Intermittent systems powered by harvested energy face significant challenges due to limited memory and fluctuating energy availability. These challenges are further amplified in real-time applications such as environmental monitoring, where multiple DNN (Deep Neural Network) tasks must satisfy strict timing guarantees and maintain high inference accuracy. Existing solutions based on lightweight static or dynamic DNNs suffer from the lack of energy adaptability and/or excessive memory overhead, making them unsuitable for real-time intermittent systems. To address these limitations, we propose LEAF, a novel layer-wise energy-adaptive framework for real-time intermittent systems executing multiple DNN tasks. LEAF consists of three key components: (i) a runtime-reconfigurable DNN model and training methodology that supports multiple latency–accuracy trade-offs without incurring additional memory overhead; (ii) an energy-aware time budget abstraction that integrates intermittent inference behavior into existing timing guarantee frameworks; and (iii) a runtime scheduling mechanism that jointly leverages the model and abstraction to ensure both timing guarantees and high inference accuracy. Experimental results demonstrate that LEAF ensures the timely, energy-aware execution of multiple DNN tasks with higher accuracy compared to existing solutions, validating its effectiveness for real-time intermittent systems.

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.

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.