The Network is The Sensor

“网络即传感器”的标题改写了著名的“网络即计算机”，这句话启动了向网络化工作站的范式转变，作为可负担得起的计算的基础。我们宣布，网络现在也已成为传感基础设施的一个组成部分。除了传输数据外，网络设备还可以报告对网络本身的观察，以丰富最终用户应用程序。观察结果（我们称之为“网络感知”）可以从协议栈的各个层收集。例如，网络层信息，如IP地址，已经被用于用户位置推断和定向广告。在其他应用中，根据收集到的物理层接收信号强度指示（Received Signal Strength Indication， RSSI）信息，偷听附近的蓝牙低能量传输/信标用于室内定位。最近开发的接触追踪应用程序正是利用了这类信息。我们将通过寻找将网络传感与普通传感器数据融合的新方法来利用这一巨大的机会。例如，我们已经报道了一个这样的新应用，通过融合视觉信息和设备地址，这样每个被跟踪的个人的图像都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。提出的研究计划拓宽了传感，包括将网络传感与普通数据传感融合的系统方法。我们将从对物联网文献的系统调查开始，将其映射到联合主任实验室（JDL）融合模型。然后，我们将解决网络传感在数据处理链中提供最大利益的问题，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，以及其他外在因素，将导致质量参差不齐和间歇性的网络感知。我们将开发新的算法和技术来解决这些问题，将压缩和数据输入相结合。我们将通过一个虚拟化层来处理异构性，这样各种网络传感源就被组合成“虚拟传感器”（也可能与普通传感器一起）。我们将虚拟传感器概念与指标相结合，使我们能够确定何时将多个传感器组合成一个虚拟传感器是有益的，何时不是。我们将识别动态，例如运动，这可能需要重新组织虚拟传感器。我们的整体评估策略将基于大规模的联合模拟，其中现有的特定领域离散事件模拟器通过其主事件循环的同步进行合作。最后，我们将作为开源项目发布生成的（协同）模拟基础设施。

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.