This paper presents a hybrid wearable sensor network system towards the Internet of Things (IoT) connected safety and health monitoring applications. The system is aimed at improving safety in the outdoor workplace. The proposed system consists of a wearable body area network (WBAN) to collect user data and a low-power wide-area network (LPWAN) to connect the WBAN with the Internet. The wearable sensors in the WBAN are exerted to measure the environmental conditions around the subject using a Safe Node and monitor the vital signs of the subject using a Health Node. A standalone local server (gateway), which can process the raw sensor signals, display the environmental and physiological data, and trigger an alert if any emergency circumstance is detected, is designed within the proposed network. To connect the gateway with the Internet, an IoT cloud server is implemented to provide more functionalities, such as web monitoring and mobile applications.

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An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications

IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art

Recently, LoRaWAN has emerged as a promising technology for the Internet of Things (IoT), owing its ability to support low-power and long-range communications. However, real-world deployment and network optimization require accurate path-loss (PL) modeling, so as to estimate network coverage, performance, and profitability. For that reason, in this paper, LoRaWAN radio channel is investigated in the 868 MHz band. Extensive measurement campaigns were carried out in both indoor and outdoor environments at urban and rural locations in Lebanon (Saint Joseph University of Beirut campus, Beirut city, and Bekaa valley). Based on empirical results, PL models are developed for LoRaWAN communications and compared with widely used empirical models. Moreover, the performance and the coverage of LoRaWAN deployment are evaluated based on real measurements. The results show that the proposed PL models are accurate and simple to be applied in Lebanon and other similar locations. Coverage ranges up to 8 and 45 km were obtained in urban and rural areas, respectively. This reveals the reliability of this promising technology for long-range IoT communications.

LoRaWAN Network: Radio Propagation Models and Performance Evaluation in Various Environments in Lebanon

This paper presents an advanced Internet of Things point-of-care bio-fluid analyzer; a LoRa/Bluetooth-enabled electronic reader for biomedical strip-based diagnostics system for personalized monitoring. We undertake test simulations (technology trial without patient subjects) to demonstrate potential of long-range analysis, using a disposable test ‘key’ and companion Android app to form a diagnostic platform suitable for remote point-of-care screening for urinary tract infection (UTI). The 868 MHz LoRaWAN-enabled personalized monitor demonstrated sound potential with UTI test results being correctly diagnosed and transmitted to a remote secure cloud server in every case. Tests ranged over distances of 1.1–6.0 Km with radio path losses from 119–141 dB. All tests conducted were correctly and robustly received at the base station and relayed to the secure server for inspection. The UTI test strips were visually inspected for correct diagnosis based on color change and verified as 100% accurate. Results from testing across a number of regions indicate that such an Internet of Things medical solution is a robust and simple way to deliver next generation community-based smart diagnostics and disease management to best benefit patients and clinical staff alike. This significant step can be applied to any type of home or region, particularly those lacking suitable mobile signals, broadband connections, or even landlines. It brings subscription-free long-range bio-telemetry to healthcare providers and offers savings on regular clinician home visits or frequent clinic visits by the chronically ill. This paper highlights practical hurdles in establishing an Internet of Medical Things network, assisting informed deployment of similar future systems.

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A Community-Based IoT Personalized Wireless Healthcare Solution Trial

Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.

A Survey on LoRaWAN Architecture, Protocol and Technologies

In this paper, we present a new loT-based health monitoring approach in which collected medical sensor data is sent to an analysis module via low-cost, low-power and secure communication links provided by a LoRaWAN network infrastructure. We mainly focus on monitoring blood pressure, glucose and temperature in rural areas where cellular network coverage is either absent or does not allow data transmission. The main objective is to reduce the burden of long trips for people living in these areas to visit healthcare facilities, while minimizing the communication cost. Several experiments have been conducted to evaluate the area covered by the LoRa network and the power consumption of our system. The results indicate that the average area covered is around 33 km2 when the LoRa Gateway is placed outdoor on a 12 meter altitude. Moreover, the results demonstrate that the power consumption of our monitoring system is at least ten times lower than other long range cellular solutions, such as GPRS/3G/4G.

IoT-based health monitoring via LoRaWAN

Despite a great deal of progress in face recognition technologies, current solutions are still vulnerable to spoof attacks. In fact, it is easy to access digital replicas of facial biometric information from readily available photos, videos and 3D masks. The literature contains several face anti spoofing methods that try to detect whether the face in the front of the recognition system is real or an artificial replica. However, these methods are not robust and require many improvements since they are sensitive to lightening conditions and pose variations. In order to address these issues, we propose a novel face anti spoofing method based on Multi Color Convolutional Neural Network (CNN) architecture named DeepColorFASD. Our approach investigates the effect of space colors (RGB, HSV and Y CbCr) on CNN architectures and proposes a fusion based voting method for face anti spoofing. In addition, we also explain the resulting feature maps visualizations. We evaluate our system through an experimental study using CASIA FASD: a well-known face anti spoofing database. The results using this challenging database demonstrate that our solution performs better than recent works as measured by Half Total Error Rate (HTER) and ROC curve.

Kaouthar Larbi

Papers

IoT-based health monitoring via LoRaWAN

DeepColorFASD: Face Anti Spoofing Solution Using a Multi Channeled Color Spaces CNN