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Towards IoT Diversity via Automated Fleet Management

Large-scale Internet of Things (IoT) systems are characterised by an increased level of heterogeneity, both in terms of hardware and software caused by varying device functionality, capabilities and performance. Moreover, since agile business requirements force IoT vendors to continuously modify the software components deployed at the Edge, even initially uniform devices constituting a common IoT ecosystem might end up running software differing in individual components and/or configurations. The amount of effort required to maintain and operate such an increasingly diverse ecosystem – i.e. to perform fleet management – grows proportionally to the size and complexity of the IoT fleet, and is especially important for IoT vendors aiming to achieve economies of scale. To address this challenge, this paper proposes a modelbased diversity engineering approach to enable automated fleet management. Based on a model of an IoT system with finegrained modifications to be applied, the proposed approach is able to diversify and manage large-scale IoT systems at run-time. As a proof of concept, the proposed approach was implemented on top of the Azure IoT Hub fleet management facilities, and validated on a Remote Patient Monitoring use case scenario.