co-located with The 23rd International Conference on Pervasive Computing and Communications (PerCom 2025)
Washington DC, March 17-21, 2025.
co-located with The 23rd International Conference on Pervasive Computing and Communications (PerCom 2025)
Washington DC, March 17-21, 2025.
Wireless sensing has recently attracted a great deal of attention thanks to its non-invasive and sensor-free nature. Contrary to traditional sensor-based and wearable sensing, wireless sensing does not need any sensors, but leverages the modifications induced on the wireless channel by objects and people to infer information about their position and movement within a physical environment. This leads to an unobtrusive system that can be integrated with nowadays transmission technology. Moreover, if operated, e.g., at sub-6GHz frequencies, wireless signals propagate through walls, allowing sensing to be performed even in non-line-of sight (NLOS) scenarios with a subsequent increase in the sensing coverage over camera-based systems. Different types of wireless signals have been employed for sensing including WiFi, RFID, mmWave, UWB, and acoustic signals. As wireless signals bounce off of physical objects within the environment such as static objects like walls or furniture, as well as any humans in the environment, their characteristics (e.g., amplitude, phase) change uniquely. This then provides an opportunity to sense the environment and obtain contextual information (e.g., recognizing the motion) through a finegrained analysis of signal variations. Wireless sensing has been considered in various applications including but not limited to localization, human activity and gesture recognition, gait estimation, fall detection, respiration monitoring, crowd counting, etc.
Deploying the wireless sensing systems on edge devices is also important, to reduce their costs and make them scalable. However, this comes with several challenges due to the constrained resources (e.g., memory, computation power, energy) of edge nodes. Accordingly, in this workshop we also look for solutions that develop novel, lightweight and cost-efficient techniques that can run at the network edge, providing means to train and run machine learning models in an energy efficient manner, both according to centralized and distributed training paradigms. We are also interested in the characterization of energetic aspects on currently available edge computing technology, including but not limited to empirical energy models.
The objective of this workshop is to bring together the research community utilizing different types of wireless signals for sensing purposes, and the community dealing with computing for embedded and energy efficient systems, and have them benefit from each other's findings. The workshop will also serve as a discussion platform about the standardization and industrial implications of wireless sensing and edge computing, by also targeting potential privacy issues. Toward these goals, the workshop will span topics including, but not limited to:
Submissions will be made using EDAS.
Manuscripts submitted for consideration should not have been already published elsewhere and should not be under review or submitted for review elsewhere during the consideration period. Manuscripts must be written in English, are limited to 6 pages, single spacing, double column, and must strictly adhere to the IEEE template format available here. All accepted papers will be included and indexed in the IEEE Digital Library (IEEE Xplore), showing their affiliation with IEEE Percom 2025. At least one author of each accepted paper is required to attend and present his/her work at the workshop.
| Paper submission deadline: | November 17, 2024 |
| Author Notification: | January 8, 2025 |
| Camera-ready submission: | February 2, 2025 |
| Workshop date: | TBD |
