NeTS: Small: Collaborative Research: Advanced Algorithmic Tools for Discovery in Cognitive Radio Networks

嵌入式计算和无线技术的普及为技术提供和改善安全和服务带来了新的机遇。这些应用包括用于提高安全性和减少拥塞的动态流量管理，用于灾难恢复的自组织网络，以及用于军事或科学目的的传感器网络。在所有这些情况下，无线设备必须发现彼此的存在，配置其无线电以最大限度地提高效率，并节省电池电量。该项目将开发新的方法来解决这些发现和效率的基本问题。此外，该项目将开发新的技术和工具，用于评估拟议解决方案的有效性。该项目还包括开发本科生和研究生课程，通过为高中教师和学生提供教育机会开展外联活动，扩大本科生和代表性不足的群体参与研究，并支持女学生学习计算机，该项目旨在开发和分析用于时空同行发现的先进算法工具，这是特设网络的基本要素。该提案的重点是认知无线电网络的特点是高移动性，拥挤的频谱，运动引起的信道中断和能源受限的商品设备。开发具有可证明保证和强大实用性能的高效发现方案对于构建此类网络以用于大规模应用（例如道路安全和无人驾驶车队的应急响应）至关重要。该项目有四个主要目标：（1）多信道发现，其中配备有频谱捷变无线电信道的节点使用确定性和概率性技术来实现多信道发现，从建设性Ramsey理论、循环代数几何码、数论筛和一种新的伪随机结构：同步采样器中汲取灵感;（2）能量受限的单信道发现算法，其中移动的节点通过与加法组合学、代数几何学和扩展图的连接进行工作循环以节省电池;（3）通过应用来自计算和凸几何学的技术来估计车辆环境内的轨迹，通过信号强度进行空间发现;以及（4）通过仿真环境使用具有多信道车辆网络的适当扩展的ns-3仿真器进行验证。虽然专注于车载网络，但这些技术也可以应用于各种不同的网络，例如医疗和遥测网络。

The prevalence of embedded computing and wireless technology has given rise to remarkable new opportunities for technology to provide and improve safety and services. Such applications include on-the-fly traffic management to improve safety and reduce congestion, establishing ad-hoc networks for disaster recovery, and maintaining sensor networks for military or scientific purposes. In all these cases, wireless devices must discover each other's existence, configure their wireless radios to maximize efficiency, and conserve battery power. This project will develop new methods to address these fundamental problems of discovery and efficiency. Additionally, the project will develop and new techniques and tools for evaluating the efficacy of proposed solutions. The project also incorporates development of undergraduate- and graduate-level courses, outreach via educational opportunities for high-school teachers and students, expanding participation of undergraduates and underrepresented groups in research, and support for female students in computing.This project aims to develop and analyze advanced algorithmic tools for spatio-temporal peer discovery, a fundamental primitive for ad-hoc networks. The proposal focuses on cognitive radio networks characterized by high mobility, congested spectrum, motion-induced channel outages and energy-constrained commodity devices. The development of efficient discovery schemes with both provable guarantees and strong practical performance will be critical for the build out of such networks for large scale applications such as road safety and emergency response for driver-less fleets. The project has four principal thrusts: (1) Multi-channel discovery, where nodes equipped with spectrally agile radios channel hop to achieve rendezvous using deterministic and probabilistic techniques for achieving multi-channel discovery, drawing inspiration from constructive Ramsey theory, cyclic algebraic geometry codes, number theoretic sieves, and a novel pseudo-random construction: the synchronizing sampler; (2) Energy-constrained single channel discovery algorithms where mobile nodes duty cycle to save battery, via connections with additive combinatorics, algebraic geometry, and expander graphs; (3) Spatial discovery through signal strength by applying techniques from computational and convex geometry for estimations of trajectories within vehicular environments; and (4) Validation via a simulation environment using the ns-3 simulator with appropriate extensions for multi-channel vehicular networks. While focusing on vehicular networks, these techniques can be applied in a wide variety of different networks, such as medical and telemetry networks as well.