IoT Notary : Attestable Sensor Data Capture in IoT Environments

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced—IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals’ privacy or service integrity. To address such concerns, we propose IoT Notary , a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable “proof-of-integrity,” based on which a verifier can attest that captured sensor data adhere to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California, Irvine to provide various real-time location-based services on the campus. We present extensive experiments over real-time WiFi connectivity data to evaluate IoT Notary , and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day’s data in less than 2 s even using a resource-limited device.

Data Analytics for Air Travel Data: A Survey and New Perspectives

From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future. We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research.

A Meta-analysis of Privacy, Ethical and Security Aspects of Facial Recognition Systems

Facial recognition systems use advanced computing to capture facial information and compare the same with proprietary databases for validation. The emergence of data capturing intermediaries and open access image repositories have compounded the need for a holistic perspective for handling the privacy and security challenges associated with FRS. The study presents the results of a bibliometric analysis conducted on the topic of privacy, ethical and security aspects of FRS. This study presents the level of academic discussion on the topic using bibliometric performance analysis. The results of the bibliographic coupling analysis to identify the research hotspots are also presented. The results also include the systematic literature review of 148 publications that are distributed across seven themes. Both the bibliometric and systematic analysis showed that privacy and security in FRS requires a holistic perspective that cuts across privacy, ethical, security, legal, policy and technological aspects.

Multi-Drone 3D Building Reconstruction Method

In the recent decade, the rapid development of drone technologies has made many spatial problems easier to solve, including the problem of 3D reconstruction of large objects. A review of existing solutions has shown that most of the works lack the autonomy of drones because of nonscalable mapping techniques. This paper presents a method for centralized multi-drone 3D reconstruction, which allows performing a data capturing process autonomously and requires drones equipped only with an RGB camera. The essence of the method is a multiagent approach—the control center performs the workload distribution evenly and independently for all drones, allowing simultaneous flights without a high risk of collision. The center continuously receives RGB data from drones and performs each drone localization (using visual odometry estimations) and rough online mapping of the environment (using image descriptors for estimating the distance to the building). The method relies on a set of several user-defined parameters, which allows the tuning of the method for different task-specific requirements such as the number of drones, 3D model detalization, data capturing time, and energy consumption. By numerical experiments, it is shown that method parameters can be estimated by performing a set of computations requiring characteristics of drones and the building that are simple to obtain. Method performance was evaluated by an experiment with virtual building and emulated drone sensors. Experimental evaluation showed that the precision of the chosen algorithms for online localization and mapping is enough to perform simultaneous flights and the amount of captured RGB data is enough for further reconstruction.

Forecasting Undergraduate Majors Using Academic Transcript Data

Committing to a major is a fateful step in an undergraduate education, yet the relationship between courses taken early in an academic career and ultimate major selection remains little studied at scale. Using transcript data capturing the academic careers of 26,892 undergraduates enrolled at a private university between 2000 and 2020, we describe enrollment histories using natural-language methods and vector embeddings to forecast terminal major on the basis of course sequences beginning at college entry. We find (I) a student's very first enrolled course predicts major thirty times better than random guessing and more than a third better than majority-class voting, (II) modeling strategies substantially influence forecasting accuracy, and (III) course portfolios varies substantially within majors, raising novel questions what majors mean or signify in relation to undergraduate course histories.

Generation of a Multi-Scale Historic BIM-GIS with Digital Recording Tools and Geospatial Information

This paper discusses the creation of an integrated historic BIM-GIS for the complex of San Pietro al Monte, an important Romanesque monument in Civate (Italy) inscribed in the UNESCO tentative list with other seven medieval Benedictine settlements. The reason behind the choice of an integrated H-BIM-GIS solution is motivated by the large extension of the considered area (about 30 km2) and the need for multi-scale digital information integrated into a 3D parametric environment. The model includes geospatial information at a territorial scale and in situ digital data capturing the complex at a higher level of detail. The work aims at exploring the pros and cons of a novel parametric 3D environment able to integrate both BIM and GIS data, methods, and processing tools in the case of historic buildings and sites.

Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring

Real-time health monitoring of civil infrastructures is performed to maintain their structural integrity, sustainability, and serviceability for a longer time. With smart electronics and packaging technology, large amounts of complex monitoring data are generated, requiring sophisticated artificial intelligence (AI) techniques for their processing. With the advancement of technology, more complex AI models have been applied, from simple models to sophisticated deep learning (DL) models, for structural health monitoring (SHM). In this article, a comprehensive review is performed, primarily on the applications of AI models for SHM to maintain the sustainability of diverse civil infrastructures. Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms easier. UAV is found to be the most promising smart data acquisition technology, whereas convolution neural networks are the most impressive DL model reported for SHM. Furthermore, current challenges and future perspectives of AI-based SHM systems are also described separately. Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems. The integration of SHM with IoT and cloud-based computing is leading us towards the evolution of future smart cities.