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

在各种应用的传感器网络部署中，需要将传感器放置在困难地形和自然障碍物的区域中。在这样的设置中，许多现有的算法可能执行得很差，或者可能具有高开销，同时低效地消耗能量。一种方法是在这些情况下利用移动的传感器，例如具有轮子的传感器。然而，移动的轮式传感器可能无法移动到具有障碍物的困难地形区域中的期望位置。轮式传感器也可能非常昂贵。跳跃传感器是一种移动的传感器，其具有仿生移动性设计，其灵感来自生物，例如蚱蜢。这些传感器仍处于有趣的概念阶段。该提案涉及跳跃传感器的设计，原型设计和评估，以及在困难地区和崎岖地形中部署传感器的有效算法。我们专注于四个研究问题，这是至关重要的大规模传感器网络的有效部署和管理，在这样的设置：强大的和节能的跳频传感器，传感器定位，传感器覆盖范围，并部署一个自适应的全地形传感器网络配备跳频传感器和提出的算法，这是所谓的LEAPNet。大多数未来的传感器网络可能会部署在我们的目标环境中，因此这项研究将有利于现实世界的应用，如监测生态系统，救灾和军事侦察。我们将进行强有力的合作，提供高效实用的解决方案，并为此提供坚实的工程设计和强大的算法基础。此外，拟议的研究和教育计划的创新整合将为学生提供分析技能和新兴技术的实践经验，这将更好地为他们在这个快速增长和变化的领域的强大技术职业做好准备。

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.