The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

Predict Daily Life Stress based on Heart Rate Variability

Department of Human Factors EngineeringThe purpose of this study is to investigate the feasibility of predicting a daily mental stress level from analyzing Heart Rate Variability (HRV) by using a Photoplethysmography (PPG) sensor which is integrated in the wristband-type wearable device. In this experiment, each participant was asked to measure their own PPG signals for 30 seconds, three times a day (at noon, 6 P.M, and 10 minutes before going to sleep) for a week. And 10 minutes before going to sleep, all participants were asked to self-evaluate their own daily mental stress level using Perceived Stress Scale (PSS). The recorded signals were transmitted and stored at each participant???s smartphone via Bluetooth Low Energy (BLE) communication by own-made mobile application. The preprocessing procedure was used to remove PPG signal artifacts in order to make better performance for detecting each pulse peak point at PPG signal. In this preprocessing, three- level-bandpass filtering which consisted three different pass band range bandpass filters was used. In this study, frequency domain HRV analysis feature that the ratio of low-frequency (0.04Hz ~ 0.15Hz) to high-frequency (0.15Hz ~ 0.4Hz) power value was used. In frequency domain analysis, autoregressive (AR) model was used, because this model has higher resolution than that of Fast Fourier Transform (FFT). The accuracy of this prediction was 86.35% on average of all participants. Prediction result was calculated from the leave-one-out validation. The IoT home appliances are arranged according to the result of this prediction algorithm. This arrangement is offering optimized user???s relaxation. Also, this algorithm can help acute stress disorder patients to concentrate on getting treatment.clos