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

研究表明，异构传感器网络（HSNs）可以显著提高传感器网络的性能。为了获得更好的性能，我们采用了一种由少量功能强大的高端传感器（h传感器）和大量低端传感器（l传感器）组成的HSN模型。该项目的目标是研究hsn的创新网络架构，并为hsn开发节能、自修复方案和路由协议。我们计划建立一个综合研究和教育项目。本课题的研究内容包括以下两部分：。研究高效、稳健的hsn网络架构。我们将研究两种不同类型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，同时确保网络寿命和覆盖要求。我们提出了一种新的密度变化部署方案。我们还将设计健壮的集群方案，可以容忍h传感器故障并提供可靠的网络结构。设计自修复和节能方案和路由协议的hsn。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。典型的传感器节点都是小而不可靠的设备，能量供应有限。路由协议应该具有高能效和对传感器故障的鲁棒性，并且能够在节点故障时找到新的路径。通过利用强大的h传感器，我们将为考虑数据融合的hsn设计自修复、节能的路由协议。该研究与一个教育项目紧密结合，该项目包括以下四个主题：1)指导研究生和本科生，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)开设新的研究生课程——无线传感器网络。3)传感器网络的实地研究。在北达科他州的几个农场部署了传感器网络用于农业监测，在田纳西州的几个化学/核电站部署了传感器网络用于危害监测。我们将带学生到农场和工厂，研究如何通过应用我们的研究成果来提高这些真实传感器网络的性能。4)建立异构传感器网络实验室，将科研与教学相结合。该研究的智力优势包括：1)在本研究中，我们将针对两种不同类型的hsn，即h传感器的位置是可控的或不可控的，开发创新的网络架构。2)我们将为hsn设计节能且自愈的路由协议，该协议对节点故障具有鲁棒性，并延长网络寿命。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、低收入群体、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