Collaborative Research: CNS Core: Medium: Information Freshness in Scalable and Energy Constrained Machine to Machine Wireless Networks

合作研究：CNS 核心：中：可扩展且能量受限的机器对机器无线网络中的信息新鲜度

基本信息

批准号：
2106932
负责人：
Ness Shroff
金额：
$ 25万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106932&HistoricalAwards=false
关键词：
Collaborative Research CNS Core Medium

项目摘要

With the ever increasing importance of connected devices in smart home, digital healthcare, precision agriculture, smart city, environment and natural disaster monitoring, etc., it is of paramount interest to design the next generation wireless network architecture that can simultaneously support better services while accommodating sharply exponential growth rates of deployment far exceeding the addition of newly available bandwidth. This project will design and analyze new near-optimal machine-to-machine (M2M) network protocols based on the key concept that the quality of service of the machine-based traffic is largely determined by how timely or how fresh the information can be delivered to the destination, instead of the sheer quantity of the delivered messages. With this new shift of design paradigm to information freshness optimization, this project develops novel tools and techniques to quantify and improve the information freshness while meeting the practical requirements of wireless M2M networks, especially on the scalability, energy efficiency, and low-complexity autonomous distributed solutions. The results would significantly advance the state-of-the-art knowledge on M2M wireless network architectures, and propel robust and continuous development of M2M applications by minimizing the battery consumption, increasing the network capacity, and improving the temporal “connectedness” among the smart devices, a critical step forward when realizing the societal impact of Internet-of-Things. To further broaden the participation in network science and computing, the project will implement multiple inclusive mechanisms that increase leadership and participation from women and under-represented groups in a national high-profile annual research workshop (IMACCS) that is being held at the Ohio State University. Several important technical challenges of M2M information freshness optimization will be addressed in this project, including (i) Optimal network coordination when any back and forth message always experiences some random delay, which results in delayed command-&-response in every aspect of the network operations. (ii) Lack of distributional knowledge. Since the delay distributions in practical networks are difficult to estimate and constantly change over time, any practically viable solution must automatically adapt to the underlying unknown delay distributions. (iii) Energy efficiency. Many smart devices are battery limited, which prompts the need for energy-centric, low-complexity distributed network protocol designs. This project will address the above key challenges and develop the analytical foundations for controlling and optimizing information freshness in wireless M2M networks, resulting in fully distributed provably efficient algorithms and protocols that will be extensively evaluated on a large-scale fully programmable 5G wireless network testbed at Rice University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着智能家居，数字医疗保健，精确协议，智能城市，环境和自然灾害监测等的连接设备的重要性越来越重要，设计下一代无线网络体系结构，可以简单地支持更好的服务，同时采用急剧指数的部署率，这远远超过了新近可用的带翼的添加远远超出了最高的兴趣。该项目将基于关键概念设计和分析新的近乎最佳的机器对机器（M2M）网络协议，即基于机器的流量的服务质量在很大程度上取决于如何及时或如何将信息传递到目的地，而不是将信息传递到目的地，而不是急需的信息。随着设计范式向信息新鲜优化的这种新的转变，该项目开发了新颖的工具和技术来量化和改善信息新鲜度，同时满足无线M2M网络的实际要求，尤其是在可扩展性，能源效率和低复杂性自动分布式解决方案方面。结果将大大提高M2M无线网络体系结构的最新知识，并通过最大程度地减少电池消耗，增加网络容量并提高智能设备之间的临时“连接性”，从而在实现Internet互联网的社交影响时，推动M2M应用程序的稳健和持续开发。为了进一步扩大网络科学和计算的参与，该项目将实施多种包容性机制，以增加妇女和代表性不足的群体的领导能力和参与，并在俄亥俄州立大学举行的国家备受瞩目的年度研究研讨会（IMACC）中。 M2M信息的几个重要技术挑战将在该项目中解决新鲜度优化，包括（i）当任何来回消息总是会经历一定的随机延迟时，最佳网络协调，这会导致网络操作各个方面的延迟命令 - ＆ - 响应。（ii）缺乏分销知识。由于实际网络中的延迟分布很难估算并随着时间的推移不断变化，因此任何实际可行的解决方案都必须自动适应基础未知的延迟分布。（iii）能源效率。许多智能设备受电池限制，该项目将应对上述主要挑战，并开发用于控制和优化无线M2M网络中信息新鲜度的分析基础，从而产生完全分布的适当有效的算法和协议，这些算法和协议将在赖斯大学进行大规模的5G无线网络测试，并在RICE University中进行了大规模的无线网络测试，并在莱斯大学中进行了大规模评估。基金会的智力优点和更广泛的影响评论标准。