NeTS-NOSS: Collaborative Research: Energy-Efficient Distributed Sensor Network Control: Theory to Implementation

能量管理是延长无线传感器网络生命周期和增加有用信息传输量的关键。 通过利用各种网络层和功能之间的跨层交互，有可能大大提高传感器网络的能源效率。然而，这种跨层解决方案也可能变得不切实际地复杂和难以实现。 为了应对这一挑战，这个合作NSF资助的项目在俄亥俄州州立大学和普渡大学的目标是开发一套高性能的传感器网络的跨层机制，是简单的，模块化的，分布式的，并证明能源效率。 该项目的主要特点是，开发的机制基于坚实的理论基础，严格管理性能和复杂性，以实现实际实施的目标。 PI将研究对能量受限的无线传感器网络的有效运行至关重要的四个主要功能，包括联合介质访问和路由、睡眠/唤醒调度、网络内聚合/计算和可靠广播。本项目中开发的理论解决方案将在两个测试平台上实施和评估：OSU现有的Kansei测试平台和原型部署（SENSE@OSU）。更广泛的影响：合作研究将对无线行业产生重大影响，并将导致我们对开发传感器网络节能分布式解决方案的基本限制的理解取得突破。公共机构还将继续其目前的努力，在本科和研究生两级招聘和培训妇女和代表性不足的少数群体。

Energy-management is essential for wireless sensor networks to prolong network lifetime and to increase the amount of useful information conveyed. There is potential to substantially improve the energy efficiency of sensor networks by exploiting cross-layer interactions among various networking layers and functionalities. However, such a cross-layer solution could also become impractically complex and difficult to implement. To address this challenge, this collaborative NSF-funded project at The Ohio State University and Purdue University aims to develop a suite of high-performance cross-layer mechanisms for sensor networks that are simple, modular, distributed, and provably energy-efficient. The key distinguishing feature of the project is that the mechanisms developed are based on a solid theoretical foundation that rigorously manages both performance and complexity with the goal of practical implementation. The PIs will investigate four major functionalities that are crucial to the efficient operation of energy-constrained wireless sensor networks, including joint medium-access and routing, sleep/wake scheduling, in-network aggregation/computation, and reliable broadcast. The theoretical solutions developed in this project will be implemented and evaluated on two testbeds: the existing Kansei testbed at OSU, and a prototype deployment (SENSE@OSU).Broader impact: The collaborative research will have a significant impact on wireless industry sectors, and will lead to breakthroughs in our understanding of the fundamental limits for developing energy-efficient distributed solutions for sensor networks. The PIs will also continue their current efforts to recruit and train women and under-represented minority groups both at the undergraduate and graduate levels.