IET Wireless Sensor Systems最新文献

The use of inexpensive, lightweight, and portable particulate matter (PM) sensors is increasingly becoming popular in air quality monitoring applications. As an example, these low-cost sensors can be used in surface or underground coal mines for monitoring of inhalable dust, and monitoring of inhalable particles in real-time can be beneficial as it can possibly assist in preventing coal mine related respiratory diseases such as black lung disease. However, commercially available PM sensors are not inherently calibrated, and as a result, they have vague and unclear measurement accuracy. Therefore, they must initially be evaluated and compared with standardised instruments to be ready to be deployed in the fields. In this study, three different types of inexpensive, light-scattering-based widely available PM sensors (Shinyei PPD42NS, Sharp GP2Y1010AU0F, and Laser SEN0177) are evaluated and calibrated with reference instruments. PM sensors are compared with reference instruments in a controlled environment. The calibration is done by means of different machine learning techniques. The results demonstrate that the calibrated response obtained by fusion of sensors has a higher accuracy in comparison to the calibrated response of each individual sensor.

In recent years, research on sleep monitoring and analysis has attracted many scholars. Among them, the polysomnography (PSG) is performed more accurately. However, PSG is not suitable to be used at home due to its complicated operation and expensive cost. On the other hand, although the Pittsburgh sleep quality index (PSQI) is a standardized form for sleep quality assessment, the subjective and backward evaluation may lead to intuitive results. Therefore, this paper is intended to develop a sleep stage estimation system for home health care services. In the proposed platform, the sleep conditions, including the heart rate (HR) and body movement, are collected by an HR monitor and a force sensor array, respectively. Also, the fuzzy inference system is applied to the sleep depth evaluation, and then, the finite state machine is utilised to estimate the sleep stage. Experimental results show that the developed platform not only reduces the burden of PSG measurements, but also provides more convincible and reasonable results, presenting as an assistive tool of the conventional PSQI tests.

Long Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands of actively connected devices (nodes) to monitor the environment or processes. In most cases, the location information of the devices arguably plays a critical role and is desirable. In this regard, the physical characteristics of the communication channel can be leveraged to provide a feasible and affordable node localisation solution. This paper presents an evaluation of the performance of LoRaWAN Received Signal Strength Indicator (RSSI)-based node localisation in a sandstorm environment. The authors employ machine learning algorithms, Support Vector Regression and Gaussian Process Regression, which turn the high variance of RSSI due to frequency hopping feature of LoRaWAN to advantage, creating unique signatures representing different locations. In this work, the RSSI features are used as input location fingerprints into the machine learning models. The proposed method reduces node localisation complexity when compared to GPS-based approaches whilst provisioning more extensive connection paths. Furthermore, the impact of LoRa spreading factor and kernel function on the performance of the developed models have been studied. Experimental results show that the SVR-enhanced fingerprint yields the most significant improvement in node localisation performance.