Wei Dong            董玮

PhD, Professor, PhD supervisor
College of Computer Science, Zhejiang University

Tel: +86-571-87952813
Email: dongw AT zju.edu.cn
R.m. 311, Zetong Building, Yuquan Campus, Zhejiang University, Hangzhou, China.
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Wei Dong is currently a full Professor at the College of Computer Science and Technology in Zhejiang University. He received the BS degree and PhD degree from the College of Computer Science and Technology in Zhejiang University in 2005 and 2010, respectively. He was a Postdoc Fellow at the Department of Computer Science and Engineering in Hong Kong University of Science and Technology in 2011. He joined in Zhejiang University as a faculty member in Feb., 2012. He leads the Emerging Networked Systems research group (EmNets). His current research interests include AIoT, edge computing and edge AI, wireless networking and IoT security.



Developing and experimenting IoT applications are still difficult and time-consuming, mainly due to their heterogeneous hardware and diverse software. In this work, we develop LinkLab, a scalable IoT testbed for heterogeneous devices. LinkLab not only supports running experiments but also supports remote development via a web-based IDE and remote compiling. By using a distributed architecture, LinkLab is a scalable, multisite, and multi-user IoT testbed, with fine-grained access control and a flexible naming mechanism. Currently, LinkLab is implemented with more than 150 IoT devices. The system has been published in ACM/IEEE IoTDI 2020 and will be reported at USENIX NSDI 2023.
TinyLink is a holistic system for rapid development of IoT applications. Developers write the application code in C-like language to specify the key logic of their applications, without dealing with the details of hardware components. Taking the application code as input, TinyLink automatically generates hardware configurations as well as the hardware dependent code executable on the targeted hardware platform. We have further extended our system to integrate cloud, device and client development in one piece of code, greatly accelerating the process for cloud-device integrated applications development. The designs and implementations have been published in ACM MobiCom 2017 and ACM MobiCom 2020.
Recent years have witnessed many low-power wireless technologies, e.g., ZigBee, Bluetooth, LoRa, etc. These heterogeneous technologies raised several important research issues, such as how to combat against cross-technology interference, how to perform cross-technology communication without a gateway, how to interoperate over a wide range of IoT protocols. In this study, we aim to address several key issues in this field. Initial works have been reported in IEEE ICNP 2018, IEEE ICDCS 2017 and IEEE TMC.
Modern IoT and mobile devices are equipped with a rich set of sensors, including accelerometer, gyroscope sensor, IMU sensor, acoustic sensor etc. Even the wireless module, traditionally used for communication, can be used as a sensing module (i.e., wireless sensing). The ubiquitous availabilty of these sensors, as well as the advances in signal processing and machine learning, give us unprecedented opportunities to better sense the physical world and human beings. In this study, we will focus on innovative applications and intelligent sensing techniques based on IMU, WiFi and millimeter-wave. Initial works have been published in UbiComp 2018 and UbiComp 2020.
Air quality monitoring has attracted a lot of attention from governments, academia and industry, especially for PM2.5 due to its significant impact on our respiratory systems. In this work, we design Mosaic, a low cost urban PM2.5 monitoring system based on mobile sensing. In Mosaic, a small number of air quality monitoring nodes are deployed on city buses to measure air quality. We have deployed the Mosaic system in Hangzhou and Ningbo, China. We conduct research on system design and deployment issues as well as sensor data calibration. These works have been published in IEEE INFOCOM 2016, IEEE INFOCOM 2017, and UbiComp 2018.
The GreenOrbs research project aims at building a long-term and large-scale WSN system in the forest. It employs the TelosB mote with msp430f1611 processor and CC2420 radio. The project was started from April 2009. From August 2010, we re-built the software based on TinyOS 2.1.1, with an improved architecture and implementation of the measurement module. Our research focuses on network measurement of large-scale sensor systems and the network protocols. A series of measurement studies have been reported in IEEE RTSS 2012, IEEE INFOCOM 2013, and ACM ToN 2014, 2015, 2016.
The development of a modern sensor network is difficult because of the long-term unattended operation mode, diverse application requirements, and stringent resource constraints. To address these issues, we design SenSpire OS, a predictable,flexible, and efficient operating system for sensor networks. We achieve system flexibility by providing a hybrid model for both event-driven programming and multithreaded programming. We have also designed an object-oriented programming language (CSpire) to enhance system usability and programming convenience. SenSpire OS has been implemented on three most commonly used sensor node platforms. The system is reported in IEEE TC 2011.

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Invited Talks