Probabilistic Tomography of Wireless Networks

大规模无线网络有望在各种物联网(IoT)应用中普及，涉及环境感知和监测、通信和计算。无论是在网络建立的过程中，还是随着网络状态的变化，推导网络拓扑都是许多网络的一项基本任务。网络拓扑和性能信息的可用性对于大型无线系统的操作和管理至关重要，这些系统包括提供低延迟、高可靠性服务所需的低功率设备。例如，最先进的智能电表网络需要这些信息来执行路由和资源调度任务，而网络中设备数量的估计对于找出有多少传感器仍然处于活动状态或检测某些子网络的故障是有用的。在涉及国家安全的问题上，推断拓扑信息甚至具有非常重要的意义，在这种情况下，人们可能不得不被动地从外部观测数据(例如设备的频谱活动)来了解目标网络的结构，而不能访问网络设备和协议。许多网络特征可以通过观察端到端数据来推断，端到端数据通常采用数据包探测的形式。集中在这些技术上的一般研究领域被称为“网络层析成像”。在过去的二十年中，该领域已经发展到包括链路丢失统计(丢失层析)、内部排队延迟(延迟层析)和结构特征(拓扑层析)的推断。到目前为止，大部分工作都集中在最优和有效的估计方法的制定上，这些方法主要适用于对其拓扑结构表现出一定限制的计算机网络。最近的一些网络层析成像研究考虑了无线系统。然而，由于缺乏包含无线网络固有的空间和物理特征的可用统计模型，调查在很大程度上受到了限制。例如，空间(无线)网络表现出独特的特征(如传递性、聚类性)，这些特征在拓扑推理任务中尚未得到充分利用。该项目致力于开发改进的主动方法(拓扑发现)和被动技术(拓扑推断)来获得无线通信网络或其一部分的拓扑。其基本假设是，无线网络结构属性的概率知识可以作为先验信息来改进实际系统中的网络推理任务，特别是拓扑层析成像。该项目将首先对为特定应用设计的无线网络的正确建模和统计特性进行基础研究，例如智能电表基础设施和战术系统。这项研究的结果将被用来开发新的拓扑层析成像算法，这些算法被优化用于所选的应用程序。该项目的技术贡献将伴随和支持一些旨在通过传播和技术转让产生影响的活动。该项目得到了三个实际合作伙伴(东芝、Moogsoft和HMGCC)的支持，每个合作伙伴都处于各自领域的前沿。

Large-scale wireless networks are expected to become prevalent in various Internet-of-Things (IoT) applications involving environment sensing and monitoring, communications, and computing. It is a fundamental task of many networks to deduce the network topology, both during the establishment of the network and periodically as the network state evolves. The availability of network topology and performance information is crucial for the operation and management of large wireless systems comprising low-power devices that are required to provide low-latency, high-reliability services. For example, state-of-the-art smart meter networks require this information to carry out routing and resource scheduling tasks, and the estimation of the number of devices in a network is useful for finding out how many sensors are still active or for detecting failures of some subnetworks. Inferring topology information even possess great importance in matters of national security in which one may have to learn the structure of a target network passively from external observables, such as the spectral activity of devices, without having access to the network devices and protocols. Many network characteristics can be inferred by observing end-to-end data, which often takes the form of packet probes. The general field of study concentrating on such techniques is known as "network tomography". Over the past twenty years, this field has been developed to include the inference of link loss statistics (loss tomography), internal queuing delays (delay tomography), and structural characteristics (topology tomography). Much of the work to date has focused on the formulation of optimal and efficient estimation methods that are primarily geared toward computer networks that exhibit certain constraints on their topologies. Some more recent studies of network tomography have considered wireless systems. However, investigations have largely been limited by the lack of available statistical models that incorporate spatial and physical characteristics inherent to wireless networks. For example, spatial (wireless) networks exhibit distinctive features (e.g., transitivity, clustering), which have not been fully exploited in topology inference tasks. This project is concerned with developing improved active methods (topology discovery) and passive techniques (topology inference) of obtaining the topology of a wireless communications network or a portion thereof. The underlying hypothesis is that probabilistic knowledge of structural properties of wireless networks can be used as prior information to improve network inference tasks, particularly topology tomography, in practical systems. The project will begin with fundamental research into the correct modelling and statistical characterisation of wireless networks designed for particular applications, such as smart meter infrastructure and tactical systems. The results of this research will be exploited to develop new topology tomography algorithms that are optimised for use in the chosen applications. The technical contributions of the project will be accompanied and supported by a number of activities aimed at delivering impact through dissemination and technology transfer. The project is supported by three hands-on partners (Toshiba, Moogsoft, and HMGCC), each of which is at the leading edge of its respective field.