Gated Recurrent Units for Blockage Mitigation in mmWave Wireless

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To address the problem, we developed a Gated Recurrent Unit (GRU) model that is trained using periodically exchanged messages in mmWave systems. We gathered extensive amount of simulation data from a commercially available mmWave simulator, show that the proposed method does not incur any additional communication overhead, and that it achieves outstanding results in selecting the optimal blockage mitigation method with an accuracy higher than 93%. We also show that the proposed method significantly increases the amount of transferred data compared to several other blockage mitigation policies

Overløpskontroll i avløpsnett med forskjellige modelleringsteknikker og internet of things

Increased urbanization and extreme rainfall events are causing more frequent instances of sewer overflow, leading to the pollution of water resources and negative environmental, health, and fiscal impacts. At the same time, the treatment capacity of wastewater treatment plants is seriously affected. The main aim of this Ph.D. thesis is to use the Internet of Things and various modeling techniques to investigate the use of real-time control on existing sewer systems to mitigate overflow. The role of the Internet of Things is to provide continuous monitoring and real-time control of sewer systems. Data collected by the Internet of Things are also useful for model development and calibration. Models are useful for various purposes in real-time control, and they can be distinguished as those suitable for simulation and those suitable for prediction. Models that are suitable for a simulation, which describes the important phenomena of a system in a deterministic way, are useful for developing and analyzing different control strategies. Meanwhile, models suitable for prediction are usually employed to predict future system states. They use measurement information about the system and must have a high computational speed. To demonstrate how real-time control can be used to manage sewer systems, a case study was conducted for this thesis in Drammen, Norway. In this study, a hydraulic model was used as a model suitable for simulation to test the feasibility of different control strategies. Considering the recent advances in artificial intelligence and the large amount of data collected through the Internet of Things, the study also explored the possibility of using artificial intelligence as a model suitable for prediction. A summary of the results of this work is presented through five papers. Paper I demonstrates that one mainstream artificial intelligence technique, long short-term memory, can precisely predict the time series data from the Internet of Things. Indeed, the Internet of Things and long short-term memory can be powerful tools for sewer system managers or engineers, who can take advantage of real-time data and predictions to improve decision-making. In Paper II, a hydraulic model and artificial intelligence are used to investigate an optimal in-line storage control strategy that uses the temporal storage volumes in pipes to reduce overflow. Simulation results indicate that during heavy rainfall events, the response behavior of the sewer system differs with respect to location. Overflows at a wastewater treatment plant under different control scenarios were simulated and compared. The results from the hydraulic model show that overflows were reduced dramatically through the intentional control of pipes with in-line storage capacity. To determine available in-line storage capacity, recurrent neural networks were employed to predict the upcoming flow coming into the pipes that were to be controlled. Paper III and Paper IV describe a novel inter-catchment wastewater transfer solution. The inter-catchment wastewater transfer method aims at redistributing spatially mismatched sewer flows by transferring wastewater from a wastewater treatment plant to its neighboring catchment. In Paper III, the hydraulic behaviors of the sewer system under different control scenarios are assessed using the hydraulic model. Based on the simulations, inter-catchment wastewater transfer could efficiently reduce total overflow from a sewer system and wastewater treatment plant. Artificial intelligence was used to predict inflow to the wastewater treatment plant to improve inter-catchment wastewater transfer functioning. The results from Paper IV indicate that inter-catchment wastewater transfer might result in an extra burden for a pump station. To enhance the operation of the pump station, long short-term memory was employed to provide multi-step-ahead water level predictions. Paper V proposes a DeepCSO model based on large and high-resolution sensors and multi-task learning techniques. Experiments demonstrated that the multi-task approach is generally better than single-task approaches. Furthermore, the gated recurrent unit and long short-term memory-based multi-task learning models are especially suitable for capturing the temporal and spatial evolution of combined sewer overflow events and are superior to other methods. The DeepCSO model could help guide the real-time operation of sewer systems at a citywide level.publishedVersio

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

AI-based smart sensing and AR for gait rehabilitation assessment

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.info:eu-repo/semantics/publishedVersio

Adversarial robustness of deep learning enabled industry 4.0 prognostics

The advent of Industry 4.0 in automation and data exchange leads us toward a constant evolution in smart manufacturing environments, including extensive utilization of Internet-of-Things (IoT) and Deep Learning (DL). Specifically, the state-of-the-art Prognostics and Health Management (PHM) has shown great success in achieving a competitive edge in Industry 4.0 by reducing maintenance cost, downtime, and increasing productivity by making data-driven informed decisions. These state-of-the-art PHM systems employ IoT device data and DL algorithms to make informed decisions/predictions of Remaining Useful Life (RUL). Unfortunately, IoT sensors and DL algorithms, both are prone to cyber-attacks. For instance, deep learning algorithms are known for their susceptibility to adversarial examples. Such adversarial attacks have been extensively studied in the computer vision domain. However, it is surprising that their impact on the PHM domain is yet not explored. Thus, modern data-driven intelligent PHM systems pose a significant threat to safety- and cost-critical applications. Towards this, in this thesis, we propose a methodology to design adversarially robust PHM systems by analyzing the effect of different types of adversarial attacks on several DL enabled PHM models. More specifically, we craft adversarial attacks using Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) and evaluate their impact on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Bi-directional LSTM, and Multi-layer perceptron (MLP) based PHM models using the proposed methodology. The obtained results using NASA's turbofan engine, and a well-known battery PHM dataset show that these systems are vulnerable to adversarial attacks and can cause a serious defect in the RUL prediction. We also analyze the impact of adversarial training using the proposed methodology to enhance the adversarial robustness of the PHM systems. The obtained results show that adversarial training is successful in significantly improvising the robustness of these PHM models.Includes bibliographical references (pages 80-98)

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie