT) is one of the most rapidly emerging technologies. It is observed that while many devices/machines get connected in an application, it is a challenge for the IoT application designer to keep the application scalable. Scalability is the ability of a device/application to adapt to the changes in the environment and meet the changing needs in the future. The paper presents a layered IoT architecture and discusses issues related to the scalability of each layer. The best open-source technologies are explored. A novel system architecture of a scalable IoT framework is conceptualized in this paper. An application covering vehicle accident reporting is designed with the proposed framework. The application is tested in real-time using the standalone hardware and its ability to report the incidents is confirmed. The scalability metrics of the proposed framework are evaluated and the results are reported

Artificial intelligence enabled automatic traffic monitoring system

The rapid advancement in the field of machine learning and high-performance computing have highly augmented the scope of video-based traffic monitoring systems. In this study, an automatic traffic monitoring system is proposed that deploys several state-of-the-art deep learning algorithms based on the nature of traffic operation. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to track congestion, detect traffic anomalies and tabulate vehicle counts. To monitor traffic queues, this study implements a Mask region-based convolutional neural network (Mask R-CNN) that predicts congestion using pixel-level segmentation masks on classified regions of interest. Similarly, the model was used to accurately extract traffic queue-related information from infrastructure mounted video cameras. The use of infrastructure-mounted CCTV cameras for traffic anomaly detection and verification is further explored. Initially, a convolutional neural network model based on you only look once (YOLO), a popular deep learning framework for object detection and classification is deployed. The following identification model, together with a multi-object tracking system (based on intersection over union -- IOU) is used to search for and scrutinize various traffic scenes for possible anomalies. Likewise, several experiments were conducted to fine-tune the system's robustness in different environmental and traffic conditions. Some of the techniques such as bounding box suppression and adaptive thresholding were used to reduce false alarm rates and refine the robustness of the methodology developed. At each stage of our developments, a comparative analysis is conducted to evaluate the strengths and limitations of the proposed approach. Likewise, IOU tracker coupled with YOLO was used to automatically count the number of vehicles whose accuracy was later compared with a manual counting technique from CCTV video feeds. Overall, the proposed system is evaluated based on F1 and S3 performance metrics. The outcome of this study could be seamlessly integrated into traffic system such as smart traffic surveillance system, traffic volume estimation system, smart work zone management systems, etc.by Vishal MandalIncludes bibliographical reference

The sources and characteristics of electronic evidence and artificial intelligence

In this updated edition of the well-established practitioner text, Stephen Mason and Daniel Seng have brought together a team of experts in the field to provide an exhaustive treatment of electronic evidence and electronic signatures. This fifth edition continues to follow the tradition in English evidence text books by basing the text on the law of England and Wales, with appropriate citations of relevant case law and legislation from other jurisdictions