Daqing Zhang

Bio: Daqing Zhang is an academic researcher from Peking University. The author has contributed to research in topics: Context (language use) & Mobile computing. The author has an hindex of 67, co-authored 331 publications receiving 16675 citations. Previous affiliations of Daqing Zhang include Institut Mines-Télécom & Institute for Infocomm Research Singapore.

A Dynamic Community Creation Mechanism in Opportunistic Mobile Social Networks

Abstract: Web-based social networking services enable like-minded people to collaborate and socialize with each other. With rich sensing and communication capabilities, mobile phones provide new possibilities for enhancing face-to-face social interaction among people who are both socially and physically close to each other. Research challenges arise as how to exploit the characteristics of people's mobility patterns and form a social community with a specific goal in the mobile environment. In this paper, we present SOCKER - a dynamic community creation mechanism based on social-aware broker selection strategies. SOCKER gradually forms a mobile social community by dynamically selecting a broker during each opportunistic encounter, and the selected broker disseminates community creation requests to the encountered users for match-making. Based on real human mobility traces, extensive evaluations are conducted showing that SOCKER achieves high community completion ratio as well as high user social satisfaction, while incurring a small overhead.

Crowd-Powered Sensing and Actuation in Smart Cities: Current Issues and Future Directions

Abstract: With the advent of seamless connection of human, machine, and smart things, there is an emerging trend to leverage the power of crowds (e.g., citizens, mobile devices, and smart things) to monitor what is happening in a city, understand how the city is evolving, and further take actions to enable better quality of life, which is referred to as Crowd-Powered Smart City (CPSC). In this article, we provide a literature review for CPSC and identify future research opportunities. Specifically, we first define the concepts with typical CPSC applications. Then, we present the main characteristics of CPSC and further highlight the research issues. In the end, we point out existing limitations which can inform and guide future research directions.

A Fresnel Diffraction Model Based Human Respiration Detection System Using COTS Wi-Fi Devices

Abstract: Recent work has revealed the sensing theory of human respiration outside the First Fresnel Zone (FFZ) using commodity Wi-Fi devices. However, there is still no theoretical model to guide human respiration detection when the subject locates in the FFZ. In our work [10], we propose a diffraction-based sensing model to investigate how to effectively sense human respiration in FFZ. We present this demo system to show human respiration sensing performance varies based on different human locations and postures. By deploying the respiration detection system using COTS Wi-Fi devices, we can observe that the respiration sensing results match the theoretical model well.

Assisting Elders with Mild Dementia Staying at Home

Abstract: Elders with mild Dementia exhibit impairments of memory, thought and reasoning. It has been recognized that pervasive computing technologies can assist those suffering from mild Dementia to improve their level of independence and quality of life through cognitive reinforcement. In this paper, we present a user-centred design approach, which is based on the iterative process of user study, prototyping, user test and evaluation, to achieve the goal of developing a cost-effective cognitive prosthetic device with associated services for elders with mild Dementia. Specifically, we describe the results of user study in three different test sites, four areas of cognitive reinforcement have been identified to assist their independent living. Of different assistive services, we choose two context-aware reminding services as a case study to illustrate how to deploy pervasive computing techniques in the system design. Finally, we present the overall system architecture and initial system implementation with first trial results.

Context Aware Computing for The Internet of Things: A Survey

Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

Membership Inference Attacks Against Machine Learning Models

Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.