Intent-Based Network Management of Software Defined Wireless Sensor Networks

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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.