Consensus algorithms are widely adopted in clustered systems to distribute data efficiently, with Raft being a prominent failover algorithm due to its effectiveness and fault tolerance. However, Raft and other consensus algorithms do not provide timing guarantees, limiting their application to time-critical systems such as industrial control systems, drone swarms, and autonomous vehicle networks, where it is critical to distribute state machines in time and prevent multiple timing misses of data. In this paper, we propose CARTEL (Consensus Adapting Real-Time and Efficient Logging), a novel consensus algorithm that integrates time-predictability into the Raft framework. To achieve this aim, we examine Raft's mechanisms and identify the characteristics that impact the timing of data propagation within a distributed system. Based on these insights, we design CARTEL by developing two mechanisms that address the primary limitations of Raft: (i) CARTEL voting to solve the uncertainty of leader election timing, and (ii) CARTEL node buffer to limit the number of indeterminately deferred data during leader failure. Moreover, we propose how to utilize the mechanisms of CARTEL to ensure time-predictability in leader elections and mitigate the indeterminate nature of data logging during leader transitions, without harming the integrity of Raft. We validate the effectiveness of CARTEL through real-world implementation. The experiments confirm that CARTEL not only reduces the uncertainty of system recovery inherent in the election process (by 65.7% compared to Raft) but also enhances the integrity of the distributed system by guaranteeing timely data logging.

Machine learning (ML) is increasingly being integrated into real-time embedded systems, enabling intelligent decision-making in applications such as autonomous driving and industrial automation. However, ensuring predictable execution of deep neural network (DNN) inference remains a major challenge, as real-time systems must meet strict timing constraints to guarantee safety and reliability. This paper identifies key challenges in achieving real-time AI inference in embedded systems, including limited memory capacity, high energy consumption, efficient multi-DNN scheduling, and heterogeneous resource management. To address these challenges, we emphasize the need for advanced scheduling algorithms to efficiently allocate heterogeneous computing resources across multiple DNNs, hierarchical memory management to reduce memory bottlenecks, and real-time neural architecture search and optimization techniques to enhance AI model performance under strict timing constraints. Furthermore, we discuss future research directions aimed at improving real-time AI execution, including time-predictable scheduling frameworks to ensure consistent inference latency, cross-device AI workload management to optimize resource utilization across heterogeneous processors, and benchmarking methodologies to systematically evaluate performance, timing guarantees, and energy efficiency in real-time AI systems. Advancing these research areas will enhance the reliability, efficiency, and scalability of AI-driven embedded systems, bridging the gap between ML advancements and real-time system requirements.

Edge devices for robotics in hazardous environments, such as rescue drones, navigate complex terrains while transmitting images to remote servers for anomaly detection, including wildfires. However, these devices operate under strict resource constraints, prioritizing operational-critical tasks (e.g., autonomous navigation) while handling image-processing workloads with minimal overhead. Offloading computation to a remote server can alleviate this burden, but unstable network conditions can degrade accuracy and timeliness. To address these challenges, this paper presents a novel offloading framework that balances computational efficiency and accuracy in image-processing tasks. Specifically, it ensures (R1) a minimum accuracy level for individual image-processing tasks associated with different camera sensors and (R2) maximizes the overall image-processing accuracy across all sensors. Our approach builds on an edge-server collaborative image reconstruction architecture, where images are divided into patches and selectively reconstructed. To achieve R1 and R2, we introduce: (i) a hierarchical scheduler that effectively prioritizes patch transmissions under resource constraints and (ii) a feedback mechanism that adapts to network instability, ensuring reliable offloading and inference. Experimental results demonstrate that our framework maintains high accuracy and timely processing, even under network failures.

Machine learning methods have been used to solve real-time scheduling problems but none has yet made an architecture that utilizes influences between real-time tasks as input features. This letter proposes a novel approach to partitioned scheduling in real-time systems using graph machine learning. We present a graph representation of real-time task sets that enable graph machine-learning schemes to capture the influence between real-time tasks. By using a graph attention network (GAT) with this method, our model successfully partitioned-schedule task sets that were previously deemed unschedulable by state-of-the-art partitioned scheduling algorithms. The GAT is used to establish relationships between nodes in the graph, which represent real-time tasks, and to learn how these relationships affect the schedulability of the system.

Targeting a multiobject tracking (MOT) system with multiple MOT tasks, this article develops Batch-MOT, the first system design that achieves both (G1) timing guarantee and (G2) accuracy maximization, by utilizing batch execution that allows multiple deep neural network (DNN) executions to perform simultaneously in a single DNN inference resulting in significantly decreased execution time without accuracy loss. To this end, we propose an adaptable scheduling framework that allows run-time execution behaviors deviated from our base scheduling algorithm (i.e., nonpreemptive fixed-priority scheduling) without compromising G1. Based on the adaptable framework, we then develop 1) a run-time batching mechanism that finds and executes a batch set of MOT tasks and 2) a run-time idling mechanism that waits for the future releases of MOT tasks for batch execution. Both run-time mechanisms can achieve G1 and G2 without incurring high run-time overhead, as they systematically exploit the run-time execution behaviors allowed by the adaptive framework. Our evaluation conducted with a real-world data set demonstrates the effectiveness of Batch-MOT in improving tracking accuracy while providing a timing guarantee compared to the state-of-the-art real-time MOT system for multiple MOT tasks.

As IoT technology develops, the demand for using robots in safety- and mission-critical environments has gradually emerged. In such scenario, it is important to ensure that specific tasks are completed within their deadlines. ROS2, a representative operating system for robots, has advantages in convenience of development and hardware abstraction but has a drawback in fully guaranteeing real-time capabilities. This drawback can cause significant problems when numerous high-critical tasks are present. We aim to address these issues through a mixed-critical system for ROS2. To this end, we propose the algorithm for our framework and demonstrate it through experiments using a simulator that can verify our approach. As a result of the experiment, our framework always showed a critical high callback deadline satisfaction rate that was better than EDF.

Although blockchain technology is being increasingly utilized across various fields, the challenge of providing timing guarantees for transactions remains unmet, which is an obstacle in implementing blockchain solutions for time-sensitive applications such as high-frequency trading and real-time payments. In this paper, we propose the first solution to achieve a timing guarantee on blockchain. To this end, we raise and address two issues for timely transactions on a blockchain: (a) architectural support, and (b) real-time scheduling principles specialized for blockchain. For (a), we modify an existing blockchain network, offering an interface to preferentially select the transactions with the earliest deadlines. We then extend the blockchain network to provide the flexibility of the number of generated blocks at a single block time. Under such architectural supports, we achieve (b) with three steps. First, to resolve a discrepancy between a periodic request of a transaction-generating node and the corresponding arrival on a block-generating node, we translate the former into the latter, which eases the modeling of the transaction load imposed on the blockchain network. Second, we derive a schedulability condition of the modeled transaction load, which guarantees no missed deadline for all transactions under a work-conserving deadline-based scheduling policy. Last, we develop a lazy scheduling policy and its condition, which reduces the number of generated blocks without compromising the degree of timing guarantees for the work-conserving policy. By implementing RT-blockchain on top of an existing open-source blockchain project, we demonstrate the effectiveness of the proposed scheduling principles with architectural supports in not only ensuring timely transactions but also reducing the number of generating blocks.

Amongst densely populated areas such as New York, Hong Kong, Seoul and etc., apartments and condominiums grew to be the most commonly used type of housing units. Throughout the years we have witnessed many issues within the residents living in such units suffer from apartment noise. Lousy neighbors are not the only cause of such problems but the way houses were built, more specifically type and thickness of the flooring materials, determines the amount of noise transfer from one household to the other. In this paper, we show feasibility of classifying flooring materials using COTS (Commercial Off-The-Shelf) mobile devices. We exert each flooring materials propagating synthetic chirps in various ways. Our approach makes it possible to distinguish three types of target flooring materials (wood, polystyrene and concrete) and can also give similar results when the target materials are piled on top of one another. To this end we i) design an acoustic signal which effectively differentiates each target flooring materials, ii) gather sound samples propagated from the target materials, and iii) provide a classification methodology using methods such as SVM and KNN. Finally, we discuss the necessities to achieve the final goal of identifying flooring materials and their thickness.