Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Applications of 5G Communications in Civil Protection

Τα δίκτυα πέμπτης γενιάς θεωρούνται ευρέως ως μία από τις πιο θεμελιώδεις τεχνολογικές εξελίξεις του τρέχοντος αιώνα, προσφέροντας υψηλή ταχύτητα, χαμηλή καθυστέρηση και κλιμάκωση. Τα επόμενα χρόνια, τα δίκτυα πέμπτης γενιάς αναμένεται να δημιουργήσουν τη χωρητικότητα, την απόδοση και την ευελιξία του ασύρματου δικτύου για να υποστηρίξουν μια εκρηκτική αύξηση στις συνδεδεμένες συσκευές, μαζί με πρωτοποριακές εφαρμογές. Αυτή η καινοτόμος νέα τεχνολογία μπορεί να βελτιώσει όλο το φάσμα της καθημερινής ζωής από την υγεία στην ψυχαγωγία και από τη γεωργία στην πολιτική προστασία. Οι κρίσιμες επικοινωνίες, ο ακρογωνιαίος λίθος της Πολιτικής Προστασίας, θα επωφεληθούν σε μεγάλο βαθμό από το 5G. Η παρούσα εργασία μελετά πώς νέα στοιχεία και τεχνολογίες του 5G όπως η επαυξημένη πραγματικότητα, η ηλεκτρονική υγεία και η βελτιστοποιημένη δρομολόγηση ασθενοφόρων μπορούν να υποστηρίξουν την Πολιτική Προστασία ενισχύοντας παράλληλα το περιβάλλον και την οικονομία.5G networks are widely considered as one of the most fundamental technology developments of our century, providing ultra-high-speed, low-latency and scalability. Over the coming years, 5G is expected to create the wireless network capacity, performance and flexibility to support an explosive increase in connected devices, along with exciting new use cases. This innovative technology can improve the whole spectrum of everyday life from health to entertainment and from agriculture to civil protection. Mission critical Communications, the cornerstone of civil protection, are to be greatly impacted by 5G. This thesis studies how new 5G components and technologies such as augmented reality, ehealth and optimized routing of ambulances are able to support the role of civil protection while enhancing the protection of the environment and the economy

Formulating earthquake response modes in Iran

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 50-51).The objective of this paper was to try and find the optimal distribution of rescuers after an earthquake with a very large magnitude caused major damage in two different cities. A model was developed to optimally divide all of the available rescuer workers such that the expected number of lives saved was maximized. When the method was tested on random sets of data on average a 5% improvement in lives saved was found. However it was also determined that there was a positive relationship between percent improvement and severity of the earthquake. This shows that the method is especially effective when extreme amounts of damage occur.by Michael D. Metzger.M.Eng

Collab-SAR:A Collaborative Avalanche Search-and-Rescue Missions Exploiting Hostile Alpine Networks

Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time- and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR, is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal

Indoor And Outdoor Real Time Information Collection in Disaster Scenario

A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario. The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers. The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A­* is also proposed

Fog Computing Architecture for Indoor Disaster Management

Most people spend their time indoors. Indoors have a higher complexity than outdoors. Moreover, today's building structures are increasingly sophisticated and complex, which can create problems when a disaster occurs in the room. Fire is one of the disasters that often occurs in a building. For that, we need disaster management that can minimize the risk of casualties. Disaster management with cloud computing has been extensively investigated in other studies. Traditional ways of centralizing data in the cloud are almost scalable as they cannot cater to many latency-critical IoT applications, and this results in too high network traffic when the number of objects and services increased. It will be especially problematic when in a disaster that requires a quick response. The Fog infrastructure is the beginning of the answer to such problems. This research started with an analysis of literature and hot topics related to fog computing and indoor disasters, which later became the basis for creating a fog computing-based architecture for indoor disasters. In this research, fog computing is used as the backbone in disaster management architecture in buildings. MQTT is used as a messaging protocol with the advantages of simplicity and speed. This research proposes a disaster architecture for indoor disasters, mainly fire disasters

Opportunistic communication schemes for unmanned vehicles in urban search and rescue

In urban search and rescue (USAR) operations, there is a considerable amount of danger faced by rescuers. The use of mobile robots can alleviate this issue. Coordinating the search effort is made more difficult by the communication issues typically faced in these environments, such that communication is often restricted. With small numbers of robots, it is necessary to break communication links in order to explore the entire environment. The robots can be viewed as a broken ad hoc network, relying on opportunistic contact in order to share data. In order to minimise overheads when exchanging data, a novel algorithm for data exchange has been created which maintains the propagation speed of flooding while reducing overheads. Since the rescue workers outside of the structure need to know the location of any victims, the task of finding their locations is two parted: 1) to locate the victims (Search Time), and 2) to get this data outside the structure (Delay Time). Communication with the outside is assumed to be performed by a static robot designated as the Command Station. Since it is unlikely that there will be sufficient robots to provide full communications coverage of the area, robots that discover victims are faced with the difficult decision of whether they should continue searching or return with the victim data. We investigate a variety of search techniques and see how the application of biological foraging models can help to streamline the search process, while we have also implemented an opportunistic network to ensure that data are shared whenever robots come within line of sight of each other or the Command Station. We examine this trade-off between performing a search and communicating the results

Case studies of communications systems during harsh environments: A review of approaches, weaknesses, and limitations to improve quality of service

The failure of communications systems may cause catastrophic damage to human life and economic activities as people are unable to communicate with each other in a timely manner and with a convenient quality of service. Therefore, the exchange of information is more than necessary for people in their everyday life or during harsh environments to prevent the death and injury of thousands of individuals. The study of communications systems behavior in harsh environments helps to design or select more resilient technologies that are capable of operating in challenging conditions. This article reviews existing approaches, major causes of failure, and weaknesses of communications systems during extreme events. First, we highlight the importance of communications systems, and then we examine related works, how communication may fail, and the effect of this failure on human life in general and during extreme events response. Furthermore, we study and analyze how communications are used during various stages of extreme events, and we identify the main weaknesses and limitations that communications systems may suffer based on many case studies. To conclude, we identify and discuss relevant attributes, requirements, and recommendations for communications systems to perform with a suitable quality of service during harsh environments and to reduce risks of communication failure in challenging conditions

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Department of Computer Science and EngineeringA large-scale disaster such as earthquakes and tsunami can cause billion-dollar destruction to a city and kill many people. To mitigate the dead troll, fast disaster response to rescue survivors in a disaster zone is of paramount importance. However, it is difficult to find the location of the injured people in a disaster zone due to the debris and smoke in collapsed buildings as well as the disruption of communication networks. This can cause poor decisions of the disaster response team about where to deploy the rescue personnel and allocate the resource. Therefore, we propose to develop an AI system to predict the location of injured people in a disaster area. In this research, our system has three major parts: (1) the prediction of the density of injured people in a gridand (2) the strategy of the rescue team to search for injured peopleand (3) the deployment the rescue team to search the location of the most density injured people area according to the first and second part. In the first part, we developed a deep learning software package that consists of state-of-the-art deep learning techniques such as attention module and data annotation to predict the density of injured civilians. Our work uses a disaster simulator called RoboCup Rescue Simulation (RCRS). To predict the density of injured people in RCRS, we train the machine learning model using the two cases of the image data: (1) single image frame such as a satellite imageand (2) multiple image sequence frame such as disaster video clip. Furthermore, we evaluate our ML model in the other two domains: (1) the prediction of the location of crime in Chicagoand (2) the prediction of the location of RSNA Pneumonia. In the second part, we propose the Treasure Hunt Problem. In RCRS, the rescue team has to search more than one injured people and it is a complicated multi-agent problem. Therefore, study a simpler problem called the Treasure Hunt Problem, in which there is only one rescue crew search the only one injured civilian. In this problem, we assume that the location of the treasure is determined based on the probability distribution, and the ML model predicts the distribution of probability that treasure exists for each coordinate within the map. To solve this problem, we propose two search strategies that makes use of the ML model to improve the effectiveness of a search mission: (1) the probabilistic greedy search that the hunter searches preferentially for the cell with the highest probability of existing treasure given by ML modeland (2) the probabilistically admissible heuristic A* search that the hunter searches the cell determined by heuristic A* search with the probability of existing treasure given by ML model. In the last part, we merge the first and second parts to search for the location of the most density injured people area. To predict the location, we predict the number of injured people with several ML models used in the first part and we convert the injured people density predicted to the probability distribution. And the rescue team search the most density injured people area according to the search strategy of the second part based on this probability distributionclos