Intent-Based Network Management of Software Defined Wireless Sensor Networks

软件定义无线传感器网络的基于意图的网络管理

• 批准号: RGPIN-2019-04454
• 负责人: Liscano, Ramiro
• 金额: $ 1.68万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752804
• 关键词: Intent; Based; Network; Management; Software

The vision of this research is to investigate intent-based policies in wireless sensor networks by leveraging a software defined network framework. The research will focus on 2 domains of research that are complimentary: to develop algorithms to distribute management and control in Software Defined Wireless Sensor Networks (SDWSNs) and reduce the policy gap in network management of SDWSNs by developing semantic policies. The objectives are to reduce both energy consumption and delays in the network in mobile sensing and actuation scenarios such as the monitoring of body sensors, vehicular networks, and cyber-physical systems. These scenarios are examples of the "new" Internet of Things (IoT) services. Perhaps the most revolutionary impact that IoT services will have to sensor networks is to change the way in which these sensor networks collect data switching from a simple sink-based collection topology to a flow-based topology. IoT services are demanding more data from distributed and connected sensors to be available in near real-time. All these services compete for the same resource. An example of this is data from vehicles that can be used to update vehicle maintenance, traffic alternative routes, and long range warning for autonomous vehicles. All these services are requiring the same data but with different priorities and QoS. This can be managed simpler with an SDWSN at a cost of more control packet overhead to the network. In order to keep this overhead down this research will investigate distributed SDN controllers in the wireless sensor network in conjunction to cluster formations of the sensor nodes. In order to maintain the QoS service demands in the sensor network a language for intent-based dynamic policies will be developed. Currently networks are configured using device specific policies and the goal is to configure these using "high-level" human understandable policies such as "requiring the average velocity of vehicles on the 401 near the Oshawa every 5 minutes delivered within 1 minute of acquisition." There is significant gap in how this requirement is specified and programmed into the network and is known as the policy gap challenge. Intents require semantic knowledge that can bridge the gap between terminology used in the requirement and that used to configure the network. It is envisioned that existing semantic representation languages such as OWL and SWRL can be leveraged to create high-level intent policies and bridge this policy gap. Concerning the global market for IoT cyber-physical systems, it is expected to be nearly $136 billion in annual revenues and a $21 billion market in Canada by the end of 2018. Nearly all major telecom and IT companies are shifting their business models and networks to accommodate this service oriented market. Machine to machine communications is expected to have significant impact in the health and manufacturing domains with an estimate of about 75% of IoT services in those 2 domains.

这项研究的愿景是通过利用软件定义的网络框架来调查无线传感器网络中基于意图的策略。该研究将重点关注2个免费的研究领域：开发算法以在软件定义的无线传感器网络（SDWSN）中分发管理和控制，并通过制定语义策略来减少SDWSN网络管理中的政策差距。这些目标是在移动感应和驱动方案中减少网络中的能耗和延迟，例如监视身体传感器，车辆网络和网络物理系统。这些方案是“新”物联网（IoT）服务的示例。物联网服务对传感器网络的最革命性影响也许是改变这些传感器网络从基于简单的水槽的收集拓扑转换为基于流的拓扑的方式。物联网服务要求从分布式和连接的传感器实时提供更多数据。所有这些服务都争夺相同的资源。一个例子是来自车辆的数据，可用于更新车辆维护，替代路线和自动驾驶汽车的远程警告。所有这些服务都需要相同的数据，但是具有不同的优先级和QoS。可以通过SDWSN更简单地将其管理为网络上的更多控制数据包。为了保持开销，这项研究将与传感器节点的群集形成结合使用无线传感器网络中的分布式SDN控制器。 为了维持传感器网络中的QoS服务需求，将制定基于意图的动态策略的语言。目前，使用特定设备的策略配置了网络，目标是使用“高级”人类可理解的政策进行配置，例如“要求在Oshawa附近的401上的平均车辆在被习惯的1分钟内交付每5分钟。”如何指定和编程到网络中存在很大的差距，被称为政策差距挑战。意图需要语义知识，这些知识可以弥合需求中使用的术语与用于配置网络的术语之间的差距。可以预见的是，可以利用猫头鹰和SWRL等现有的语义表示语言来制定高级意图策略并弥合此政策差距。 关于物联网网络物理系统的全球市场，预计到2018年底，它的年收入将近1360亿美元，加拿大的210亿美元市场。几乎所有主要的电信和IT公司都在转移其业务模型和网络以适应这一面向服务的市场。机器到机器通信预计将对健康和制造领域产生重大影响，估计这两个域中约有75％的物联网服务。