A self-adaptive MAC protocol for supporting mobile nodes in wireless sensor networks (Self-MACm)

无线传感器网络已经被提出了几种应用。其中一些节点需要移动节点，例如建议用于监督患者康复的节点。但机动性也带来了几个挑战。该项目将解决其中的两个问题，即(1)需要减少建立新链路的等待时间和(2)需要调整静态节点的占空比以支持节能和服务质量感知的多跳通信。对于这两个挑战，我们提出了采用贝叶斯估计的方法来建模和推理移动节点和静态节点之间的链路质量恶化速率以及中继节点的分组到达和离开速率。因此，我们将链路质量恶化和分组接收率建模为随机过程，并通过分析接收到的分组的质量在运行时动态更新它们的概率密度函数。然后我们使用密度函数来预测未来的链路质量和分组到达率。然后，该预测将用于执行无缝切换，并动态和异步地适应中继节点的占空比。

Several applications have been proposed for wireless sensor networks. Some of them, such as the ones proposed for supervising the rehabilitation of patients, require mobile nodes. But mobility brings with it several challenges. This project will address two of them, namely, (1) the need to reduce the latency of establishing new links and (2) the need to adapt the duty cycle of static nodes to support an energy-efficient and a quality-of-service-aware multi-hop communication. For both challenges, we propose to adopt Bayesian Estimation approach to model and reason about the deterioration rate of a link quality between a mobile and a static node and the packet arrival and departure rates of relay nodes. Thus, we model link quality deterioration and packet reception rate as random processes and dynamically update their probability density functions at runtime by analysing the quality of received packets. We then employ the density functions to predict future link quality and packet arrival rate. The prediction will then serve to carry out a seamless handover and to dynamically and asynchronously adapt the duty cycle of relay nodes.