Towards a privacy aware information system for emergency response

Intelligent Systems in Smart Cities capture and exchange a large variety of information, for example, environmental data, location, biometric and personal data, health records, among others, in order to improve the quality of services. On one hand, such systems could represent an important, lifesaving resource for public services aimed at addressing emergency situations (e.g. firefighters, police), by providing access to a large amount of diverse information. On the other hand, they are also a threat with respect to data protection and privacy when disclosing all sort of personal and sensitive information. Since not all the information available can be used or helpful for handling the emergency we have the challenge of ensuring that the least possible amount of sensitive information is exchanged, therefore reducing the risk of unwanted disclosure and misuse. Thus, being aware and include a privacy-by design approach when managing personal and sensitive data is essential in the context of emergency systems. This work aims to analyse the privacy issues that Intelligent Systems face when sharing information with public services to attend emergency situations. By characterising these issues, we aim of informing the knowledge requirements for designing an Intelligent System that only allows valuable and helpful information to be exchanged, minimising personal and sensitive data disclosure

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT

Indoor Localization for Fire Safety : A brief overview of fundamentals, needs and requirements and applications

An indoor localization system for positioning evacuating people can be anticipated to increase the chances of a safe evacuation and effective rescue intervention in case of a tunnel fire. Such a system may utilize prevalent wireless technologies, e.g., Bluetooth, RFID and Wi-Fi, which today are used to survey incoming and outgoing traffic to a certain space or location, to estimate group sizes and to measure the duration of visits during normal operation of buildings. Examples also exist of where the same wireless technologies are used for safety purposes, for example to assess real-time location, tracking and monitoring of vehicles, personnel and equipment in mining environments. However, they are relatively few, and typically rely on a high degree of control over the people that are to be tracked, and their association with (connection to) the localization system used for the tracking. In this report, the results of a brief overview of the literature within the field of indoor localization in general, and the application of indoor localization systems within the field of particularly fire safety, is summarized. This information forms the underlying basis for the planning and execution of a future field study, in which an indoor Wi-Fi localization system will be tested and evaluated in terms of if, and if so how, it can be used to position evacuating people in tunnels. Whereas such a system allows digital footprints to be collected within a wireless network infrastructure (also already existing ones), questions remains to be answered regarding aspects such as precision and accuracy, and furthermore, how these aspects are affected by other independent variables. In the end of this report, examples of research questions deemed necessary to answer in order to enable a sound evaluation of the system is presented. These need to be addressed in the future planning of the above-mentioned field study

Highly-efficient fog-based deep learning AAL fall detection system

[EN] Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient Assisted Living (AAL) solutions that rely on the technologies of the Internet of Things (IoT), Cloud Computing and Machine Learning (ML). Recently, Deep Learning (DL) have been included for its high potential to improve accuracy on fall detection. Also, the use of fog devices for the ML inference (detecting falls) spares cloud drawback of high network latency, non-appropriate for delay-sensitive applications such as fall detectors. Though, current fall detection systems lack DL inference on the fog, and there is no evidence of it in real environments, nor documentation regarding the complex challenge of the deployment. Since DL requires considerable resources and fog nodes are resource-limited, a very efficient deployment and resource usage is critical. We present an innovative highly-efficient intelligent system based on a fog-cloud computing architecture to timely detect falls using DL technics deployed on resource-constrained devices (fog nodes). We employ a wearable tri-axial accelerometer to collect patient monitoring data. In the fog, we propose a smart-IoT-Gateway architecture to support the remote deployment and management of DL models. We deploy two DL models (LSTM/GRU) employing virtualization to optimize resources and evaluate their performance and inference time. The results prove the effectiveness of our fall system, that provides a more timely and accurate response than traditional fall detector systems, higher efficiency, 98.75% accuracy, lower delay, and service improvement.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's Horizon 2020 research and innovation program as part of the ACTIVAGE project under Grant 732679.Sarabia-Jácome, D.; Usach, R.; Palau Salvador, CE.; Esteve Domingo, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet of Things. 11:1-19. https://doi.org/10.1016/j.iot.2020.100185S11911“World Population Ageing.” [Online]. Available: http://www.un.org/esa/population/publications/worldageing19502050/. [Accessed: 23-Sep-2018].“Falls, ” World Health Organization. [Online]. Available: http://www.who.int/news-room/fact-sheets/detail/falls. [Accessed: 20-Sep-2018].Rashidi, P., & Mihailidis, A. (2013). A Survey on Ambient-Assisted Living Tools for Older Adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579-590. doi:10.1109/jbhi.2012.2234129Bousquet, J., Kuh, D., Bewick, M., Strandberg, T., Farrell, J., Pengelly, R., … Bringer, J. (2015). Operative definition of active and healthy ageing (AHA): Meeting report. Montpellier October 20–21, 2014. European Geriatric Medicine, 6(2), 196-200. doi:10.1016/j.eurger.2014.12.006“WHO | What is Healthy Ageing?”[Online]. Available: http://www.who.int/ageing/healthy-ageing/en/. [Accessed: 19-Sep-2018].Fei, X., Shah, N., Verba, N., Chao, K.-M., Sanchez-Anguix, V., Lewandowski, J., … Usman, Z. (2019). CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Generation Computer Systems, 90, 435-450. doi:10.1016/j.future.2018.06.042W. Zaremba, “Recurrent neural network regularization,” no. 2013, pp. 1–8, 2015.Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” pp. 1–9, 2014.N. Zerrouki, F. Harrou, Y. Sun, and A. Houacine, “Vision-based human action classification,” vol. 18, no. 12, pp. 5115–5121, 2018.Panahi, L., & Ghods, V. (2018). Human fall detection using machine vision techniques on RGB–D images. Biomedical Signal Processing and Control, 44, 146-153. doi:10.1016/j.bspc.2018.04.014Y. Li, K.C. Ho, and M. Popescu, “A microphone array system for automatic fall detection,” vol. 59, no. 2, pp. 1291–1301, 2012.Taramasco, C., Rodenas, T., Martinez, F., Fuentes, P., Munoz, R., Olivares, R., … Demongeot, J. (2018). A Novel Monitoring System for Fall Detection in Older People. IEEE Access, 6, 43563-43574. doi:10.1109/access.2018.2861331C. Wang et al., “Low-power fall detector using triaxial accelerometry and barometric pressure sensing,” vol. 12, no. 6, pp. 2302–2311, 2016.S.B. Khojasteh and E. De Cal, “Improving fall detection using an on-wrist wearable accelerometer,” pp. 1–28.Theodoridis, T., Solachidis, V., Vretos, N., & Daras, P. (2017). Human Fall Detection from Acceleration Measurements Using a Recurrent Neural Network. IFMBE Proceedings, 145-149. doi:10.1007/978-981-10-7419-6_25F. Sposaro and G. Tyson, “iFall : an android application for fall monitoring and response,” pp. 6119–6122, 2009.A. Ngu, Y. Wu, H. Zare, A.P. B, B. Yarbrough, and L. Yao, “Fall detection using smartwatch sensor data with accessor architecture,” vol. 2, pp. 81–93.P. Jantaraprim and P. Phukpattaranont, “Fall detection for the elderly using a support vector machine,” no. 1, pp. 484–490, 2012.Aziz, O., Musngi, M., Park, E. J., Mori, G., & Robinovitch, S. N. (2016). A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical & Biological Engineering & Computing, 55(1), 45-55. doi:10.1007/s11517-016-1504-yV. Carletti, A. Greco, A. Saggese, and M. Vento, “A smartphone-based system for detecting falls using anomaly detection,” vol. 6978, 2017, pp. 490–499.Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2018). Fall detection system for elderly people using IoT and Big Data. Procedia Computer Science, 130, 603-610. doi:10.1016/j.procs.2018.04.11

Context-aware information in mobile devices

This paper describes a novel approach for indoor location and integration of other services in a university campus using Bluetooth Low Energy (BLE) devices. These BLE devices broadcast a Bluetooth signal in a limited and configured range/area, thus functioning as beacons that provide useful context-aware information to nearby devices operating with custom applications. Such applications can interpret the received signals as location and provide a range of useful services to the end user (students, events attendees), namely, indoor location and navigation or personalized complex workflows that require the interaction of the end user with multiple services within the university campus.info:eu-repo/semantics/acceptedVersio

A Survey on Multihop Ad Hoc Networks for Disaster Response Scenarios

Disastrous events are one of the most challenging applications of multihop ad hoc networks due to possible damages of existing telecommunication infrastructure.The deployed cellular communication infrastructure might be partially or completely destroyed after a natural disaster. Multihop ad hoc communication is an interesting alternative to deal with the lack of communications in disaster scenarios. They have evolved since their origin, leading to differentad hoc paradigms such as MANETs, VANETs, DTNs, or WSNs.This paper presents a survey on multihop ad hoc network paradigms for disaster scenarios.It highlights their applicability to important tasks in disaster relief operations. More specifically, the paper reviews the main work found in the literature, which employed ad hoc networks in disaster scenarios.In addition, it discusses the open challenges and the future research directions for each different ad hoc paradigm

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution