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With rapid development of Internet of Things (IoT), the number of deployed IoT devices increases dramatically to meet the requirements of various applications, such as smart homes, smart cities, and environmental monitoring. WiFi, ZigBee, and LoRa are well-known wireless technologies used to connect IoT devices. The trend of rapid growth shows that these devices will coexist in large quantities in the 6G era, leading to severe cross-technology coexistence problems. Our observation on the heterogeneous wireless networks reveals that the sensing of signal type, as well as signal channel, can assist wireless devices with more efficient transmission strategies. In this article, we propose a general signal identification method based on deep learning to predict the signal type and channel for the three kinds of signals. Specifically, we adopt object detection on the spectrogram images to achieve this task. We collect real signals based on a hardware testbed and construct a dataset for training and testing the model. We also make a preliminary investigation for this method, and the results demonstrate its high performance. Finally, we discuss a series of open challenges and directions for further research on this topic, and hope this article can inspire more researchers to conduct studies in this area.

With the prosperity of Internet of Things, a large number of heterogeneous wireless devices share the same unlicensed spectrum, leading to severe cross-technology interference (CTI). Especially, the transmission power asymmetry of heterogeneous devices will further deteriorate this problem, as the high-power devices (like WiFi) always corrupt the ongoing transmissions of low-power devices (like ZigBee), because the energy of the ZigBee signal perceived by WiFi is too weak to achieve the CCA (Clear Channel Assessment) threshold and the channel is considered to be idle. This paper proposes an enhanced CCA (E-CCA) mechanism to mitigate CTI in WiFi and ZigBee coexistence networks. E-CCA contains ZShark which can identify signals within a tolerable time duration. It also contains a MAC layer design, with which a WiFi device can still determine the channel to be busy when the detected energy is below the CCA threshold while a ZigBee signal is identified, thus to avoid CTI to ZigBee transmissions. We set up a testbed based on USRP B200 and TelosB to collect data and evaluate the performance of ZShark. We further evaluate the performance of E-CCA through both theoretical analysis and simulations on NS-3. The results show significant performance improvement of ZigBee.

With the rapid growth of Internet of Things, the number of heterogeneous wireless devices working in the same frequency band increases dramatically, leading to severe cross-technology interference. To enable coexistence, researchers have proposed a large number of mechanisms to manage interference. However, existing mechanisms have severe modifications in either the physical or MAC (medium access control) layers, making them very different from the standard. In this paper, we design and implement SledZig to boost cross-technology coexistence for low-power devices through both enabling more transmission opportunities and avoiding interference. SledZig is fully compatible with the standard in both physical and MAC layers. It decreases the WiFi signal power on the channel of low-power devices while keeps the WiFi transmission power unchanged, through making constellation points on the overlapped subcarriers have the lowest power, which can be achieved by just encoding the WiFi payload. We implement SledZig on hardware testbed and evaluate its performance under different settings. Experiment results show that SledZig can effectively increase ZigBee transmissions and improve its performance over a WiFi channel under various WiFi data traffic, with as low as 6.94$\%$ WiFi throughput loss. Authors