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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637430
• 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.

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传感器，传感器网络正变得越来越智能。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，并在几次迭代中逐步开发。开放意味着系统必须处理多余的/相互冲突的需求。设计这种系统的理论基础已经在分布式系统（DS）研究中得到了研究，其中功能和/或控制是分布式的。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），详细讨论了分布式决策系统的相关理论和实践实现。在数据系统中，组件（如传感器、代理）的交互方式通常由场景（如序列图）来描述。在一个大规模的系统中，可能存在数千个这样的场景。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而昂贵的任务。例如，在商用无人机（UAV）机队中，每架无人机上都有多个传感器，异构无人机机队可能具有不同的运动场景和任务分配。无人机之间的充分通信使协调和动态任务分配成为可能。这些无人机逐渐组装成一个舰队。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行为，通常被称为组件级（例如每架无人机）的“紧急行为”（EB）和系统级（例如舰队中的无人机）的“隐含场景”（IS）。电子商务/信息系统可能会对用户、环境和业务造成昂贵和/或不可逆转的损害。