Designing for Reuse in an Industrial Internet of Things Monitoring Application

Abstract The Internet of Things (IoT) continues to experience rapid growth, and its influence is extending into previously unreached domains. However, some of these new domains impose specific limitations that complicate the design and implementation of IoT systems. Examples of such limitations are the exclusion of specific protocols, restrictions on the types of data that can be collected, requirements about what information can be transmitted to the public and controls around how that communication occurs. Capturing, representing and designing for these limitations as well as reuse is essential for the quick and successful deployment of such projects. In this paper, we present a case study of an IoT human in the loop monitoring system built for use within an industrial setting. We report our experiences with both designing the first deployment of the system as well as designing variation points into the software architecture to account for future iterations and deployment into other environments

Implementing stakeholder engagement to explore alternative models of consent: An example from the PREP-IT trials

Introduction: Cluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent. Methods: The PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have. Results: Patient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation. Discussion: Involvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal