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In mixed criticality systems (MCSs), the time-triggered scheduling approach focuses on a special case of safety-critical embedded applications which run in a time-triggered environment. Sometimes, for these types of MCSs, perfectly periodical (i.e., jitterless) scheduling for certain critical tasks is needed. In this paper, we propose FENP_MC (Fixed Execution Non-Preemptive Mixed Criticality), a real-time, table-driven, non-preemptive scheduling method specifically adapted to mixed criticality systems which guarantees jitterless execution in a mixed criticality time-triggered environment. We also provide a multiprocessor version, namely, P_FENP_MC (Partitioned Fixed Execution Non-Preemptive Mixed Criticality), using a partitioning heuristic. Feasibility tests are proposed for both uniprocessor and homogenous multiprocessor systems. An analysis of the algorithm performance is presented in terms of success ratio and scheduling jitter by comparing it against a time-triggered and an event-driven method in a non-preemptive context.

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The Real-Time Internet of Things is an emerging technology intended to enable real-time information communication and processing over a global network of devices at the edge level. Given the lessons learned from general real-time systems, where the mixed-criticality scheduling concept has proven to be an effective approach for complex applications, this paper formalizes the paradigm of the Mixed-Criticality Internet of Things. In this context, the evolution of real-time scheduling models is presented, reviewing all the key points in their development, together with some connections between different models. Starting from the classical mixed-criticality model, a mathematical formalization of the Mixed-Criticality Internet of Things concept, together with a specifically tailored methodology for scheduling mixed-criticality applications on IoT nodes at the edge level, is presented. Therefore, a novel real-time hardware-aware task model for distributed mixed-criticality systems is proposed. This study also offers a model for setting task parameters based on an IoT node-related affinity score, evaluates the proposed mapping algorithm for task scheduling, and presents some use cases.

Mixed criticality systems are one of the relatively new directions of development for the classical real-time systems. As the real-time embedded systems become more and more complex, incorporating different tasks with different criticality levels, the continuous development of mixed criticality systems is only natural. These systems have practically entered every field where embedded systems are present: avionics, automotive, medical systems, wearable devices, home automation, industry and even the Internet of Things. While scheduling techniques have already been proposed in the literature for different types of mixed criticality systems, the number of papers addressing multiprocessor platforms running in a time-triggered mixed criticality environment is relatively low. These algorithms are easier to certify due to their complete determinism and isolation between components of different criticalities. Our research has centered on the problem of real-time scheduling on multiprocessor platforms for periodic tasks in a time-triggered mixed criticality environment. A partitioned, non-preemptive, table-driven scheduling algorithm was proposed, called Partitioned Time-Triggered Own Criticality Based Priority, based on a uniprocessor mixed criticality method. Furthermore, an analysis of the scheduling algorithm is provided in terms of success ratio by comparing it against an event-driven and a time-triggered method.