ITR: Technologies for Sensor-based Wireless Networks of Toys for Smart Developmental Problem-solving Environments

尽管网络和计算技术取得了巨大进步，但它们的应用仍然局限于传统的人与人之间和人与计算机之间的通信。然而，摩尔定律推动了成本和外形的持续降低，现在使网络-甚至无线网络-和计算功能不仅嵌入到我们的PC和笔记本电脑中，而且还嵌入到其他对象中。此外，这些越来越小、更便宜的处理器和无线网络接口与基于MEMS技术的新兴微型传感器相结合，使得廉价的传感、处理和通信功能可以悄悄地嵌入到熟悉的物理对象中。其结果是一种正在出现的范式转变，其中信息技术的主要作用将是加强或协助通过具有嵌入式(A)对外部刺激作出反应的微型传感器和(B)无线网络和计算引擎与计算服务器和其他联网嵌入式对象进行无绳通信的熟悉的物理对象的“人与现实世界”通信。拟议的研究旨在探索实现上述愿景的无线网络、中间件和数据管理技术。自组织结构、分布式性质、不可靠的传感、大规模/密度和新颖的传感器数据类型等问题是具有互联的物理对象的这种深度仪器化的物理环境的特征。这要求人们重新考虑为不同需求开发的当前体系结构、协议、算法和形式化。此外，为了提供一个具体的问题领域，我们建议在一个旨在为幼儿教育解决发展问题的环境的“智能幼儿园”驱动程序应用程序中使用和评估我们的技术。这是一种自然的应用，因为幼儿通过探索和与环境中的玩具等对象互动来学习。我们设想的系统将通过为每个孩子提供个性化的童年学习环境来增强教育过程，该环境适应环境，协调多个孩子的活动，并允许教师对学习过程进行不引人注目的评估。这将通过无线联网、传感器增强型玩具以及后端中间件服务和数据库技术来实现。这项研究的主要信息技术贡献将是：使用短程无线电的网络的无线协议，重点是高度非结构化、动态和密集的嵌入式设备网络，以及传感器数据的能源效率和服务质量需求问题。与传统网络中基于ID的方法相反，被设计用于通过对象能力和属性来命名、寻址和路由的网络体系结构。用于识别、定位和跟踪仪表化环境中的用户和对象的高效技术和算法，尤其是在室内。中间件体系结构提供服务，例如特殊通信模式、在属性和容量约束下的上下文感知网络资源分配和调度、功率感知操作、使用共享后台服务器的媒体处理以及上下文发现、跟踪和改变通知。数据管理方法，在高度动态的环境中处理来自多个不同类型、不可靠、有噪声的传感器的数据，并支持实时传感器数据解释和融合以及离线挖掘。从传感器数据中自动挖掘用户配置文件，并将其用于仪表化环境中的任务规划和操作执行，用于传感器辅助的儿童语音自动识别技术。作为上述补充，驾驶员应用程序将基于以上想法构建用于解决发展问题的智能幼儿园的原型，并在真实的课堂环境中进行评估。各种物体，特别是玩具，将被无线联网，并具有传感和可能的致动器功能。无线网络具有适用于处理高密度接近对象的无线电和协议，将使用玩具网络中间件API将玩具彼此互连并互连到数据库和计算服务器。嵌入玩具和儿童佩戴的传感器将允许数据库服务器发现和跟踪关于儿童和玩具的背景和配置信息，并协调听觉、视觉、运动、触觉和其他反馈。该系统将通过在多个儿童之间提供一个个性化、上下文适应和协调的问题解决环境来促进发展进程。它还将允许教师或家长监控和记录非显眼的无纸化评估。项目团队是跨学科的，来自加州大学洛杉矶分校CS和EE部门的研究人员负责项目的技术部分，来自加州大学洛杉矶分校教育与信息科学研究生院(GSE&Amp；IS)的研究人员负责应用程序部分。GSE&amp；IS在校园内经营着一所著名的实验小学，这所小学将用于现实生活中的

Despite enormous progress in networking and computing technologies, their application has remained restricted to conventional person-to-person and person-to-computer communication. However, the Moore's Law driven continual reduction in cost and form factor is now making it possible to imbed networking - even wireless networking - and computing capabilities not just in our PCs and laptops but also other objects. Further, a marriage of these ever tinier and cheaper processors and wireless network interfaces with emerging micro-sensors based on MEMS technology is allowing cheap sensing, processing, and communication capabilities to be unobtrusively embedded in familiar physical objects. The result is an emerging paradigm shift where the primary role of information technology would be to enhance or assist in "person to physical world" communication via familiar physical objects with embedded (a) micro-sensors to react to external stimuli, and (b) wireless networking and computing engines for tetherless communication with compute servers and other networked embedded objects. The proposed research seeks to explore wireless networking, middleware, and data management technologies for realizing the above vision. The problems of ad hoc structure, distributed nature, unreliable sensing, large scale/density, and novel sensor data types are characteristic of such deeply instrumented physical environments with inter-networked physical objects. This requires one to rethink current architectures, protocols, algorithms, and formalisms that were developed for different needs. Further, to provide a concrete problem domain, we propose to use and evaluate our technologies in a "smart kindergarten" driver application targeted at developmental problem-solving environments for early childhood education. This is a natural application as young children learn by exploring and interacting with objects such as toys in their environment. Our envisioned system would enhance the education process by providing a childhood learning environment that is individualized to each child, adapts to the context, coordinates activities of multiple children, and allows unobtrusive evaluation of the learning process by the teacher. This would be done by wirelessly-networked, sensor-enhanced toys with back-end middleware services and database techniques. The main information technology contributions of this research would be: Wireless protocols for networks using short-range radios, with focus on highly unstructured, dynamic, and dense networks of embedded devices, and problems of energy efficiency and quality of service needs of sensor data. Network architectures designed for naming, addressing, and routing by object capabilities and attributes, as opposed to id based approaches in conventional networks. Efficient techniques and algorithms for identifying, locating, and tracking users and objects in instrumented environments, particularly indoors. Middleware architecture providing services such as special communication patterns, context-aware network resource allocation and scheduling under attribute and capacity constraints, power-aware operation, media processing using shared background servers, and context discovery, tracking, and change notification. Data management methods to handle data from multiple heterogeneous, unreliable, noisy sensors in a highly dynamic environment, with support for real-time sensor data interpretation and fusion, and off-line mining. Automated mining of user profiles from sensor data, and their use in task planning and execution of actions in the instrumented environment Techniques for sensor-assisted automatic speech recognition of children's speech.Complementing the above will be the driver application where a Smart Kindergarten for developmental problem solving will be prototyped based on the above ideas, and evaluated in a real classroom setting. Various objects, particularly toys, will be wirelessly networked and have sensing and perhaps actuator capabilities. A wireless network, with radios and protocols suitable for handling a high density of proximate objects, will interconnect the toys to each other and to database and compute servers using a toy network middleware API. Sensors embedded in toys and worn by children will allow the database servers to discover and track context and configuration information about the children and the toys, and also orchestrate aural, visual, motion, tactile and other feedback. The system will enhance the developmental process by providing a problem-solving environment that is individualized, context adaptive, and coordinated among multiple children. It will also allow monitoring and logging for unobtrusive paper-free assessment by teacher or parent.The project team is interdisciplinary, with researchers from UCLA's CS and EE Departments for the technology component of the project, and from UCLA's Graduate School of Education and Information Sciences (GSE&IS) for the application component. GSE&IS operates a reputed laboratory elementary school on campus, which will be used for real-life evaluation of