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Tang Lab
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A lab studying vehicle-to-vehicle communication, pedestrian positioning, and IoT communication.
AboutAbout the lab
Research on wireless networking and computing.
The Tang Lab studies wireless networking and computing.
One pillar of its research is vehicle-to-vehicle and vehicle-to-pedestrian communication, in which vehicles and pedestrians exchange information wirelessly, along with technology for accurately measuring pedestrian positions.
The lab also works on power-efficient communication for sensors and IoT devices, and on over-the-air computation, which performs data collection and processing at the same time.
It also studies content-centric networking, which stores and delivers content within the network, as well as content analysis.
Key terms
- Vehicle-to-vehicle communication
- Communication in which vehicles exchange position information wirelessly. At intersections with limited visibility, it lets a vehicle detect the approach of vehicles it cannot see.
- Content-centric networking
- A communication approach in which content is accessed by its name rather than by an IP address, making use of caching within the network.
- Over-the-air computation
- A method that performs data collection and processing at the same time, using analog waveforms on the wireless channel (AirComp).
NewsLab news
An undergraduate fourth-year student received the Student Encouragement Award at the 88th National Convention of the Information Processing Society of Japan
A paper by a doctoral student was published in ACM IMWUT
A master's student received the Excellent Presentation Award at the 103rd ITS Workshop of the Information Processing Society of Japan
A paper by a doctoral student was published in ACM Transactions on Sensor Networks
An undergraduate fourth-year student received the Student Encouragement Award at the 86th National Convention of the Information Processing Society of Japan
ResearchResearch themes
The lab aims to make communication more efficient, more reliable, and more power-saving, and to make positioning more accurate, in environments where vehicles, pedestrians, sensors, IoT devices, and other things are connected wirelessly. Its research is presented in three areas: Intelligent Transport Systems (ITS); sensor networks, IoT, and edge computing; and big data, content analysis, and semantic communication.
Making vehicle-to-vehicle communication more efficient and reliable
The lab studies how to make vehicle-to-vehicle communication, in which vehicles share position and traffic information wirelessly, more efficient and more reliable. It also covers the introduction of content-centric networking and methods for allocating communication resources in Cellular-V2X.
*Illustrative imageCommunication between pedestrians and vehicles
To reduce pedestrian traffic accidents, the lab studies vehicle-to-pedestrian communication, which wirelessly notifies vehicles of a pedestrian's location at places such as intersections with limited visibility. It examines how to make this communication more efficient and how to reduce the power consumption of pedestrians' devices.
*Illustrative imageMeasuring pedestrian positions with high accuracy
In urban areas, buildings reduce the accuracy of GPS positioning. The lab therefore examines using vehicles and roadside radio units with accurate positions as pseudolites (pseudo-satellites), and estimating the angle and distance between a vehicle and a pedestrian from the phase information of radio signals.
*Illustrative imageEfficiently determining where pedestrians are
Because the level of danger to a pedestrian differs by location, it is important to know which area a pedestrian is in. The lab studies how to make more efficient the process of matching polygons that represent area boundaries against pedestrian coordinates.
MembersFaculty
The Tang Lab consists of faculty members and students in the doctoral, master's, and undergraduate programs.As of 2026
- ProfessorSuhua Tang
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “SONYC acoustic sensor nodes deployed in New York City” Charlie Mydlarz; Mohit Sharma; Yitzchak Lockerman; Ben Steers; Claudio Silva; Juan Pablo Bello / CC BY-SA 4.0 (cropped)
- Communication between pedestrians and vehicles: “Boston - Crosswalk (cropped 2)” User:Piotrus / CC BY-SA 3.0 (cropped)
- Measuring pedestrian positions with high accuracy: “Olkhon Island, GPS navigation, Smartphone, Lake Baikal, Russia” Vyacheslav Argenberg / CC BY 4.0 (cropped)
- Efficiently determining where pedestrians are: “Crowd crossing the street, Hungary 2011” Keymeulen / CC0 (cropped)
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