CAREER: Software Adaptation and Synthesis Techniques for Internet of Things Systems

智能手表、智能眼镜和云助听器等可穿戴物联网(IoT)设备有望改善我们的日常生活。然而，开发这样的系统是具有挑战性的。系统开发人员必须考虑到用户和硬件平台的差异，并预期系统将如何应对不断变化的运行条件。例如，助听器可以使用其板载处理器在相对安静的环境中增强语音，但在用户遇到更嘈杂的环境时依赖更强大的云计算资源来增强语音。目前，开发人员缺乏有效的编程语言和工具来帮助他们创建和改进这样的物联网系统。本项目研究如何使用策略驱动的软件适配和合成来开发自适应物联网系统。我们方法的核心是将系统的功能(封装在软件组件中)与其运行时适配(指定为策略)分开。开发人员将被提供一种语言来编写策略，这些策略控制组件的执行时间、组件的并发性，并动态选择应该使用哪个替代组件实现。策略语言将与数据驱动技术相结合，这些技术集成了模拟、程序分析和机器学习技术，以配置策略参数并合成新策略。作为该提案的一部分进行的研究将为构建自适应物联网系统提供坚实的基础。初步研究活动将有助于缩短物联网系统的开发时间并提高其稳健性，使这项技术更接近实现。主要的教育活动包括将有关物联网系统的新主题纳入操作系统课程，将有关软件适配和综合的主题纳入移动计算课程。这些努力将创造一条对系统研究广泛感兴趣的本科生和研究生的渠道。作为该项目的一部分获得的出版物、教育材料、软件制品和成果将在该项目完成后的五年内在http://cs.uiowa.edu/~ochipara/career上公开提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Wearable Internet-of-Things (IoT) devices, such as smart watches, smart glasses, and cloud-enabled hearing aids promise to improve our everyday lives. However, developing such systems is challenging. System developers must account for the differences in users and hardware platforms, and anticipate how a system will respond to the changing operating conditions. For example, a hearing aid may use its onboard processor to augment speech in relatively quiet surroundings but rely on more powerful cloud computing resources to enhance speech as users encounters more noisy environments. Today, developers lack effective programming languages and tools to help them create and improve such IoT systems.This project investigates how adaptive IoT systems may be developed using policy-driven software adaptation and synthesis. Central to our approach is to separate the functionality of a system (encapsulated in software components) from its run-time adaptation (specified as a policy). Developers will be provided with a language to write policies that control when components are executed, their concurrency, and dynamically selects which alternate component implementation should be used. The policy language will be combined with data-driven techniques that integrate simulation, program analysis, and machine learning techniques to configure the parameters of a policy as well as synthesize new policies.The research undertaken as part of this proposal will provide a solid foundation for building adaptive IoT systems. The primary research activities will help reduce the development time and improve the robustness of IoT systems, bringing this technology closer to fruition. The primary educational activities include incorporating new topics on IoT systems into the Operating Systems curriculum and topics on software adaptation and synthesis into the Mobile Computing curriculum. These efforts will create a pipeline of undergraduate and graduate students that are broadly interested in systems research. The publications, educational materials, software artifacts, and results obtained as part of this project will be made publically available at http://cs.uiowa.edu/~ochipara/career for five years following the completion of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.