Abstract-The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means for ZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy to in-packet blocks for recovering a certain number of random corruptions. Therefore the bursty nature of ZigBee in-packet corruptions under Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blocks have no errors but still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applying complex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may be helpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as "recovering any k consecutive errors" instead of "recovering any random k errors". This lowered requirement allows us to design far more efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simple yet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced. More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmission overhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47%) and latency (by 22%).

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Recently, Cross-Technology Communication (CTC), allowing the direct communication among heterogeneous devices with incompatible physical layers, has attracted much research attention. Many efficient CTC protocols have been proposed to demonstrate its promise in IoT applications. However, the applications built upon CTC will be significantly impaired when CTC suffers from malicious attacks such as jamming or sniffing. In this article, we implement a reactive jamming system, JamCloak, that can attack most existing CTC protocols. To this end, we first propose a taxonomy of the existing CTC protocols. Then based on the taxonomy, we extract essential features to train a CTC detection model, and estimate the parameters that can efficiently jam CTC links. Experimental results show that JamCloak consistently achieves 94.7% of classification accuracy on average in both Line-of-Sight and Non-Line-of-Sight scenarios. We also apply JamCloak to attack three existing CTC protocols: WiZig, Esense and EMF. Results show that JamCloak can significantly reduce PDR (packet delivery ratio) by 80.8% on average in practical environments. In the meantime, JamCloak’s jamming gain is more than 1.78× higher than the existing reactive jammer. In addition, we propose a practical countermeasure against reactive jamming attacks over CTC links like JamCloak. Results show that our approach significantly improves the jamming detection accuracy by 91.2% on average than the existing approach, and effectively decreases the reduction in packet delivery ratio to 1.7%.