SafePASS - Transforming marine accident response

The evacuation of a ship is the last line of defence against human loses in case of emergencies in extreme fire and flooding casualties. Since the establishment of the International Maritime Organisation (IMO), Maritime Safety is its cornerstone with the Safety of Life at Sea Convention (SOLAS) spearheading its relentless efforts to reduce risks to human life at sea. However, the times are changing. On one hand, we have the new opportunities created with the vast technological advances of today. On the other, we are facing new challenges, with the ever-increasing size of the passenger ships and the societal pressure for a continuous improvement of maritime safety. In this respect, the EU-funded Horizon 2020 Research and Innovation Programme project SafePASS, presented herein, aims to radically redefine the evacuation processes, the involved systems and equipment and challenge the international regulations for large passenger ships, in all environments, hazards and weather conditions, independently of the demographic factors. The project consortium, which brings together 15 European partners from industry, academia and classification societies. The SafePASS vision and plan for a safer, faster and smarter ship evacuation involves: i) a holistic and seamless approach to evacuation, addressing all states from alarm to rescue, including the design of the next generation of life-saving appliances and; ii) the integration of ‘smart’ technology and Augmented Reality (AR) applications to provide individual guidance to passengers, regardless of their demographic characteristics or hazard (flooding or fire), towards the optimal route of escape

Study of evacuation drills through data collection, dimensional analysis, statistical regression, and IoT technologies

Let's imagine the evacuation sirens go off, how soon would we all be out of the building? It will depend how many we are, 1000 people or maybe just 200? It will also depend on the dimensions and design of the building, and above all on how we behave. I mean, the time to assimilate that we have to leave, decide to leave, perhaps delayed by picking something up, waiting for or convincing someone, and other issues that make the exit longer. In other words, it is a system with many components that interact with each other. The foundations of evacuation modeling were established in the 1970s and 80s, these being an analytical breakdown of the total time when carrying out an evacuation taking into account the different issues that could influence each of the defined components. All these aspects were of increasing complexity and began to be studied with the help of computer simulations. Computer simulations are very helpful, but require a large amount of resources to carry them out. They do not avoid the need to carry out evacuation drills and do not allow comparisons between evacuations of different buildings. This Ph.D. thesis proposes a different approach, from a holistic view, of a system as a whole, where each building is a black box characterized by a dimensionless parameter that allows different buildings to be differentiated. The building is the independent variable of the system, while the ratio of evacuation time to people evacuated will be the dependent variable. In this way, the evacuations of different buildings can be compared. One great difficulty concerning the study and research of evacuations is to collect enough data, both quantitative and qualitative. In this Ph.D. thesis, data have been collected from evacuation drills at the University of Valladolid for 10 years. In addition, a new indoor positioning system is proposed to facilitate the collection of future data that could be used to feed both the model proposed by this thesis, and to validate other study models. This Ph.D. thesis offers an unprecedented approach to be able to compare the evacuations of different buildings, taking into account their most relevant characteristics for the evacuation, which until now had not been possible. The theoretical approach is supported by the historical data collected, and by data published by other authors. Furthermore, it also offers a viable solution for collecting more evacuation data using indoor positioning technologies.Imaginemos que suenan las sirenas de evacuación, ¿en cuánto tiempo estaríamos todos fuera del edificio? Dependerá cuantos seamos (¿1.000 personas o quizás sólo 200?), de las dimensiones y diseño del edificio, y sobre todo de cómo nos comportemos, es decir, el tiempo que tardemos en asimilar que tenemos que salir, en decidirnos a salir, quizás en entretenernos en coger algo, o esperar o convencer a alguien, y otras cuestiones que nos pueden entretener. Es decir, se trata de un sistema con multitud de elementos que interaccionan entre sí. Las bases de la modelización de la evacuación se establecieron en las décadas de los años 70 y 80 del siglo XX, siendo un desglose analítico del tiempo total a considerar cuando se efectúa una evacuación teniendo en cuenta las diferentes variables que podían influir en cada uno de los elementos definidos. La modelización de todos estos aspectos de complejidad creciente, pasó a ser estudiado con la ayuda de simulaciones por ordenador. Las simulaciones de ordenador son de gran ayuda pero implican un gran esfuerzo en recursos para poder llevarlas a cabo, no evitan la realización de ejercicios de simulacros y no permiten comparar edificios diferentes. Esta tesis doctoral propone un enfoque diferente, desde una visión holística, como un sistema en su conjunto donde cada edificio sea una caja negra caracterizada con un parámetro adimensional que permita diferenciar edificios distintos, siendo el edificio la variable independiente del sistema mientras que la relación ``tiempo de evacuación entre personas evacuadas'' es la variable dependiente. De esta manera se pueden comparar las evacuaciones de diferentes edificios. Una gran dificultad que tiene el estudio e investigación sobre las evacuaciones es recopilar datos suficientes, tanto cuantitativos como cualitativos, de las mismas. En esta tesis se han recogido los datos de simulacros de evacuación de la Universidad de Valladolid durante 10 años. Además se propone un sistema, basado en localización indoor, para facilitar la recogida de datos futuros, válidos para alimentar tanto al modelo propuesto por esta tesis, como para validar otros modelos de estudio. Esta tesis ofrece un planteamiento novedoso para poder comparar las evacuaciones de edificios diferentes, teniendo en cuenta sus características más relevantes en la evacuación, lo cual, hasta ahora, no había sido posible. El planteamiento teórico se ve respaldado por los datos históricos recogidos y por los datos publicados por otros autores. Además, propone una solución viable para la recopilación de más datos de evacuaciones valiéndose de tecnologías de posicionamiento en interiores.Escuela de DoctoradoDoctorado en Ingeniería Industria

Indoor location systems in emergency scenarios - A Survey

Indoor location data are critical in emergency situations. Command centers need to monitor their operational forces. Rescuers need to find potential victims to carry proper care and the building’s occupants need to find the way for fast evacuation. Despite the growing body of research in indoor location, no technique is considered appropriate for different situations. Furthermore, few studies have analyzed the applicability of these techniques in an emergency setting, which has particular characteristics. This survey reviews works in indoor location applied to emergency scenarios, analyzing their applicability in relation to existing requirements in these types of situations

Victim Detection and Localization in Emergencies

Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.This work was performed in the framework of the Horizon 2020 project LOCUS (Grant Agreement Number 871249), receiving funds from the European Union. This work was also partially funded by Junta de Andalucia (Project PY18-4647:PENTA)

Lost in the City? - A Scoping Review of 5G Enabled Location-Based Urban Scenarios

5G mobile network technologies and scenarios with the associated innovations receive growing interest among academics and practitioners. Current literature on 5G technologies discusses several scenarios and specific chances and challenges. However, 5G literature is fragmented and not systematically reviewed. We conducted a scoping review on 5G applications in urban scenarios. We reviewed 1,394 papers and identified 20 studies about urban logistics and emergency indoor localization. Our review accumulates current academic knowledge on these scenarios and identifies six further research directions in four research fields. It reveals several further research opportunities, e.g., regarding trust and privacy concerns. We review and discuss 5G literature for academics and practitioners, contribute towards more tailored 5G research and reflect on cost- efficient 5G applications in urban scenarios

Application of Integer Programming for Mine Evacuation Modeling with Multiple Transportation Modes

The safe evacuation of miners during an emergency within the shortest possible time is very important for the success of a mine evacuation program. Despite developments in the field of mine evacuation, little research has been done on the use of mine vehicles during evacuation. Current research into mine evacuation has emphasized on miner evacuation by foot. Mathematical formulations such as Minimum Cost Network Flow (MCNF) models, Ant Colony algorithms, and shortest path algorithms including Dijkstra's algorithm and Floyd-Warshall algorithm have been used to achieve this. These models, which concentrate on determining the shortest escape routes during evacuation, have been found to be computationally expensive with expanding problem sizes and parameter ranges or they may not offer the best possible solutions.An ideal evacuation route for each miner must be determined considering the available mine vehicles, locations of miners, safe havens such as refuge chambers, and fresh-air bases. This research sought to minimize the total evacuation cost as a function of the evacuation time required during an emergency while simultaneously helping to reduce the risk of exposure of the miners to harmful conditions during the evacuation by leveraging the use of available mine vehicles. A case study on the Turquoise Ridge Underground Mine (Nevada Gold Mines) was conducted to validate the Integer Programming (IP) model. Statistical analysis of the IP model in comparison with a benchmark MCNF model proved that leveraging the use of mine vehicles during an emergency can further reduce the total evacuation time. A cost-savings analysis was made for the IP model, and it was found that the time saved during evacuation, by utilizing the IP model, increased linearly, with an increase in the number of miners present at the time of evacuation

IoT based Intelligent Fire Escape System

This implementation paper is regarding an intelligent system which helps user to get out of the building very safely, thereby reducing human casualties. It helps user by sending appropriate maps which contain navigation that helps user to escape safely. The introduction contains the modules required to implement this intelligent fire escape system. Proposed system contains the system that we have put forth. Results contain the screesnhots and actual images of implementation. This provides flow control by assigning different routes to different users

Navigating MazeMap: indoor human mobility, spatio-logical ties and future potential

Global navigation systems and location-based services have found their way into our daily lives. Recently, indoor positioning techniques have also been proposed, and there are several live or trial systems already operating. In this paper, we present insights from MazeMap, the first live indoor/outdoor positioning and navigation system deployed at a large university campus in Norway. Our main contribution is a measurement case study; we show the spatial and temporal distribution of MazeMap geo-location and wayfinding requests, construct the aggregated human mobility map of the campus and find strong logical ties between different locations. On one hand, our findings are specific to the venue; on the other hand, the nature of available data and insights coupled with our discussion on potential usage scenarios for indoor positioning and location-based services predict a successful future for these systems and applications.Comment: 6 pages, accepted at PerMoby Workshop at IEEE PerCom 201

IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI

Indoor localization is the process of determining the location of a person or object inside a building. Potential usage of indoor localization includes navigation, personalization, safety and security, and asset tracking. Commonly used technologies for indoor localization include WiFi, Bluetooth, RFID, and Ultra-wideband. Among these, WiFi's Received Signal Strength Indicator (RSSI)-based localization is preferred because of widely available WiFi Access Points (APs). We have two main contributions. First, we develop our method, 'IndoorGNN' which involves using a Graph Neural Network (GNN) based algorithm in a supervised manner to classify a specific location into a particular region based on the RSSI values collected at that location. Most of the ML algorithms that perform this classification require a large number of labeled data points (RSSI vectors with location information). Collecting such data points is a labor-intensive and time-consuming task. To overcome this challenge, as our second contribution, we demonstrate the performance of IndoorGNN on the restricted dataset. It shows a comparable prediction accuracy to that of the complete dataset. We performed experiments on the UJIIndoorLoc and MNAV datasets, which are real-world standard indoor localization datasets. Our experiments show that IndoorGNN gives better location prediction accuracies when compared with state-of-the-art existing conventional as well as GNN-based methods for this same task. It continues to outperform these algorithms even with restricted datasets. It is noteworthy that its performance does not decrease a lot with a decrease in the number of available data points. Our method can be utilized for navigation and wayfinding in complex indoor environments, asset tracking and building management, enhancing mobile applications with location-based services, and improving safety and security during emergencies

SafePASS : a new chapter for passenger ship evacuation and marine emergency response

Despite the current high level of safety and the efforts to make passenger ships resilient to most fire and flooding scenarios, there are still gaps and challenges in the marine emergency response and ship evacuation processes. Those challenges arise from the fact that both processes are complex, multi-variable problems that rely on parameters involving not only people and technology but also procedural and managerial issues. SafePASS Project, funded under EU's Horizon 2020 Research and Innovation Programme, is set to radically redefine the evacuation processes by introducing new equipment, expanding the capabilities of legacy systems on-board, proposing new Life-Saving Appliances and ship layouts, and challenging the current international regulations, hence reducing the uncertainty, and increasing the efficiency in all the stages of ship evacuation and abandonment process