With an increasing number of rich embedded sensors, like accelerometer and GPS, smartphone becomes a pervasive people-centric sensing platform for inferring user's daily activities and social contexts. Alternatively, wireless sensor network offers a comprehensive platform for capturing the surrounding environmental information using mobile sensing nodes, e.g., the OpenSense project [2] in Switzerland deploying air quality sensors like CO on public transports like buses and trams. The two sensing platforms are typically isolated from each other. In this paper, we build ExposureSense, a rich mobile participatory sensing infrastructure that integrates the two independent sensing paradigms. ExposureSense is able to monitor people's daily activities as well to compute a reasonable estimation of pollution exposure in their daily life. Besides using external sensor networks, ExposureSense also supports pluggable sensors (e.g., O ₃ ) to further enrich air quality data using mobile participatory sensing with smartphones.

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions.

In several applications, data objects are assumed to move on predefined spatial networks such as road segments, railways, invisible air routes. Moving objects may exhibit similarity with respect to their traversed paths, and therefore two objects can be correlated based on their path similarity. In this paper, we study similarity search for moving object trajectories for spatial networks. The problem poses some important challenges, since it is quite different from the case where objects are allowed to move without any motion restrictions. Experimental results performed on real-life spatial networks show that trajectory similarity can be supported in an effective and efficient manner, by using metric-based access methods.