Stress can be recognised by observing changes in physiological responses on the human body. Wearable sensors for stress detection are becoming more prominent in recent years due to their functionality and non-intrusive nature. By utilising data from wearable sensors, we have developed a personalized stress detection system. Our system performs classification on stress level using multimodal data from wrist-worn device Empatica E4 wearable sensor. We implemented three different classification algorithms: Logistic Regression, Decision Tree, and Random Forest and used four-class classification conditions: baseline, stress, amusement, and meditation. By evaluating the performance of the system, we demonstrate that our system can perform the best and consistent personalized stress detection using Random Forest classifier with the accuracy of 88%-99% on 15 subjects.

The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system—a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset.

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Fish is beneficial for the human body because it has high protein content. Consuming fish is necessary and expert knowledge is needed to identify fresh fish that are suitable for consumption. In this study, we developed a classification system to identify four classes of consumable fish by grouping fish images based on texture extraction and color features. We use fish meat and fish scale as identification parameters. Fish meat image is measured using the HSV colors model (Hue, Saturation, and Value) and GLCM (Gray Level Co-occurrence Matrix) method. We use these values for texture feature extraction of scales. Then we use k-Nearest Neighbor (kNN) as the classifier. The test results from 320 sample images show that the identification accuracy of tilapia meat is 90% and 97.5% for mackerel meat. Meanwhile for the scales, the accuracy up to 87.5% for tilapia scales and 95% for mackerel scales.

The growing number of connected Internet of Things (IoT) devices has increased the necessity for processing IoT data from multiple heterogeneous data stores. IoT data integration is a challenging problem owing to the heterogeneity of data stores in terms of their query language, data models, and schemas. In this paper, we propose a multi-store query system for IoT data called MusQ, where users can formulate join operation queries for heterogeneous data sources. To reconcile the heterogeneity between source schemas of IoT data stores, we extract a global schema from local source schemas semi-automatically by applying schema-matching and schema-mapping steps. In order to minimize the burden on the user to understand the finer details of various query languages, we define a unified query language called the multi-store query language (MQL), which follows a subset of the Datalog grammar. Thus, users can easily retrieve IoT data from multiple heterogeneous sources with MQL queries. As the three MQL query-processing join algorithms are based on a mediator-wrapper approach, MusQ performs efficient data integration over significant volumes of IoT data from multiple stores. We conduct extensive experiments to evaluate the performance of the MusQ system using a synthetic and large real IoT data set for three different types of data stores (RDBMS, NoSQL, and HDFS). The experimental results demonstrate that MusQ is suitable, scalable, and efficient query processing for multiple heterogeneous IoT data stores. Those advantages of MusQ are important in several areas that involve complex IoT systems, such as smart city, healthcare, and energy management. INDEX TERMS Data management and analytics, Internet of Things, multi-store system, query processing, schema integration. FIGURE 2. Grammar of MQL queries.