NeTS-NOSS: Collaborative Research: LEAPNet: Self-adaptable All Terrain Sensor Networks

NetS-NOSS：合作研究：LEAPNet：自适应全地形传感器网络

• 批准号: 0721441
• 负责人: Li Xiao
• 金额: $ 60.89万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721441&HistoricalAwards=false
• 关键词: NeTS; NOSS; Collaborative; Research; LEAPNet

In deployment of sensor networks for various applications, sensors need to be placed in the areas of difficult terrain and natural obstacles. In such settings, many existing algorithms may perform poorly or may have high overhead while inefficiently consuming energy. One approach is to utilize mobile sensors in these situations, such as sensors with wheels. However, mobile wheeled sensors may not be able to move to the desired locations in the areas of difficult terrain with obstacles. Wheeled sensors can also be very expensive. A hopping sensor is a type of mobile sensor with a bionic mobility design that is inspired by creatures, such as grasshoppers. These sensors are still at an interesting concept stage. This proposal addresses the design, prototyping, and evaluation of hopping sensors and efficient algorithms for sensor deployment in difficult areas and rugged terrain. We focus on four research issues that are critical to the effective deployment and management of large scale sensor networks in such settings: robust and power-efficient hopping sensors, sensor localization, sensor coverage, and deployment of a self-adaptive all terrain sensor networks equipped with the hopping sensors and the proposed algorithms, which is called LEAPNet. Most future sensor networks are likely to be deployed in our targeted environments, and hence this research will benefit real-world applications such as monitoring ecosystems, disaster relief, and military reconnaissance. Strong collaborative efforts will be made to provide efficient and practical solutions with solid engineering designs and strong algorithmic foundations for this purpose. In addition, an innovative integration of the proposed research and education program will provide students with analytical skills and hands-on experiences by emerging technologies, which will better prepare them for strong technical careers in this rapidly growing and changing area.

在部署各种应用的传感器网络时，需要将传感器放置在地形复杂和自然障碍物的区域。在这种情况下，许多现有算法可能性能较差，或者可能具有较高的开销，同时低效地消耗能量。一种方法是在这些情况下使用移动传感器，例如带轮子的传感器。然而，在地形复杂、有障碍物的地区，移动轮式传感器可能无法移动到期望的位置。轮式传感器也可能非常昂贵。跳跃传感器是一种移动传感器，具有仿生移动性设计，灵感来自于蝗虫等生物。这些传感器仍处于有趣的概念阶段。这项提案涉及跳跃传感器的设计、原型制作和评估，以及在困难地区和崎岖地形中部署传感器的有效算法。重点研究了在这样的环境下大规模传感器网络的有效部署和管理的四个关键问题：健壮和高能效的跳跃传感器、传感器定位、传感器覆盖，以及部署配备跳跃传感器的自适应全地形传感器网络和所提出的算法LEAPNet。大多数未来的传感器网络很可能部署在我们的目标环境中，因此这项研究将有助于现实世界的应用，如监测生态系统、救灾和军事侦察。将作出强有力的协作努力，为此目的提供具有坚实的工程设计和坚实的算法基础的高效和实用的解决方案。此外，拟议的研究和教育计划的创新整合将通过新兴技术为学生提供分析技能和实践经验，这将使他们在这个快速增长和变化的领域为强大的技术职业生涯做好更好的准备。