Data analytics approach to design verification of distributed systems and sensor networks

用于分布式系统和传感器网络设计验证的数据分析方法

• 批准号: RGPIN-2017-04842
• 负责人: Far, Behrouz
• 金额: $ 1.75万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710777
• 关键词: Data; analytics; approach; design; verification

Sensor networks are at the front line of advanced analytic systems. Sensor networks are getting smarter through deployment of machine learning empowered sensors in many application areas including tactical systems, intelligent transportation, health care, environmental monitoring, oil drilling and smart power distribution. Application systems (i.e. infrastructure, hardware and software) based on smart sensor networks are usually open ended and developed incrementally in several iterations. Being open implies that the system must cope with superfluous/conflicting requirements. Theoretical basis of designing such systems has been studied in the distributed systems (DS) research in which functionality and/or control are distributed. DS related theories and their practical implementations are discussed in detail in software engineering and artificial intelligence, e.g. distributed software systems (DSS) and multi-agent systems (MAS). In DS the way components (e.g. sensors, agents) interact is usually described by scenarios (e.g. sequence diagrams). In a large scale system, thousands of such scenarios may exist. Maintaining consistency among scenarios in multiple iteration of design and development is a complex and expensive task. For example, in a commercial unmanned aerial vehicle (UAV) fleet, there are several sensors in each UAV and a fleet of heterogeneous UAVs may have different motion scenarios and task allocations. The full communication between the UAVs enables coordination and dynamic task allocation. The UAVs are assembled as a fleet, incrementally. Due to lack of centralized control and multiplicity of scenarios, the overall system may exhibit unintended/unexpected behavior, commonly known as “emergent behavior” (EB) at the component level (e.g. within each UAV) and “implied scenario” (IS) at the system level (e.g. UAVs in a fleet). EB/IS may lead to costly and/or irreversible damage to the users, environment, and the business. Our goal in this research is to manage (i.e. model, analyze, detect and resolve) unwanted behavior (i.e. EB and IS) by identifying design flaws that may lead to unwanted behavior as early as possible in the system development process. Unique points in this research are: (1) Modeling both interactions of components and their internal states together that saves the components' states and preserves the interaction information among the components; (2) Using interaction information to detect potential problems; (3) Ability to investigate whether new behavior can exist between components based on identified interactions; (4) Ability to suggest solutions based on the cause of the detected problem; (5) Ability to investigate interactions of the same-type components, which are common in sensor networks. Typical applications areas - extremely important to Canada - are intelligent transportation, energy, health and robotics, among the others.

传感器网络处于先进分析系统的前沿。通过在战术系统、智能交通、医疗保健、环境监测、石油钻探和智能配电等许多应用领域部署机器学习授权的传感器，传感器网络正在变得更加智能。基于智能传感器网络的应用系统(即基础设施、硬件和软件)通常是开放式的，并且分几次迭代递增地开发。开放意味着系统必须处理多余/冲突的需求。在功能和/或控制是分布式的分布式系统(DS)研究中，已经研究了设计此类系统的理论基础。在软件工程和人工智能领域，如分布式软件系统(DSS)和多智能体系统(MAS)中，详细讨论了DS的相关理论及其实际实现。在DS中，组件(例如传感器、代理)交互的方式通常由场景(例如序列图)来描述。在大规模系统中，可能存在数千种这样的场景。在设计和开发的多次迭代中维护场景之间的一致性是一项复杂且昂贵的任务。例如，在商用无人机(UAV)机队中，每架无人机都有几个传感器，而一组不同类型的无人机可能会有不同的运动场景和任务分配。无人机之间的充分通信实现了协调和动态任务分配。无人机以递增的方式组装成一个机队。由于缺乏集中控制和场景的多样性，整个系统可能会表现出意想不到的/意外的行为，通常被称为组件级别(例如，每架无人机内)的“紧急行为”(EB)和系统级别(例如，机队中的无人机)的“隐含场景”(IS)。EB/IS可能会对用户、环境和企业造成代价高昂和/或不可逆转的损害。 我们在这项研究中的目标是通过在系统开发过程中尽早识别可能导致有害行为的设计缺陷来管理(即建模、分析、检测和解决)有害行为(即EB和IS)。 该研究的独特之处在于：(1)将组件之间的交互及其内部状态一起建模，以保存组件的状态并保留组件之间的交互信息；(2)使用交互信息来检测潜在问题；(3)能够根据识别的交互来调查组件之间是否存在新的行为；(4)能够根据检测到的问题的原因提出解决方案；(5)能够调查传感器网络中常见的同类组件的交互。 对加拿大极其重要的典型应用领域包括智能交通、能源、健康和机器人等。