This paper introduces two novel techniques, TDMEN-CACHE and TDMEN-INTERSEC, designed to enhance load balancing and energy efficiency in Wireless Multimedia Sensor Networks (WMSNs). These techniques leverage temporal and spatial redundancies to optimize task division among sensor nodes, reducing energy consumption and extending network lifetime. TDMEN-CACHE minimizes reprocessing by utilizing cached data from previous multimedia events, while TDMEN-INTERSEC capitalizes on overlapping sensing areas to distribute processing tasks among neighboring nodes. The proposed methods were evaluated using real-world case studies in precision agriculture and bird monitoring, demonstrating significant improvements in energy efficiency, event detection rates, and network longevity compared to conventional solutions. A packet loss model was also integrated to simulate real-world communication scenarios, further validating the robustness of the techniques under varying conditions.
Multi-core embedded systems face increasing pressure to guarantee timing predictability while sharing critical hardware resources such as caches and memory buses. Although static partitioning hypervisors provide strong spatial isolation, they do not alone prevent temporal interference caused by concurrent memory accesses. To address this gap, this work integrates a fine-grained memory bandwidth regulation mechanism into the Bao hypervisor, extending the MemGuard framework with distinct accounting for read and write operations. Treating memory accesses asymmetrically enables more accurate budgeting and avoids the conservatism inherent in uniform regulation, improving both resource utilization and isolation. The system enforces dedicated bandwidth guarantees and dynamically reallocates unused quotas via feedback-driven budget functions, making it suitable for mixed-criticality environments where high-priority tasks demand strict timing consistency. The implementation is evaluated on a quad-core ARMv8 platform (Raspberry Pi 4) running multiple Bao partitions, each executing periodic real-time tasks alongside memory-intensive workloads. Experiments compare several adaptive budget policies and reveal that separating read and write regulation reduces interference and enhances predictability without sacrificing throughput. Among the evaluated approaches, Adaptive Memory Budget with Penalty (AMBP) demonstrated strong scalability, while Adaptive Feedback Control (AFC) and Proportional-Integral Controller (PIC) achieved an advantageous balance between performance stability and computational overhead. Overall, the integration advances memory isolation capabilities for lightweight hypervisors and provides an efficient foundation for reliable real-time execution on modern embedded multi-core platforms.
In the realm of human–computer interaction, convolutional neural networks (CNN) have recently gained increasing attention for solving hand gesture recognition problems. Numerous existing CNN-based architectures perform well with recognition accuracy but may result in high computational complexity and require excessive resources when deployed on embedded devices. In this paper, an efficient hardware accelerator based on Tiny YOLOv2 networks using binary weights and low-bit activations is proposed to address the above issue. The processing elements (PEs) are designed to leverage low-bit calculations and resource allocation is employed to improve hardware performance. Furthermore, the design space of an accelerator is explored by fine-tuning parameters such as parallelization factors and clock rates. Experiments conducted on the PASCAL VOC and Hindi Indian Sign Language (ISL) datasets demonstrate that the Tiny YOLOv2 model with 1-bit weights and 5-bit activations achieves impressive mean average precision (mAP) scores of 50.2
In developing resource-constrained Internet of Things applications, the focus on design and modeling supports embedded programming. Taking into account the specificities of the application and the limitations of resource-constrained devices in the design and modeling process will streamline the development process. Traditional solutions aimed at resource-constrained devices often focus on programming at the expense of design and modeling, compromising systems with potential flaws and leading to rework. This paper tackles the challenge of developing programs for resource-constrained IoT devices by proposing a statechart-based design and modeling approach. Statecharts offer a powerful and accessible solution for event-driven IoT applications due to their compact visual representation of reactive systems. The intuitive visualization of state transitions and event-driven logic aligns seamlessly with the core event-driven behaviors of IoT devices. Statechart4IoT, as the implementation of this approach, offers a compelling solution for designing, modeling, and pre-implementation analysis of resource-constrained IoT applications. Leveraging the statechart formalism oriented to resource-constrained embedded environments, this framework provides an intuitive web-based environment that enables developers to design and model IoT applications in an interactive visual programming manner. Finally, the framework generates binary-based statechart scripts with small footprints optimized for deployment to resource-constrained devices. To fully realize the capabilities of the proposed approach, a statechart interpreter solution can be implemented as middleware on devices. This will enable the execution of deployed scripts, which ultimately complete the computational behavior of statecharts. This paper evaluates our approach with such a statechart middleware on two distinct operating systems and three hardware platforms. The results obtained indicate the feasibility of the proposed approach.
This study employs physics-informed neural ordinary differential equations to perform time series forecasting for dynamic system monitoring. In this approach, predictions of the system’s dynamic response over time are generated by integrating physical laws with observed data, enhancing forecasting accuracy. The dataset consists of vibration data from radial and tangential accelerometers, recorded over a 5-second interval under various conditions such as faults, imbalances, and misalignments. The results reveal that the fully informed forecasting model consistently produces lower kurtosis values and a more normally distributed error, suggesting superior model generalization over time. Meanwhile, the partially informed model also shows improved performance, with fewer biased predictions than the uninformed model, which exhibits higher kurtosis and a greater number of outliers in its forecasts. Importantly, the study demonstrates that the model can successfully extrapolate system behavior beyond the training period, maintaining high prediction accuracy despite the limited amount of data. Overall, these findings underscore the potential of using Physics-Informed Neural Ordinary Differential Equations for reliable time series forecasting in dynamic system monitoring.
