The Network is The Sensor

《网络即传感器》一书引用了著名的《网络即计算机》一书，这本书开启了向网络工作站的范式转变，将其作为负担得起的计算的基础。我们宣布，联网现在也已成为传感基础设施的组成部分。除了传输数据外，网络设备还可以报告有关网络本身的观察结果，以丰富最终用户应用程序。可以从协议栈的各个层收集观测数据(我们将其称为“网络侦听”)。例如，诸如IP地址的网络层信息已经用于用户位置推断和定向广告。在其他应用中，监听附近的蓝牙低能量传输/信标用于基于收集的物理层接收信号强度指示(RSSI)信息的室内定位。最近开发的接触者跟踪应用程序正是利用了这种类型的信息。我们将利用这一巨大的机会，通过寻找新的方法来融合网络传感和普通传感器数据。例如，我们已经报道了一种通过融合视觉信息和设备地址的新型应用，使得每个被跟踪的人的图像都被标注了他们在任何时间点拥有的设备地址。该应用程序不仅展示了网络和视觉传感的融合，还展示了学习和预测算法如何从这种数据融合中获益。拟议的研究计划扩大了传感范围，包括一种将网络传感与普通数据传感融合在一起的系统方法。我们将首先对物联网文献进行系统调查，将其映射到联合董事实验室(JDL)融合模型。然后，我们将讨论网络侦听在数据处理链中提供最大好处的问题，以及我们如何在资源有限的设备上支持它。我们承认，有限的资源以及其他外部因素将导致网络侦听质量参差不齐和断断续续。我们将开发新的算法和技术来解决这些问题，将压缩和数据补偿结合起来。我们将通过虚拟化层处理异构性，以便将各种网络感知源(也可能与普通传感器一起)组合成“虚拟传感器”。我们将把虚拟传感器的概念与指标结合起来，使我们能够确定何时将多个传感器合并为一个虚拟传感器是有益的，以及何时不是有益的。我们将认识到动态，例如运动，这可能需要重新组织虚拟传感器。我们的总体评估策略将基于大规模协同仿真，其中现有的特定于领域的离散事件仿真器通过其主事件循环的同步进行协作。最后，我们将把生成的(共同)模拟基础设施作为一个开放源码项目发布。

The title "The Network is The Sensor" paraphrases the well-known "The Network is The Computer" that launched a paradigm shift to networked workstations as the basis for affordable computing. We pronounce that networking has now also become an integral part of the sensing infrastructure. Apart from transferring data, networking devices can report observations about the network itself to enrich end-user applications. The observations (which we will call "network sensing") can be collected from various layers of the protocol stack. As an example, network layer information, such as IP addresses, are already used for user location inference and targeted advertising. In other applications, overhearing nearby Bluetooth Low Energy transmissions/beacons is used for indoor localization based on collected physical layer Received Signal Strength Indication (RSSI) information. Recently developed contact-tracing applications exploit precisely this type of information. We will exploit this tremendous opportunity by finding novel ways to fuse network sensing with ordinary sensor data. For example, we have reported on one such novel application by fusing visual information and device addresses, such that each tracked individual's image is annotated with the device address(es) they have in their possession at any point in time. The application showcases not just the fusion of network and visual sensing, but also how learning and prediction algorithms can reap benefits from such data fusion. The proposed research program broadens sensing to include a systematic way of fusing network sensing with ordinary data sensing. We will start with a systematic survey of Internet-of-Things literature mapping it to the Joint Directors Lab (JDL) fusion model. We will then address issues of where network sensing provides the most benefit in the data-processing chain, and how we can support it on devices with limited resources. We accept that the limited resources, as well as other extrinsic factors, will result in varying quality and intermittent network sensing. We will develop new algorithms and techniques to address these issues as a combination of compression and data imputation. We will handle heterogeneity via a virtualization layer such that various network sensing sources are combined (possibly together with ordinary sensors as well) into "virtual sensors". We will combine the virtual sensor concept with metrics that allow us to identify when it is beneficial to combine multiple sensors into one virtual and when not. We will recognize the dynamics, e.g. movement, that could require the re-organization of the virtual sensors. Our overall evaluation strategy will be based on large-scale co-simulations, where existing domain-specific discrete event simulators cooperate via a synchronization of their main event loops. Finally, we will release the resulting (co-)simulation infrastructure as an open source project.