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In location-based social networks (LBSNs), users sense urban point-of-interest (PoI) information in the vicinity and share such information with friends in online social networks. Given users' limited social connections and severe lags in…
This paper presents a mobile crowdsensing approach to identify the indoor points of interest (POI) by exploiting Wi-Fi similarity measurements. Since indoor environments are lacking the GPS positioning accuracy when compared to outdoors, we…
Many smartphone apps transmit personally identifiable information (PII), often without the users knowledge. To address this issue, we present PrivacyProxy, a system that monitors outbound network traffic and generates app-specific…
Air pollution has become a global concern for many years. Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity. To better utilize the sensory data with varying credibility, truth discovery frameworks…
The health effects of air pollution have been subject to intense study in recent decades. Exposure to pollutants such as airborne particulate matter and ozone has been associated with increases in morbidity and mortality, especially with…
Indoor airborne transmission poses a significant health risk, yet current monitoring solutions are invasive, costly, or fail to address it directly. My research explores the untapped potential of ambient audio sensing to estimate key…
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time…
Both assistant driving and self-driving have attracted a great amount of attention in the last few years. However, the majority of research efforts focus on safe driving; few research has been conducted on in-vehicle climate control, or…
The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations…
Air quality monitoring in automotive workshops is crucial for occupational health and regulatory compliance. This study presents the development of an environmental monitoring system based on Internet of Things (IoT) and Artificial…
Automatic service discovery is essential to realizing the full potential of the Internet of Things (IoT). While discovery protocols like Multicast DNS, Apple AirDrop, and Bluetooth Low Energy have gained widespread adoption across both IoT…
Mobile devices have access to personal, potentially sensitive data, and there is a large number of mobile applications and third-party libraries that transmit this information over the network to remote servers (including app developer…
Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems. However, the issue of contextual bias…
Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch…
In the modern world of technology Internet-of-things (IoT) systems strives to provide an extensive interconnected and automated solutions for almost every life aspect. This paper proposes an IoT context-aware system to present an Ambient…
Developing information technology to democratize scientific knowledge and support citizen empowerment is a challenging task. In our case, a local community suffered from air pollution caused by industrial activity. The residents lacked the…
Participatory Sensing is an emerging computing paradigm that enables the distributed collection of data by self-selected participants. It allows the increasing number of mobile phone users to share local knowledge acquired by their…
Due to the significant air pollution problem, monitoring and prediction for air quality have become increasingly necessary. To provide real-time fine-grained air quality monitoring and prediction in urban areas, we have established our own…
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significantly among different users. To better utilize sensory data,…
Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural…