WiCast: Parallel Cross-Technology Transmission for Connecting Heterogeneous IoT Devices
Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, for the downlink from WiFi to multiple IoT technologies, serially emulating and transmitting the data of each IoT technology has extremely low spectrum efficiency. In this paper, we propose WiCast, a parallel CTC that uses IEEE 802.11ax to emulate a composite signal that can be received by commodity BLE, ZigBee, and LoRa devices. By taking advantage of OFDMA in 802.11ax, WiCast uses a single Resource Unit (RU) for parallel CTC and sets other RUs free for high-rate WiFi users. But such a sophisticated composite signal is very easily distorted by emulation imperfections, dynamic channel noises, cyclic prefix, and center frequency offset. We propose a CTC link model that jointly models the emulation errors and channel distortions. Then we carve the emulated signal with elaborate compensations in both time and frequency domains. Based on the proposed CTC scheme, a unified Media Access Control approach is introduced to discover and synchronize the heterogeneous IoT devices. We implement a prototype of WiCast using USRP N210 platform along with commodity ZigBee, BLE, and LoRa devices. The extensive experiments demonstrate WiCast can achieve an efficient parallel transmission with the aggregated goodput up to 390.24kbps.
Authors
- Dan Xia (ORCID: https://orcid.org/0009-0009-4934-0675)
- Shanguo Huang (ORCID: https://orcid.org/0000-0002-0750-2340)
- Xiaolong Zheng (ORCID: https://orcid.org/0000-0001-7950-6773)
- Liang Liu (ORCID: https://orcid.org/0000-0002-5040-2468)
- Huadóng Ma (ORCID: https://orcid.org/0000-0002-7199-5047)
Institutions
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Journal
- IEEE Transactions on Mobile Computing
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1109/tmc.2025.3564340
- Citations
- 4
- Primary Topic
- Advanced MIMO Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 2.92