Cross-Technology Interference (CTI) badly harms the transmission reliability for low-power networks such as ZigBee at 2.4-GHz band. Though promising, channel hopping still faces challenges because the increasingly dense deployment of CTI leaves very few available channels. Selecting a good channel with the least overhead is crucial but challenging. Most of the existing works are heuristic methods that choose a channel far from the current one to avoid adjacent channels that may be correlatively interfered by CTI with a wider bandwidth such as WiFi. However, we observe that the correlated channels influenced by the same CTI source do not necessarily have the same channel qualities and even the opposite state, due to the uneven spectrum power density of CTI. Such channel opportunities are unexplored and wasted. In this article, we propose CoHop, a quantitative correlation-based channel hopping method for low-power wireless networks. We establish a quantitative model that describes the correlation of channel qualities to capture channel opportunities and calculate channel quality without probing, to reduce probing overhead. The probing sequence is optimized based on the Pearson Correlation Coefficient and the prediction-based probing algorithm. We implement CoHop on TinyOS and evaluate its performance in various environments. The experimental results show that CoHop can increase the Packet Reception Ratio by 80%, compared with existing methods.

中文翻译：

---

合跳

交叉技术干扰 (CTI) 严重损害了低功率网络（例如 2.4 GHz 频段的 ZigBee）的传输可靠性。尽管前景广阔，但频道跳频仍然面临挑战，因为 CTI 日益密集的部署使得可用频道非常少。选择开销最少的好渠道至关重要，但也具有挑战性。现有的大多数工作都是启发式方法，它们选择远离当前信道的信道，以避免相邻信道可能受到具有更宽带宽的 CTI 相关干扰，例如 WiFi。然而，我们观察到，由于 CTI 的频谱功率密度不均匀，受相同 CTI 源影响的相关通道不一定具有相同的通道质量甚至相反的状态。这样的渠道机会是未经开发和浪费的。在本文中，我们提出 CoHop，一种用于低功率无线网络的基于定量相关的信道跳频方法。我们建立了一个描述信道质量相关性的定量模型，以捕获信道机会并在不进行探测的情况下计算信道质量，以减少探测开销。基于皮尔逊相关系数和基于预测的探测算法优化探测序列。我们在 TinyOS 上实现 CoHop，并评估其在各种环境中的性能。实验结果表明，与现有方法相比，CoHop 可以将数据包接收率提高 80%。以减少探测开销。基于皮尔逊相关系数和基于预测的探测算法优化探测序列。我们在 TinyOS 上实现 CoHop，并评估其在各种环境中的性能。实验结果表明，与现有方法相比，CoHop 可以将数据包接收率提高 80%。以减少探测开销。基于皮尔逊相关系数和基于预测的探测算法优化探测序列。我们在 TinyOS 上实现 CoHop，并评估其在各种环境中的性能。实验结果表明，与现有方法相比，CoHop 可以将数据包接收率提高 80%。