Adaptive Learning Methods for Deeply Embedded Devices

智能手机已经改变了我们的日常生活，但一场更大的革命即将到来。未来将由深深嵌入我们环境中的微型计算设备组成（例如，房屋，车辆，衣服，道路，工作场所），它将感测和处理数据，并与其他设备和云通信。电子、传感器、网络和电池技术的进步将使制造这种设备成为可能，而支持这种新型计算机的软件技术还远未准备好。因此，我们的研究将探索改善与此计算平台相关的软件方法的途径，提出有效的方法来处理设备上的感测数据，同时处理它们提供的有限资源。本研究将从三个方面展开。首先，将研究基于人工智能的方法，以有效处理设备感测到的数据。让我们假设一个给定的感兴趣的对象，每个设备都有不同的视图。所遵循的方法是在网络级做出关于对象类别的决定之前，在设备上并根据它们各自的视图本地处理感测到的数据。这将涉及从一个通用的识别模型开始，该模型是从对象观察的大型数据库中产生的，然后根据每个设备运行的上下文和环境，以在线方式专门化该模型。作为第二个方面，我们将开发为设备提供自我管理能力的方法。这将导致设备性能的改善，并增加其能源自主性，所有这一切都很少或根本没有人为干预。我们的第三个目标是建立方法来设计这些设备上使用的软件系统，根据他们的能力和职责。为此，我们将开发技术，探索系统设计中不同的可能权衡。事实上，对于这些设备，为了实现更好的性能而增加处理通常会导致消耗更多的资源，可能超出可用的资源或显著降低其能量自主性。更好地理解与更好的传感性能相关的影响应该允许系统设计人员做出更明智的决定。这项研究对于支持深度嵌入式设备的开发是必要的，这是所谓的环境智能范式成为现实所必需的。在智能环境中，嵌入式设备正在帮助人们以不引人注目和自然的方式进行日常活动。这些微型设备将很好地集成在环境中，这样它们将从我们的周围环境中消失，唯一可见的元素是用户界面。这种新的计算模式对我们日常生活的影响将在生产力（例如自动化，资源管理，生活质量），安全（例如交通，公共安全）和健康（例如，疾病检测，第一反应）。该方案开发的技术应使加拿大在这一领域具有竞争优势，为开发更强大、能够适应和自我管理的软件创造知识产权和专门知识，这些是使计算走出办公室进入现实世界的关键素质。

Smartphones have changed our everyday life, but an even greater revolution is at our door. The future will be composed of tiny computing devices deeply embedded in our environment (e.g., houses, vehicles, clothes, roads, workplaces), which will sense and process data, and communicate with other devices and the Cloud. The advances in electronics, sensors, networking, and battery technologies will make it possible to build such devices, while the software technologies supporting this new class of computers is far from ready. Therefore, our research will explore avenues to improve software approaches relevant to this computing platform, by proposing efficient methods for processing the sensed data on the devices while dealing with the limited resources that they provide. Three aspects will be developed in the research. First, methods grounded in artificial intelligence will be investigated to allow an efficient processing of the data sensed by the devices. Let us assume a given object of interest, for which each device has a different view. The approach followed is to process the sensed data locally, on the devices and according to their respective view, before reaching a decision regarding the class of object at the network level. That would involve starting with a general recognition model, which is produced from large databases of object observations, and then specializing the model in an online fashion, according to the context and environment in which each device is operating. As a second aspect, we will develop methods to provide self-managing capabilities to the devices. This should lead to an improvement in the performance of the devices and increase their energy autonomy, all this with little or no human intervention. Our third objective is to establish methodologies to design the software system used on these devices in accordance with their capabilities and duties. For that, we will develop techniques that will explore the different possible trade-offs in the design of the systems. Indeed, with these devices, an increase in processing so as to achieve better performance generally leads to the consumption of more resources, possibly going beyond what is available or significantly reducing their energy autonomy. A better understanding of the impact associated with better sensing performance should allow the system designer to make a more informed decision. This research is necessary to support the exploitation of deeply embedded devices, required for the so-called ambient intelligence paradigm to become a reality. In an ambient intelligence world, the embedded devices are helping people to carry out their everyday activities in an unobtrusive and natural way. These miniature devices will be well integrated in the environment, such that they will disappear from our surroundings, the only visible element being the user interface. The impact of this new computing paradigm on our everyday life will be tremendous in terms of productivity (e.g. automation, resources management, quality of life), safety (e.g. transportation, public security), and health (e.g., disease detection, first response). The technologies developed in this programme should provide Canada with a competitive edge in this domain, creating intellectual property and expertise for developing software that is more robust, capable of adaptation and self-management, which are key qualities to allow computing to move out of the office and enter the real-world.