I am working with the members of NTU IoT Sensing Group to conduct research on networked sensing in various cyber-physical systems that are carried by humans, embedded in infrastructures (e.g., buildings, data centers, and smart grids), and distributed in wild environments. With an experimental basis, our research tries to understand how the physical processes affect the cyber systems (e.g., sensor networks and the broader Internet of Things) and exploit certain properties of the physical processes to advance the functional and non-functional (e.g., resilience) aspects of the cyber systems. Our research has been recognized by various best paper awards and runner-ups at prestigious international conferences. With immediate application potential, our research has been funded (or co-funded) externally by government authorities and companies in the information technology, energy, and manufacturing sectors, through 10 projects as of 2021. I have been serving on the technical program committees of various international conferences and also the editorial boards of journals that are related to networked sensing.
AIoT (AI + IoT)
Sensing for resilient system functions (e.g., timing, synchronization, location, etc)
Names underlined are students, research staff, visiting students who worked directly with me in my group for the publication.
[ACM/IEEE IPSN'21]Wenjie Luo, Zhenyu Yan, Qun Song, Rui Tan. PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems. Best Artifact Award Runner-Up. [pdf] [code and data] [video presentation]
[ACM BuildSys'20]Duc Van Le, Yingbo Liu; Rongrong Wang; Rui Tan; Lek Heng Ngoh. Experiences and Learned Lessons from an Air Free-Cooled Tropical Data Center Testbed. [pdf] [video presentation]
[IEEE ICDCS'20]Chaojie Gu, Linshan Jiang, Rui Tan, Mo Li, Jun Huang. Attack-Aware Data Timestamping in Low-Power Synchronization-Free LoRaWAN. [pdf] [video presentation]
[ACM SenSys'19]Qun Song, Zhenyu Yan, Rui Tan. Moving Target Defense for Embedded Deep Visual Sensing against Adversarial Examples. [pdf] [slides]
[ACM MobiCom'19]Zhenyu Yan, Qun Song, Rui Tan, Yang Li, Adams Wai Kin Kong. Towards Touch-to-Access Device Authentication Using Induced Body Electric Potentials. [pdf] [slides]
[ACM UbiComp'18]Qun Song, Chaojie Gu, Rui Tan. Deep Room Recognition Using Inaudible Echos. [pdf] [slides]
[ACM SenSys'17]Zhenyu Yan, Yang Li, Rui Tan, Jun Huang. Application-Layer Clock Synchronization for Wearables Using Skin Electric Potentials Induced by Powerline Radiation. [pdf] [slides]
[ACM/IEEE IPSN'17]Yang Li, Rui Tan*, David Yau. Natural Timestamping Using Powerline Electromagnetic Radiation. *Corresponding author. Best Paper Award. [pdf] [slides]
[IEEE RTSS'16]Sreejaya Viswanathan, Rui Tan*, David Yau. Exploiting Power Grid for Accurate and Secure Clock Synchronization in Industrial IoT. *Corresponding author. [pdf] [slides]
[ACM/IEEE ICCPS'16] Rui Tan, Hoang Hai Nguyen, Eddy. Y. S. Foo, Xinshu Dong, David K. Y. Yau, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Hoay Beng Gooi. Optimal False Data Injection Attack against Automatic Generation Control in Power Grids. [pdf] [slides]
[ACM CCS'13] Rui Tan, Varun Badrinath Krishna, David K. Y. Yau, Zbigniew Kalbarczyk. Impact of Integrity Attacks on Real-Time Pricing in Smart Grids. [pdf] [slides]
[ACM/IEEE IPSN'13] Guojin Liu, Rui Tan*; Ruogu Zhou; Guoliang Xing; Wen-Zhan Song; Jonathan M. Lees. Volcanic Earthquake Timing using Wireless Sensor Networks. *Co-primary authors. [pdf] [slides]
[IEEE PerCom'13] Dennis E. Phillips, Rui Tan*; Mohammad-Mahdi Moazzami; Guoliang Xing; Jinzhu Chen; David K. Y. Yau. Supero: A Sensor System for Unsupervised Residential Power Usage Monitoring. *Co-primary authors. Mark Weiser Best Paper Award Finalist. [pdf] [slides] [video]
Method and System for Carrying Out Timing Related Tasks. Inventors (ordered alphabetically): Yang Li, Rui Tan, Sreejaya Viswanathan, David Yau.
US Patent 16464264 (applied in May 2019; granted in Oct 2020).
An Automated Calibration Approach for Data Center Digital Twin via Neural Surrogate Model.
Inventors: Ruihang Wang, Xin Zhou, Yonggang Wen, Rui Tan.
Singapore provisional patent application 10202009828S, Oct 2020.
Air-Cooled Tropical Data Centre 2.0
Funded by Singapore National Research Foundation (NRF). PI. S$1.62M, Apr 2021 to Mar 2023.
As part of an NRF IAF-PP Programme (Sustainable Tropical Data Centre Testbed).
Hoang Hai Nguyen (Research Engineer, 2013-2015; admitted to UIUC PhD program in 2015)
Yang Li (Postdoctoral Researcher, 2015-2018)
Sreejaya Viswanathan (Senior Research Engineer, 2015-2018)
Zhan Teng Teo (Research Engineer, 2015-2016)
Sheng-Yuan Chiu (Visiting Student, 2013, 2014)
Recent and current research
1. AIoT (2017-now)
Physics-directed domain adaptation
Run-time domain shifts are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations. Existing transfer learning techniques require substantial target-domain data and thus incur high post-deployment overhead. We study how to exploit the first principle governing the domain shift to reduce the demand on target-domain data. Specifically, we use the first pinciple fitted with few source/target-domain data pairs to transform the existing source-domain training data into augmented data for updating the deep neural networks. We have applied this PhyAug approach to recover the accuracy losses of DeepSpeech2 caused by microphone characteristic using 5-second data collected from the microphone. This work won the IPSN'21 Best Artifact Award Runner-Up.
Moving target defense against adversarial examples
Deep models are vulnerable to adversarial examples. Many existing countermeasures build their security on the attacker's ignorance of the defense mechanisms and can be subverted once the attackers know the details of the defense. In this research, we apply the strategy of moving target defense to generate multiple fork models at run time from a factory-designed base model, that collaboratively detect and thwart adversarial examples. We also develop efficient implementations of our defense on embedded GPU platforms.
Deep learning-based location fingerprinting
This research uses a smartphone to emit a short inaudible acoustic chirp (only two milliseconds long) and record the ambient's reverberation as a fingerprint of the smartphone's location. We apply deep learning to deal with the challenge of the reverberation's limited information due to its narrow band and short recording time (only 0.1 seconds). We have applied this approach to implement a room-level localization system that can recognize up to 50 rooms with 97.7% accuracy. We have also used it to fingerprint 15 locations in a crowded museum and achieve 89% accuracy.
Lightweight privacy preservation for deep learning and inference
2. Sensing for resilient system functions, e.g., timing, synchronization, location, etc (2015-now)
Powerline forensic time and secure clock synchronization
The frequency of the alternating current (ac) voltage of power grid has tiny fluctuations over time. The fluctuations at different locations in a power grid are similar. Based on this, we match the fluctuation traces collected by two voltage sensors to identify the offset between their clocks and synchronize their clocks. This approach achieves 10 microseconds synchronization accuracy in a building and 100 microseconds accuracy for two nodes 10km apart. NTP in LAN can only achieve milliseconds accuracy; PTP (Precision Time Protocol) in LAN can achieve microseconds accuracy, but it requires PTP-enabled switches. Our system also gives security against the packet delay attack that is effective against message-exchange-based clock synchronization protocols. We also extend the above idea to study the accuracy of the forensic time derived from the powerline's magnetic field and achieve 150 milliseconds accuracy. This work won the IPSN'17 Best Paper Award.
Wearable clock synchronization and device authentication in ambient electrostatic field
Powerlines induce an electrostatic field oscillating at the power grid frequency (e.g., 50Hz). This field will cause the redistribution of the charges on the human body viewed as an uncharged equipotential conductor. The redistributed charges in return distort the near-body electric field. We use an ungrounded analog-to-digital converter to measure the transient potential difference between the human body and its near field. As any such two wearables capture the same power grid frequency, we use this common frequency with the principle of NTP and achieve milliseconds synchronization accuracy. Moreover, the transient potential difference signals captured by two wearables at close locations on the same human body are similar. We use this to develop a touch-based device authentication system, in which the user with a token device can authenticate himself/herself to a touchable smart object.
Summary: Exploit power network signals for time acquisition and location sensing
Our research has exploited the ac voltages of a power grid, the magnetic and electrostatic fields induced by the powerlines distributed in civil infrastructures to acquire time and location information. Time acquisition: We can synchronize wall-powered and wireless devices distributed in a geographic area served by the same power system (from building area to city area), with sub-millisecond and sub-second accuracy, respectively. We can also synchronize body-contacted devices worn by users distributed in the geographic area, with milliseconds accuracy. Location sensing: We exploit the powerline-induced magnetic field to perform SLAM in the building area. We also exploit the powerline electrostatic potential received by the human body to perform same-body detection for device authentication.
Secure data timestamping in LoRaWAN
LoRaWAN is promising for the applications of collecting low-rate data. Data samples need timestamps to make sense. However, tightly synchronizing the nodes incurs much overhead to bandwidth-limited LoRaWAN. We propose to perform gateway-side timestamping, which saves bandwidth. However, this gateway-side timestamping is vulnerable to a crafty frame delay attack. We conducted experiments in a campus network, and showed that, by setting up two attack devices (collider and eavesdropper), all LoRaWAN end devices in the area of about 50,000 square meters are affected by the attack. To develop attack awareness, we design a LoRaTS gateway based on cheap radio hardware to detect the carrier frequency offset (CFO) changes caused by the frame delay attack.
3. Thermal and energy management in data centers (2011-2014, 2017-now)
TDC1: World's first trial of air free-cooled data center in tropics (completed)
Air free cooling that utilizes natural outside air to cool the IT equipment in data center has been thought infeasible in Singapore's tropical condition with year-round high temperatures and humidity levels. From 2017 to 2019, we designed, constructed, and experimented with an air free-cooled and deeply sensorized data center testbed consisting of three server rooms hosting 12 server racks with 60kW total power rating. Our results show that Singapore's temperatures of up to 37℃ aren't a concern, but the cleaness and relative humidity of the air supplied to the servers need to be well controlled to maintain the servers' reliability. The energy efficiency metrics (e.g., DCiE) obtained on our testbed would be the upper limit in the local context. The findings of our research are documented in an NTU technical report and communicated to the local data center industry via various invited technical presentations. (Read more details on TDC1.)
TDC2: A commercially ready tropical data center (ongoing)
Based on our TDC1 research results, our TDC2 project sets up an actual data center with 400 servers in an enclosed and conditioned building. The main objectives of TDC2 include: 1) To develop a practicable methodology to determine the optimal setpoints for supply air temperature and relative humidity to achieve the highest energy efficiency; 2) To understand the server reliability under the optimal temperature/humidity setpoints and the implication on the cost-benefit relationship. During the project period, the TDC2 is deeply sensorized for research and meanwhile offers commercial computing services. After the project period, TDC2 will be fully handed over to our data center operator partner for offering continued commercial services. TDC2's ultimate goal is to seed high-temperature data centers with improved energy efficiency in Singapore's tropical condition. (Read CNA's news report on TDC2.)
4. Secure sensing and control in smart grids (2012-now)
False data injection attacks on smart grid controls
[04/2020] Appreciated as Distinguished TPC Member of INFOCOM'20.
[07/2019] Our paper on air free-cooled data centers in tropics is accepted to BuildSys'19. The paper receives (highly) positive comments from all Reviewers!
[07/2019] Our paper on counteracting adversarial example attacks on deep visual sensing is accepted to SenSys'19.
[02/2019] Two papers accepted to IoTDI and ICCPS of CPS-IoT Week 2019!
[02/2019] Two papers on exploiting indoor powerline radiation presented at / accepted by MobiCom'18 and MobiCom'19!
[09/2018] We use a smartphone to emit a 2 milliseconds inaudible chirp and record audio for just 0.1 seconds to recognize a room. The paper will be presented on Ubicomp'18.
[11/2017] Our paper on using LoRaWAN to build control plane for multi-hop wireless networks is accepted to INFOCOM'18.
[07/2017] Our paper on using skin electric potentials to synchronize the clocks of wearables is accepted to SenSys'17!
[04/2017] Our IPSN'17 paper won the Best Paper Award! In this research, we show that the electromagnetic radiation from powerlines contains time information with errors down to 50 milliseconds. PhotoNTU/SCSE newsIllinois news
[01/2016] Our paper on attacks against power grid electricity generation control system is accepted to ICCPS'16. In this work, we really attacked a generator of a microgrid to deviate the 50Hz system frequency.