Wei Dong            董玮 PhD, Professor, PhD supervisor College of Computer Science, Zhejiang University Tel: +86-571-87952813 Email: dongw AT zju.edu.cn Office:   R.m. 311, Zetong Building, Yuquan Campus, Zhejiang University, Hangzhou, China.

News

Projects

Publications

Grants

Awards

Talks

Misc

Biography

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 Embedded and Networked Systems research group (EmNets). His current research interests include IoT systems and networks, edge computing, wireless and mobile computing.

News

• [Recruiting] I am looking for Postdocs in the area of IoT.
• We have won the CCF Distinguished Awards for Science and Technology Progress.
• 《从创意到原型：物联网应用快速开发》, 科学出版社. (京东链接、当当链接)
• 《物联网快速开发平台》，中国计算机学会通讯 (CCCF)
• [2022.03] Our paper on "5G bandwidth prediction" is accepted to ACM/IEEE IWQoS 2022.
• [2022.03] Our two papers "TinyNet" and "WAIT" are conditionally accepted to ACM MobiSys 2022.
• [2021.11] One paper is accepted to IEEE Transactions on Mobile Computing.
• [2021.11] One paper is accepted to IEEE Transactions on Computers.
• [2020.12] Two papers are accepted to IEEE INFOCOM 2021.
• [2020.12] One paper is accepted to ACM Transactions on Sensor Networks.
• [2020.09] Our "TinyEdge" paper is accepted to IEEE/ACM Symposium on Edge Computing 2020.
• [2020.08] Two papers "TinyLink" and "JamCloak" are accepted to ACM Transactions on Sensor Networks.
• [2020.03] Our paper on edge-centric IoT programming (EdgeProg) is accepted to IEEE ICDCS 2020.
• [2020.02] One paper is accepted to IEEE Internet of Things Journal.
• [2020.01] Our "LinkLab" paper is conditionally accepted to ACM/IEEE IoTDI 2020.
• [2020.01] One paper is conditionally accepted to ACM/IEEE IPSN 2020.
• More ...

Projects

	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.
	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.

Selected Publications (full list)

• [MobiSys'22] TinyNet: a Lightweight, Modular, and Unified Network Architecture for the Internet of Things

  Interoperability among a vast number of heterogeneous IoT nodes is a key issue. However, the communication among IoT nodes does not fully interoperate to date. The underlying reason is the lack of a lightweight and unified network architecture for IoT nodes having different radio technologies. In this paper, we design and implement TinyNet, a lightweight, modular, and unified network architecture for representative low-power radio technologies including 802.15.4, BLE, and LoRa. The modular architecture of TinyNet allows us to simplify the creation of new protocols by selecting specific modules in TinyNet. We implement TinyNet on realistic IoT nodes including TI CC2650 and Heltec IoT LoRa nodes. We perform extensive evaluations. Results show that TinyNet (1) allows interoperability at or above the network layer; (2) allows code reuse for multi- protocol co-existence and simplifies new protocol design by module composition; (3) has small code size and memory footprint.

   
• [MobiSys'22] Bring WebAssembly to Resource-constrained IoT Devices for Seamless Device-Cloud Integration

  Recent years have witnessed the progressive in-tegration between IoT (Internet of Things) devices and the cloud server, which promotes the efficiency and interoperabilityof IoT applications. WebAssembly, known for its performance and portability, is considered a promising technology to bridgethe heterogeneity between devices and the server. Nevertheless,the resource-constrained devices, which are commonly deployedin the wild, are difficult to participate in this device-cloudintegration because it could hardly run WebAssembly efficiently. Hence, we propose WAIT, a lightweight WebAssembly runtimeon resource-constrained IoT devices for device-cloud integratedapplications. WAIT is the first work to enable the Ahead-of-Time(AOT) compilation of WebAssembly on resource-constrained devices by leveraging several approaches to reduce memory usage.Moreover, WAIT introduces various safety checks at compile-time to guarantee the sandbox execution of WebAssembly and optimizes energy consumption for IoT devices. Results show that WAIT achieves 84.8×lower RAM usage compared with the state-of-the-art WebAssembly AOT runtime, and reduces 1.2×~4.9×energy consumption while guaranteeing the sandboxed execution of WebAssembly modules.

   
• [INFOCOM'21] WiProg: A WebAssembly-based Approach to Integrated IoT Programming

  Programming a complete IoT application usually requires separated programming for device, edge and/or cloud sides, which slows down the development process and makes the project hardly portable. Existing solutions tackle this problem by proposing a single coherent language while leaving two issues unsolved: efficient migration among the three sides and the platform dependency of the binaries. We propose WiProg, an integrated approach to IoT application programming based on WebAssembly. WiProg proposes an edge-centric programming approach that enables developers to write the IoT application as if it runs on the edge. This is achieved by the peripheral-accessing SDKs and annotations specifying the computation placement. WiProg automatically processes the program to insert auxiliary code and then compile it to WebAssembly At runtime, WiProg leverages dynamic code offloading with compact memory snapshotting to achieve efficient execution. WiProg also provides interfaces for the customization of offloading policies. Results on real-world applications and computation benchmarks show that WIPROG achieves an average reduction by 18.7%~54.3% and 20.1%~57.6% in terms of energy consumption and execution time.

   
• [INFOCOM'21] LoFi: Enabling 2.4GHz LoRa and WiFi Coexistence by Detecting Extremely Weak Signals

  Low-Power Wide Area Networks (LPWANs) emerges as attractive communication technologies to connect the Internet-of-Things. A new LoRa chip has been proposed to provide long range and low power support on 2.4GHz. Comparing with previous LoRa radios operating on sub-gigahertz, the new one can transmit LoRa packets faster without strict channel duty cycle limitations and have attracted many attentions. Prior studies have shown that LoRa packets may suffer from severe corruptions with WiFi interference. However, there are many limitations in existing approaches such as too much signal processing overhead on weak devices or low detection accuracy. In this paper, we propose a novel weak signal detection approach, LoFi, to enable the coexistence of LoRa and WiFi. LoFi utilizes a typical physical phenomenon Stochastic Resonance (SR) to boost weak signals with a specific frequency by adding appropriate white noise. Based on the detected spectrum occupancy of LoRa signals, LoFi reserves the spectrum for LoRa transmissions. We implement LoFi on USRP N210 and conduct extensive experiments to evaluate its performance. Results show that LoFi can enable the coexistence of LoRa and WiFi in 2.4GHz. The packet reception ratio of LoRa achieves 98% over an occupied 20MHz WiFi channel, and the WiFi throughput loss is reduced by up to 13%.

   
• [MobiCom'20] TinyLink 2.0: Integrating Device, Cloud, and Client Development for IoT Applications

  The recent years have witnessed the rapid growth of IoT (Internet of Things) applications. A typical IoT application usually consists of three essential parts: the device side, the cloud side, and the client side. The development of a complete IoT application is very difficult for non-expert developers because it involves drastically different technologies and complex interactions between different sides. Unlike traditional IoT development platforms which use separate approaches for these three sides, we present TinyLink 2.0, an integrated IoT development approach with a single coherent language. It achieves high expressiveness for diverse IoT applications by an enhanced IFTTT rule design and a virtual sensor mechanism which helps developers express application logic with machine learning. Moreover, TinyLink 2.0 optimizes the IoT application performance by using both static and dynamic optimizers, especially for resource-constrained IoT devices. We implement TinyLink 2.0 and evaluate it with eight case studies, a user study, and a detailed evaluation of the proposed programming language as well as the performance optimizers. Results show that TinyLink 2.0 can speed up IoT development significantly compared with existing approaches from both industry and academia, while still achieving high expressiveness.

   
• [ToN'19] Understanding Path Reconstruction Algorithms in Multihop Wireless Networks

  Low-power and multihop wireless networking is envisioned as a promising technology to achieve both energy efficiency and easy deployment for many Internet of Things (IoT) applications. Measuring packet-level path is crucial for managing large-scale multihop wireless networks. Packet-level path information encodes the routing path, a packet that takes through a network. The availability of packet-level path information can greatly facilitate many network management tasks. It is challenging to reconstruct packet-level paths using a small overhead, especially for large-scale networks. While there is a long list of existing path reconstruction algorithms, these algorithms focus on specific network scenarios, e.g., periodic monitoring networks or event detection networks. There lacks a unified model for systematically understanding and comparing the performance of these algorithms in different network scenarios. In this paper, we fill this gap by proposing an abstract model. Using this model, it is possible to derive a decision space for selecting the best algorithm for different networks. Furthermore, this model also guides us to devise better path reconstruction algorithms (cPath_T, cPath_S, and cPath_ST ) with respect to path reconstruction ratio. Extensive experiments demonstrate the prediction power of our model as well as the advantages of our proposed algorithms. The results show that our algorithm (cPath_ST ) improves a path reconstruction ratio from 94.4%, 34.3%, and 30.8% to 98.9%, 99.9%, and 60.1% on average in three network scenarios, respectively, compared with the best state-of-the-art algorithms.

  
• [MobiCom'17, TOSN] TinyLink: a Holistic System for Rapid Development of IoT Applications

  Rapid development is essential for IoT (Internet of Things)application developers to obtain first-mover advantages and reducethe development cost. In this paper, we present TinyLink, a holisticsystem for rapid development of IoT applications. The key ideaof TinyLink is to use atop-downapproach for designing boththe hardware and the software of IoT applications. Developerswrite the application code in a C-like language to specify thekey logic of their applications, without dealing with the detailsof the specific hardware components. Taking the applicationcode as input, TinyLink automatically generates the hardwareconfiguration as well as the binary program executable on thetarget hardware platform. TinyLink provides unified APIs forapplications to interact with the underlying hardware components.We implement TinyLink and evaluate its performance using real-world IoT applications. Results show that: (1) TinyLink achievesrapid development of IoT applications, reducing 52.58% of linesof code in average compared with traditional approaches; (2)TinyLink searches a much larger design space and thus can generatea superior solution for the hardware configuration, comparedwith the state-of-the-art approach; (3) TinyLink incurs acceptableoverhead in terms of execution time and program memory.

  
• [TMC'17] Embracing Corruption Burstiness: Fast Error Recovery for ZigBee under Wi-Fi Interference

  The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means forZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy toin-packet blocks for recovering a certain number of random corruptions. Therefore the bursty nature of ZigBee in-packet corruptionsunder Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blockshave no errors but still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applyingcomplex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may behelpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as"recovering any k consecutive errors" instead of "recovering any random k errors". This lowered requirement allows us to design farmore efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simpleyet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced.More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmissionoverhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47%) and latency (by 22%).

  
• [INFOCOM'16] Mosaic: A Low-Cost Mobile Sensing System for Urban Air Quality Monitoring

  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 paper, we present the design, implementation,and evaluation of 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. Current low-cost particle sensors based on light-scattering, however, are vulnerable to airflow disturbance on moving vehicles. In order to address this problem, we build our air quality monitoring nodes, Mosaic-Nodes, with a novel constructive airflow-disturbance design based on a carefully tuned airflow structure and a GPS-assisted filtering method. Further, the buses used for system deployment are selected by a novel algorithm which achieves both high coverage and low computation overhead. The collected sensor data is also used to calculate the PM2.5 of locations without direct measurements by an existing inference model. We apply the Mosaic system in a testing urban area which includes more than 70 point-of-interests. Results show that the Mosaic system can accurately obtain the urban air quality with high coverage and low cost.

   
• [IPSN'16] TinySDM: Software Defined Measurement in Wireless Sensor Networks

  Network measurement, which provides detailed information about the behaviors of operational networks, is essential for network management in wireless sensor networks. In the literature, there have been many approaches focusing on measuring individual aspect of the network, e.g., per-packet routing path and per-hop delay. However, there lacks a general support for conducting different measurement tasks. When managing an operational network, a network operator often needs to switch the current measurement task to a different one,in order to diagnose the observed symptoms. In this paper, we propose TinySDM, a software-defined measurement architecturefor WSNs. TinySDM provides a general support for conducting different measurement tasks. TinySDM defines a set of carefully selected hooks that allow the users to easily execute their own measurement tasks. In addition, TinySDM provides a C-like language called TinyCode Language (TCL) to enable easy customization of measurement tasks. By only transmitting the binary code of the measurement task, TinySDM significantly reduces the size of the disseminated data compared with existing reprogramming approaches. We implement TinySDM on theTinyOS/TelosB platform and evaluate its performance extensively in a testbed with 60 nodes. We also use TCL to implement four specific measurement tasks. Results show that TinySDM is flexible, efficient and easily programmable.

   
• [INFOCOM'13, ToN] Measurement and Analysis on the Packet Delivery Performance in a Large Scale Sensor Network

  Understanding the packet delivery performance of awireless sensor network (WSN) iscritical for improving systemperformance and exploring future developments and applicationsof WSN techniques. In spite of many empirical measurements inthe literature, we still lack in-depth understanding on how and towhat extent different factors contribute to the overall packet lossesfor a complete stack of protocols at large scale. Specifically, very little is known about: 1) when, where, and under what kind of cir-cumstances packet losses occur; 2) why packets are lost. As a steptoward addressing those issues, we deploy a large-scale WSN anddesign a measurement system for retrieving important system met-rics. We propose MAP, a step-by-step methodology to identify thelosses, extract system events, and perform spatial-temporal corre-lation analysis by employing a carefully examined causal graph.MAP enables us to get a closer look at the root causes of packetlosses in a low-power ad hoc network. This study validates someearlier conjectures on WSNs and reveals some newfindings. Thequantitative results also shed lights for future large-scale WSN deployments.

   
• [INFOCOM'13, TOSN] Optimizing Relocatable Code for Efficient Software Update in Networked Embedded Systems

  Recent advances in Microelectronic Mechanical Systems (MEMS) and wireless communication technologieshave fostered the rapid development of networked embedded systems like wireless sensor networks. Systemsoftware for these self-organizing systems often needs to be updated for a variety of reasons. We present aholistic software update (i.e., reprogramming) system called R3 for networked embedded systems. R3 hastwo salient features. First, the binary differencing algorithm within R3 (R3diff) ensures an optimal resultin terms of the delta size under a configurable cost measure. Second, the similarity preserving methodwithin R3 (R3sim) optimizes the binary code format for achieving a large similarity with a small metadataoverhead. Overall, R3 achieves the smallest delta size compared with other software update approaches suchas Stream, Rsync, RMTD, Zephyr, Hermes, and R2 (e.g., 50%–99% reduction compared to Stream and about20%–40% reduction compared to R2). R3's implementation on TelosB/TinyOS is lightweight and efficient.

   
• [TC'11] SenSpire OS: A Predictable, Flexible, and Efficient Operating System for Wireless Sensor Networks

  The development of a modern sensor network is difficult because of the long-term unattended operation mode, diverseapplication requirements, and stringent resource constraints. To address these issues, we present SenSpire OS, a predictable,flexible, and efficient operating system for wireless sensor networks. We improve system predictability by two-phase interrupt servicingand predictable thread synchronization; we achieve system flexibility by providing a hybrid model for both event-driven programmingand multithreaded programming; we retain system efficiency by employing stack sharing and modular design. Moreover, we havedesigned a three-layer networking stack and an object-oriented programming language (CSpire) to enhance system usability andprogramming convenience. Having implemented SenSpire OS on three most commonly used sensor node platforms, we evaluate itsperformance extensively. Results show that SenSpire OS ensures predictable system performance, provides a flexible hybrid modelfor application programming, and is efficient in resource utilization.

   
• [SIGMETRICS'10, TECS] Elon: Enabling Efficient and Long-Term Reprogramming for Wireless Sensor Networks

  We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wirelesssensor networks. Elon reduces the transferred code size significantly by introducing the concept ofreplaceablecomponent. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, itsignificantly prolongs the reprogrammable lifetime (i.e., the time period during which the sensor nodes canbe reprogrammed) by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfersup to 120–389 times less information than Deluge, and 18–42 times less information than Stream. Thesoftware reboot mechanism that Elon applies reduces the rebooting cost by 50.4%–53.87% in terms of beaconpackets, and 56.83% in terms of unsynchronized nodes. In addition, Elon prolongs the reprogrammable lifetime by a factor of 3.3.

   

Grants

Current grants

• PI, IoT Network Architecture for Interoperating across Heterogeneous Wireless Protocols, NSFC Grant No. 62072396, 2021.01~2024.12.
• PI, Research on Key Techniques for Cloud-Edge Integrated Data Collection, sub-project under National Key R&D Program of China, No. 2019YFB1600700, 2019.12~2022.11.
• PI, Low Power Wireless Technologies for the IoT, Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars, No. LR19F020001, 2019.01~2022.12.

Past grants

• PI, Software Update for Sensor Networks, NSFC Grant No. 61202402, 2013.1~2015.12.
• PI, Event-based Code Diagnosis for Networked Embedded Systems, NSFC Grant No. 61472360, 2015.1~2018.12.
• PI, Wireless Transmission Characteristics and Key Technologies under Cross-Technology Interference, NSFC Grant No. 61772465, 2018.1~2021.12.
• PI, Reprogramming Models and Protocols for Sensor Networks, Doctoral Program of Higher Education, No. 20120101120179, 2013.1~2015.12.
• PI, Mobile Sensing System for Air Quality Monitoring, Fundamental Research Funds for the Central Universities, No. 2016FZA5010, 2016.1~2016.12.
• PI, Network Tomography for Large-Scale Sensor Networks, Fundamental Research Funds for the Central Universities, No. 2014QNA5021, 2014.1~2015.12.
• PI, Energy-Efficient Reprogramming for Sensor Networks, Fundamental Research Funds for the Central Universities, No. 2012QNA5007, 2012.3~2013.12.
• PI, Low Power Wireless Technologies for the IoT, Fundamental Research Funds for the Central Universities, No. 2017FZA5013, 2017.1~2018.12.
• PI, Air Quality Monitoring with Mobile Sensor Networks, Commonwealth Project of Zhejiang Province, No. 2015C33077, 2015.1~2016.12.
• Co-PI, Zhejiang Provincial Platform of IoT Technology, Grant No. 2013E60005, 2012.1~2015.12.

Awards

• CCF Distinguished Awards for Science and Technology Progress, 2021.
• Second Class in Self-made Equipment of National Universities, 2021.
• Outstanding Achievement Award, Zhejiang Computer Federation, 2020.
• Outstanding Service Award, Zhejiang Computer Federation, 2019.
• Zhejiang NSF Distinguished Young Scholar, 2019.
• CCF-Tencent Riho-Bird Excellent Patent Award, 2017.
• Best Video Award, ACM MobiCom 2017.
• Zhejiang Provincial 151 Talent Project, 2017.
• Best Paper Award, SIGCOMM China Symposium (in conjuction with the ACM Turing 50th Celebration Conference), 2017.
• Best Paper Award, 12th IEEE Int'l Conf. on Mobile Ad-hoc and Sensor Networks (MSN), 2016.
• Second Prize of National IoT Competition & AWS Cloud Innovation Award, 2016.
• CCF-Tencent Riho-Bird Research Award Funds, 2016.
• CCF-Intel Young Faculty Researcher Program, 2015.
• First Class Science and Technology Progress Awards by Zhejiang Province, 2014.
• Nominee for the National Excellent Doctorial Dissertation Award, China Ministry of Education, 2013.
• Qiushi Distinguished Young Scholar of Zhejiang University, 2013.
• Excellent Doctorial Dissertation Award of Zhejiang Province, 2011.

Invited Talks

• “Low Code Development for IoT” (物联网低代码开发), ACM SIGBED China Forum, ACM TURC, Hefei, July 30, 2021.
• “Low Code Development for IoT” (物联网低代码开发), Computer Architecture Forum, Computer Conference of Jiangsu Province, Xuzhou, June 27, 2021.
• "Taming the Challenges in the IoT 2.0 era" (物联网2.0时代的挑战和机遇), Nanjing University, Dec. 8, 2019.
• “From Ideas to Prototypes: Rapid Development of IoT Applications” (从创新到原型: 物联网应用快速开发), Aliyun Yunqi Conference (阿里云云栖大会), Shenzhen, March 29, 2018.
• “From Ideas to Prototypes: Rapid Development of IoT Applications”, IoT Scientist Forum, Hangzhou, Nov. 27, 2017.
• "IoT and Smart Home", Invited Talk, China Mobile Digital Home Competition, Hangzhou, June 23, 2017.
• “My Road of Teaching and Research in Computer Networks”, Invited Talk, 6th National Symposium on Computer Network Education, Ningbo University, April 22-23, 2017.
• “TinyLink: a Holistic System for Rapid Development of IoT Applications”, Invited Talk, 11st Int'l Workshop on IoT and Cloud Computing, Wuxi, Dec 21-25. 2016.
• "Air Quality Monitoring with Mosaic", Invited Talk, Northwest University, Sept 18, 2016.
• "Air Quality Monitoring with Mosaic", Invited Talk, Ningbo University, April 26, 2016.
• "Air Quality Monitoring with Mosaic", The 10th ACM International Workshop on IoT and Cloud Computing, Wuxi, Dec. 21-25, 2015.
• "Towards a Software-Defined Architecture for the Internet of Things", Annual Forum for CCF Excellent Doctoral Dissertation Award, Sun Yat-sen University, Guangzhou, China, August 7-9, 2015.
• "Towards a Software-Defined Architecture for the Internet of Things", The 9th ACM International Workshop on IoT and Cloud Computing, Wuxi, China, July 2015.
• "Towards a Software-Defined Architecture for the Internet of Things", SFU-ZJU Joint Workshop on Big Data, Hangzhou, China, June 26, 2015.
• "Ten Rules for Research", The 2nd EmNets Summer Seminar, August 27-30, 2014.

Misc

• ZJU timer for teaching
• CS labs in Zhejiang University: EAGLE-lab, CCNT, DCD, CAD/CG, nmlab
• Networking Conferences Statistics
• Technical Reports [ EECS@UC Berkeley | CSE@Washington | MSU ]
• Prof. John Stankovic, Prof. Chenyang Lu, Prof. Guoliang Xing, Prof. Tian He, Prof. Gang Zhou
• Prof. David Culler, Prof. Philip Levis, Prof. Matt Welsh, Prof. Prabal Dutta,
• Prof. Dina Katabi, Prof. Sachin Katti, Prof. Shyam Gollakota, Prof. Swarun Kumar
• Prof. Kang Shin, Prof. Tarek F. Abdelzaher, Prof. Xinyu Zhang
• Others: Prof. Mani B. Srivastava, Prof. Deepak Ganesan, Prof. Romit, Prof. Lothar, Prof. Suman Banerjee, Prof. Yingying Chen