Adaptive signal processing for wireless sensor networks

无线传感器网络(WSNs)以其易于部署、低成本和可扩展性等优点，在我们的社会中扮演着越来越重要的角色。在过去的几年里，我一直将这项技术应用于加拿大许多具有重要战略意义的领域，如环境监测、精准农业、野生动物跟踪、高速公路安全、食品安全和医疗保健。基于我们以前在该领域的专业知识，该研究计划的主要目标是研究自适应信号处理算法，以便实现可靠、能量和带宽高效的无线传感器网络。自适应和认知的概念对于无线传感器网络的突破是重要的，因为它们的分布式拓扑结构，以及信道和网络条件的时变性质。我们将研究诸如自适应信道建模和识别、自适应多跳均衡、受限节点上的自适应调制和编码以及分布式认知信号估计和数据处理等创新技术。

Wireless sensor networks (WSNs) are gaining an increased role in our society due to their easy deployment, low cost and scalability. In the last few years I have been applying this technology in a number of strategically important areas to Canada such as environment monitoring, precision agriculture, wildlife tracking, highway safety, food safety, and health care. Built upon our previous expertise in this area, the main goal of this research program is to investigate adaptive signal processing algorithms in order to enable reliable, energy and bandwidth-efficient wireless sensor networks. The adaptive and cognitive concept is important to make a breakthrough in wireless sensor networks because of their distributed toplogy in addition to the time-varying nature of the channel and network conditions. We will investigate innovative techniques such as adaptive channel modeling and identification, adaptive multi-hop equalization, adaptive modulation and coding over constrained nodes, as well as distributed cognitive signal estimation and data processing.