Intent-Based Network Management of Software Defined Wireless Sensor Networks

本研究的目标是通过利用软件定义的网络框架来研究无线传感器网络中基于意图的策略。这项研究将集中在两个互补的研究领域：开发软件定义的无线传感器网络(SDWSNs)中分布式管理和控制的算法，并通过开发语义策略来减少SDWSNs网络管理中的策略差距。其目标是减少移动传感和驱动场景中的网络能耗和延迟，例如身体传感器、车辆网络和网络物理系统的监控。这些场景都是“新”物联网(IoT)服务的例子。也许物联网服务将给传感器网络带来的最具革命性的影响是改变传感器网络收集数据的方式，从简单的基于汇聚的收集拓扑转变为基于流的拓扑。物联网服务要求来自分布式和互联传感器的更多数据近乎实时可用。所有这些服务都在争夺相同的资源。例如，来自车辆的数据可用于更新车辆维护、交通替代路线和自动驾驶车辆的远程警告。所有这些服务都需要相同的数据，但优先级和服务质量不同。使用SDWSN可以更简单地管理这一点，代价是网络需要更多的控制数据包开销。为了降低这种开销，本研究将结合传感器节点的簇形成来研究无线传感器网络中的分布式SDN控制器。为了维护传感器网络中的服务质量需求，将开发一种基于意图的动态策略语言。目前，网络是使用特定于设备的策略来配置的，目标是使用人类可以理解的“高级”策略来配置这些策略，例如“要求Oshawa附近的401上的车辆每5分钟在获取后的1分钟内交付的平均速度。”在如何将这一要求指定和编程到网络中方面存在重大差距，这被称为策略差距挑战。意图需要语义知识，能够弥合需求中使用的术语和用于配置网络的术语之间的差距。可以预见，可以利用现有的语义表示语言，例如OWL和SWRL来创建高级意图策略，并弥合该策略差距。关于物联网网络物理系统的全球市场，预计到2018年底，该市场的年收入将接近1360亿美元，加拿大市场将达到210亿美元。几乎所有主要的电信和IT公司都在改变他们的商业模式和网络，以适应这个以服务为导向的市场。机器对机器的通信预计将在健康和制造领域产生重大影响，估计这两个领域的物联网服务约占75%。

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.