NeTS NOSS: Collaborative Research: Towards Robust and Self-Healing Heterogeneous Wireless Sensor Networks

研究表明，异构传感器网络能够显著提高传感器网络的性能。为了实现更好的性能，我们采用了一个HSN模型，由少量功能强大的高端传感器（H传感器）和大量的低端传感器（L传感器）。本计画的目标是研究高速网路的创新网路架构，并发展高效能、自我修复的高速网路架构与路由协定。我们计划建立一个综合的研究和教育计划。该项目的研究部分包括以下两个部分：研究HSN的高效和健壮的网络架构。我们将研究两种不同类型的HSN的创新网络架构：H传感器位置可控和不可控的HSN。我们将确定H-传感器和L-传感器的最佳密度，以及H-传感器的最佳位置，以最小化传感器节点的成本，同时确保网络的寿命和覆盖要求。我们提出了一种新的密度变化部署方案的H-传感器。我们还将设计强大的集群方案，可以容忍H传感器故障，并提供可靠的网络结构。为HSNs设计自愈和节能的方案和路由协议无线传感器网络的主要功能是感知环境并将获取的信息传输到基站进行进一步处理。因此，路由是传感器网络中必不可少的操作。典型的传感器节点是小型的、不可靠的设备，具有有限的能量供应。路由协议应该是能量有效的和鲁棒的传感器故障，并能够找到新的路径时，节点发生故障。通过利用强大的H传感器，我们将设计自愈，能源效率的路由协议，考虑到数据融合的高速传感器网络。 该研究与一个教育计划紧密结合，该计划包括以下四个主题：1）指导研究生和本科生，并招募北达科他州和田纳西州代表性不足群体的学生参与该项目。2)开发一门新的研究生课程-无线传感器网络。3)传感器网络的实地研究。传感器网络已经部署在北达科他州的几个农场用于农业监测，以及田纳西州的几个化学/核工厂用于危险监测。我们将带学生到农场和工厂去研究如何应用我们的研究成果来提高这些真实的传感器网络的性能。4)通过建立异构传感器网络实验室，将研究与教育结合起来。 本论文的创新之处在于：1）在本研究中，我们将针对两种不同类型的高速网路开发创新的网路架构，H传感器的位置是否可控。2)我们将为高速传感器网络设计能量有效和自我修复的路由协议，这是鲁棒的节点故障和延长网络的生命周期。更广泛的影响是：招收代表性不足的群体的学生，包括北达科他州的女性，低收入，第一代，美洲原住民和非洲裔美国学生

Research has shown that Heterogeneous Sensor Networks (HSNs) can significantly improve the performance of sensor networks. To achieve better performance, we adopt an HSN model consisting of a small number of powerful high-end sensors (H-sensors) and a large number of low-end sensors (L-sensors). The objective of this project is to investigate innovative network architectures of HSNs, and develop energy-efficient, self-healing schemes and routing protocols for HSNs. We plan to build an integrated research and education program. The research components of the project consist of the following two parts: . Investigating efficient and robust network architectures of HSNs.We will investigate innovative network architectures for two different types of HSNs: HSNs where the locations of H-sensors are controllable and NOT controllable. We will determine the optimal density of H-sensors and L-sensors, and the optimal locations of H-sensors to minimize the cost of sensor nodes while ensuring a network lifetime and coverage requirement. We propose a novel Density-Varying-Deployment scheme for H-sensors. We will also design robust clustering schemes that can tolerate H-sensor failures and provide reliable network structures.. Designing self-healing and energy-efficient schemes and routing protocols for HSNs.The primary functionality of wireless sensor networks is to sense the environment and transmit the acquired information to a base station for further processing. Thus, routing is an essential operation in sensor networks. Typical sensor nodes are small, unreliable devices with limited energy supply. The routing protocols should be energy-efficient and robust to sensor failures, and be able to find new paths when nodes fail. By utilizing powerful H-sensors, we will design self-healing, energy-efficient routing protocols for HSNs which take into consideration of data fusion. The research is tightly coupled with an educational program that includes the following four themes, 1) Mentoring graduate and undergraduate students, and recruiting students of underrepresented groups in North Dakota and Tennessee to participate in the project. 2) Developing a new graduate course-Wireless Sensor Networks. 3) Field study of sensor networks. Sensor networks have been deployed in several farms in North Dakota for agricultural monitoring and several chemical/nuclear plants in Tennessee for hazard monitoring. We will take students to the farms and plants to study how to improve the performance of these real sensor networks by applying our research results. 4) Integrating research and education together by setting up a Heterogeneous Sensor Network Lab. The Intellectual Merits include:1) In this research, we will develop innovative network architectures for two different kinds of HSNs, i.e., the locations of H-sensors are controllable or not. 2) We will design energy-efficient and self-healing routing protocols for HSNs, which are robust to node failures and prolong network lifetime. The Broader Impacts are:Recruiting students of underrepresented groups, including female, low incoming, first generation, Native American, and African American students in North Dakota