STEER: An Architecture to Support Self-adaptive IoT Networks for Indoor Monitoring Applications

Abstract

IoT infrastructures are becoming increasingly more difficult to manage. One of the main issues is the high volatility present in the infrastruture, which increasingly demands self-adaptive solutions. As a proposal to handle this challenge, this paper presents STEER (Sdn-based inTEnt drivEn iot netwoRks), a new approach for the dynamic adaptation of IoT networks for indoor monitoring applications, based on the unification of Intent-Driven Networks (IDN) and Software-Defined Networks (SDN). Particularly, we explore the ability of IDNs to dynamically interpret an application’s intent, using an IDN-based mediator attached to an SDN-controller to autonomously adapt the IoT network behavior at runtime, thus realizing the intent according to the current operating context of the network. We demonstrate the approach using a representative application scenario related to IoT indoor environment monitoring in the domain of indoor air quality monitoring. The experiments allowed us to validate the applicability of the approach and show the system-wide effect of dynamic adaptation to the current operating environment on improving performance according to the metric under consideration, in this case, the number of application-level messages exchanged in the network.