LoRaWAN communication implementation platforms

A key role in the development of smart Internet of Things (IoT) solutions is played by wireless communication technologies, especially LPWAN (Low-Power Wide-Area Network), which are becoming increasingly popular due to their advantages: long range, low power consumption and the ability to connect multiple edge devices. However, in addition to the advantages of communication and low power consumption, the security of transmitted data is also important. End devices very often have a small amount of memory, which makes it impossible to implement advanced cryptographic algorithms on them. The article analyzes the advantages and disadvantages of solutions based on LPWAN communication and reviews platforms for IoT device communication in the LoRaWAN (LoRa Wide Area Network) standard in terms of configuration complexity. It describes how to configure an experimental LPWAN system being built at the Department of Computer Science and Telecommunications at Poznan University of Technology for research related to smart buildings

PNT cyber resilience : a Lab2Live observer based approach, Report 1 : GNSS resilience and identified vulnerabilities. Technical Report 1

The use of global navigation satellite systems (GNSS) such as GPS and Galileo are vital sources of positioning, navigation and timing (PNT) information for vehicles. This information is of critical importance for connected autonomous vehicles (CAVs) due to their dependence on this information for localisation, route planning and situational awareness. A downside to solely relying on GNSS for PNT is that the signal strength arriving from navigation satellites in space is weak and currently there is no authentication included in the civilian GNSS adopted in the automotive industry. This means that cyber-attacks against the GNSS signal via jamming or spoofing are attractive to adversaries due to the potentially high impact they can achieve. This report reviews the vulnerabilities of GNSS services for CAVs (a summary is shown in Figure 1), as well as detection and mitigating techniques, summarises the opinions on PNT cyber testing sourced from a select group of experts, and finishes with a description of the associated lab-based and real-world feasibility study and proposed research methodology

Experimental and simulation approaches for improving integrated circuit impedance characterisation under electrostatic discharge condition

This study was conducted to produce an accurate macro model of an Integrated Circuit (IC) by means of experiment, to be implemented for any application, both in time domain and frequency domain analysis. A probe is designed and optimised to measure a multipin IC with different pin distance. The multipin IC characteristic impedance was experimentally measured using two probes, where the measured combinations of S-Parameter are combined using a self-written software to produce a complete S-Parameter representation of the IC. The S-Parameter file is not suitable for time domain analysis, because vector fitting is required for each simulation. The S-Parameter file is then converted to macro model with controlled accuracy level. The macro model is also ensured its passivity and causality by using commercial macro modelling software (IdEM). The macro model has shown good correlation between time domain and frequency domain analysis. The macro model was then exported as a SPICE model, and was implemented on an Advanced Driver Assistance Systems (ADAS) printed circuit board (PCB). Co-simulation was then performed on the PCB and the results are compared with the measurement results of the fabricated PCB. The SPICE model used in this simulation has shown good resonant frequency correlation between 91 % to 99 %. Finally, the PCB along with the SPICE model was simulated with an Electrostatic Discharge (ESD) gun to observe the current distribution. This research has produced a practical and accurate method, to accurately model an IC as a SPICE model. The SPICE model will help many engineers to improve the accuracy of the virtual prototyping, hence reducing the product’s time to market

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.Includes bibliographical references

Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry

[Abstract] Industry 5.0 follows the steps of the Industry 4.0 paradigm and seeks for revolutionizing the way industries operate. In fact, Industry 5.0 focuses on research and innovation to support industrial production sustainability and place the well-being of industrial workers at the center of the production process. Thus, Industry 5.0 relies on three pillars: it is human-centric, it encourages sustainability and it is aimed at developing resilience against disruptions. Such core aspects cannot be fully achieved without a transparent end-to-end human-centered traceability throughout the value chain. As a consequence, Auto-Identification (Auto-ID) technologies play a key role, since they are able to provide automated item recognition, positioning and tracking without human intervention or in cooperation with industrial operators. Although the most popular Auto-ID technologies provide a certain degree of security and productivity, there are still open challenges for future Industry 5.0 factories. This article analyzes and evaluates the Auto-ID landscape and delivers a holistic perspective and understanding of the most popular and the latest technologies, looking for solutions that cope with harsh, diverse and complex industrial scenarios. In addition, it describes a methodology for selecting Auto-ID technologies for Industry 5.0 factories. Such a methodology is applied to a specific use case of the shipbuilding industry that requires identifying the main components of a ship during its construction and repair. To validate the outcomes of the methodology, a practical evaluation of passive and active UHF RFID tags was performed in an Offshore Patrol Vessel (OPV) under construction, showing that a careful selection and evaluation of the tags enables product identification and tracking even in areas with a very high density of metallic objects. As a result, this article serves as a useful guide for industrial stakeholders, including future developers and managers that seek for deploying identification and traceability technologies in Industry 5.0 scenarios.This work was supported in part by the Auto-Identication for Intelligent Products Research Line of the Navantia-Universidade da Coruña Joint Research Unit under Grant IN853B-2018/02, and in part by the Centro de Investigación de Galicia ``CITIC,'' funded by Xunta de Galicia and the European Union (European Regional Development Fund-Galicia 2014_2020 Program) under Grant ED431G 2019/01Xunta de Galicia; IN853B-2018/02Xunta de Galicia; ED431G 2019/0

RISK MITIGATION IN THE SUPPLY CHAIN: EXAMINING THE ROLE OF IT INVESTMENT TO MANAGE SAFETY PERFORMANCE

Safety management in the supply chain is an interesting topic. The existence of unexpected supply chain events makes supply chain decision making difficult. To improve their response to unexpected events such as natural disasters or workplace accidents, managers are beginning to examine the link between information technology (IT) and safety in the supply chain. This dissertation examines the IT and safety link in three main ways. First, in the chapter entitled, "IT Investment and Safety: An Examination of The Impact of Information Technology on Safety Performance in a High Reliability Organization," drawing upon the work of Bharadwaj (2000), a theoretical model that links a firm's investment in IT resources to safety is developed. This model is empirically tested. A key finding is that physical IT resources, human IT resources, and growth in IT resources do contribute to safety performance. The second way that the IT and safety link is examined is through a U.S. Department of Transportation sponsored survey. In the chapter entitled, "Technology Adoption Patterns in the U.S. Motor Carrier Industry," a national survey is conducted to examine the safety technology adoption practices of larger trucking firms. The survey consists of twenty-six leading-edge safety technologies. A key finding is that larger trucking firms and firms that travel long distances are leaders in IT investment. Drawing on the resource-based view of the firm (RBV), the third way that the IT and safety link is examined is in the chapter entitled "Driving for Safety: An Examination of Safety Technology Adoption and Firm Safety Performance in the U.S. Motor Carrier Industry." The RBV framework describes how a firm's internal resources may be used to improve firm performance. Based on an over 50% survey response rate, a key finding is that safety technology resources do contribute to safety performance. It is also discovered that if the firm's top management team is knowledgeable about safety technology practices, the effect of safety technology resources on safety performance increases. Similarly, if the firm's IT staff has technology project management skills, the effect of safety technology resources on safety performance increases

Ortsbezogene Anwendungen und Dienste: 9. Fachgespräch der GI/ITG-Fachgruppe Kommunikation und Verteilte Systeme ; 13. & 14. September 2012

Der Aufenthaltsort eines mobilen Benutzers stellt eine wichtige Information für Anwendungen aus den Bereichen Mobile Computing, Wearable Computing oder Ubiquitous Computing dar. Ist ein mobiles Endgerät in der Lage, die aktuelle Position des Benutzers zu bestimmen, kann diese Information von der Anwendung berücksichtigt werden -- man spricht dabei allgemein von ortsbezogenen Anwendungen. Eng verknüpft mit dem Begriff der ortsbezogenen Anwendung ist der Begriff des ortsbezogenen Dienstes. Hierbei handelt es sich beispielsweise um einen Dienst, der Informationen über den aktuellen Standort übermittelt. Mittlerweile werden solche Dienste kommerziell eingesetzt und erlauben etwa, dass ein Reisender ein Hotel, eine Tankstelle oder eine Apotheke in der näheren Umgebung findet. Man erwartet, nicht zuletzt durch die Einführung von LTE, ein großes Potenzial ortsbezogener Anwendungen für die Zukunft. Das jährlich stattfindende Fachgespräch "Ortsbezogene Anwendungen und Dienste" der GI/ITG-Fachgruppe Kommunikation und Verteilte Systeme hat sich zum Ziel gesetzt, aktuelle Entwicklungen dieses Fachgebiets in einem breiten Teilnehmerkreis aus Industrie und Wissenschaft zu diskutieren. Der vorliegende Konferenzband fasst die Ergebnisse des neunten Fachgesprächs zusammen.The location of a mobile user poses an important information for applications in the scope of Mobile Computung, Wearable Computing and Ubiquitous Computing. If a mobile device is able to determine the current location of its user, this information may be taken into account by an application. Such applications are called a location-based applications. Closely related to location-based applications are location-based services, which for example provides the user informations about his current location. Meanwhile such services are deployed commercially and enable travelers for example to find a hotel, a petrol station or a pharmacy in his vicinity. It is expected, not least because of the introduction of LTE, a great potential of locations-based applications in the future. The annual technical meeting "Location-based Applications and Services" of the GI/ITG specialized group "Communication and Dsitributed Systems" targets to discuss current evolutions in a broad group of participants assembling of industrial representatives and scientists. The present proceedings summarizes the result of the 9th annual meeting

Phase C/D program development plan. Volume 1: Program plan

The Phase C/D definition of the Modular Space Station has been developed. The modular approach selected during the option period was evaluated, requirements were defined, and program definition and preliminary design were accomplished. The Space Station Project is covered in depth, the research applications module is limited to a project-level definition, and the shuttle operations are included for interface requirements identification, scheduling, and costing. Discussed in detail are: (1) baseline program and project descriptions; (2) phase project planning; (3) modular space station program schedule; (4) program management plan; (5) operations; (6) facilities; (7) logistics; and (8) manpower

Context-Aware Aided Parking Solutions Based on VANET

Vehicular Ad-hoc Network (VANET) is a special application of the Mobile Ad-hoc Network (MANET) for managing road traffic and substantially contributes to the development of Intelligent Transportation Systems (ITS). VANET was introduced as a standard for data communication between moving vehicles with and without fixed infrastructure. It aims to support drivers by improving safety and driving comfort as a step towards constructing a safer, cleaner and a more intelligent environment. Nowadays, vehicles are manufactured equipped with a number of sensors and devices called On Board Units (OBU) assisting the vehicle to sense the surrounding environment and then process the context information to effectively manage communication with the surrounding vehicles and the associated infrastructure. A number of challenges have emerged in VANET that have encouraged researchers to investigate this concept further. Many of the recent studies have applied different technologies for intelligent parking management. However, despite all the technological advances, researchers are no closer to developing a system that enables drivers to easily locate and reserve a parking space. Limited resources such as energy, storage space, availability and reliability are factors which could have contributed to the lack success and progress in this area. The task then is to close these gaps and present a novel solution for parking.This research intends to address this need by developing a novel architecture for locating and reserving a parking space that best matches the driver's preferences and vehicle profile without distracting the driver. The simple and easy-to-use mechanism focuses on the domain of an intelligent parking system that exploits the concept of InfoStation (IS) and context-aware system creating a single framework to locate and reserve a parking space. A three tier network topology comprising of vehicles, IS and the InfoStation Centre (ISC) has been proposed as the foundation of the on-street parking system architecture. The thesis attempts to develop the architecture of a parking management solution as a comfort-enhancing application that offers to reduce congestion related stress and improve the driver experience by reducing the time it takes to identify and utilise a parking space that is available.Saudi Arabia Cultural Bureau in U