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，但其应用仍然局限于传统的人对人和人对计算机通信。然而，摩尔定律推动了成本和外形因素的不断降低，这使得网络（甚至是无线网络）和计算能力不仅可以嵌入到我们的PC和笔记本电脑中，还可以嵌入到其他物体中。此外，这些更小、更便宜的处理器和无线网络接口与新兴的基于MEMS技术的微传感器的结合，使廉价的传感、处理和通信功能能够不显眼地嵌入到熟悉的物理对象中。其结果是一种新兴的范式转变，其中信息技术的主要作用将是通过具有嵌入式（a）对外部刺激做出反应的微传感器和（B）用于与计算服务器和其他联网嵌入式对象进行无绳通信的无线联网和计算引擎的熟悉的物理对象来增强或协助“人到物理世界”的通信。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。特设结构，分布式性质，不可靠的传感，大规模/密度，和新的传感器数据类型的问题是这样的深度仪表化的物理环境与互联网络的物理对象的特征。这就要求人们重新思考当前的架构、协议、算法和为不同需求而开发的形式主义。此外，提供一个具体的问题域，我们建议使用和评估我们的技术在“智能幼儿园”的驱动程序应用程序，针对儿童早期教育的发展解决问题的环境。这是一个自然的应用程序，因为幼儿通过探索和与周围环境中的玩具等物体互动来学习。我们设想的系统将通过提供一个儿童学习环境来增强教育过程，该环境针对每个儿童进行个性化，适应上下文，协调多个儿童的活动，并允许教师对学习过程进行不显眼的评估。这将通过无线网络，传感器增强玩具与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： 用于使用短程无线电的网络的无线协议，重点关注嵌入式设备的高度非结构化，动态和密集网络，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，网络体系结构被设计用于通过对象能力和属性来命名、寻址和路由。 用于在仪表化环境中（特别是室内）识别、定位和跟踪用户和对象的高效技术和算法。 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、跟踪和更改通知。 数据管理方法，用于在高度动态的环境中处理来自多个异构、不可靠、噪声传感器的数据，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，以及它们在仪表化环境中的任务规划和动作执行中的使用 用于儿童语音的传感器辅助自动语音识别的技术。补充上述内容的将是驱动程序应用程序，其中用于发展问题解决的智能幼儿园将基于上述想法进行原型设计，并在真实的课堂环境中进行评估。各种各样的物体，特别是玩具，将被无线联网，并具有传感和可能的致动器能力。具有适合于处理高密度邻近对象的无线电和协议的无线网络将使用玩具网络中间件API将玩具彼此互连并将玩具互连到数据库和计算服务器。嵌入玩具中并由儿童佩戴的传感器将允许数据库服务器发现和跟踪有关儿童和玩具的上下文和配置信息，并协调听觉，视觉，运动，触觉和其他反馈。该系统将通过提供一个解决问题的环境来加强发展过程，该环境是个性化的，环境适应性的，并且在多个儿童之间进行协调。该项目团队是跨学科的，来自加州大学洛杉矶分校计算机科学和电子工程系的研究人员负责该项目的技术部分，来自加州大学洛杉矶分校教育和信息科学研究生院（GSE IS）的研究人员负责应用部分。GSE IS在校园内经营着一所著名的实验室小学，这所学校将用于对学生进行现实生活中的评估。

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