Mobile operating systems, such as Android, are increasingly used across diverse applications, where ensuring high responsiveness to user interactions is critical, particularly in mission-critical and real-time scenarios. Mobile operating systems typically process user interaction events and UI rendering on the same thread, commonly referred to as the main thread of a mobile application. As a result, user interaction handling can face significant delays when blocked by overloaded UI rendering tasks, compromising responsiveness. Existing mobile operating systems lack effective mechanisms to mitigate this issue. This paper addresses the problem by restructuring the UI rendering workflow to improve responsiveness in the presence of heavy rendering workloads. Specifically, two techniques are proposed that are tailored to whether the event handling results require screen display. Experimental results demonstrate improvements in both average-case and worst-case response times of event handling, enhancing the UI responsiveness. Although the implementation focuses on Android, the proposed approaches are adaptable to other mobile operating systems with similar rendering architectures, such as iOS and HarmonyOS.

Electrophoretic displays (EPDs), also known as e-paper, offer a paper-like visual experience by reflecting ambient light, making them distinct from traditional LCD or LED displays. They are favored for their eye comfort, energy efficiency, and material flexibility, which make them appealing for a wide range of embedded devices, including eReaders, smartphones, tablets, and wearables. However, EPDs face a significant challenge: the necessity for a fast refresh rate (to maintain an acceptable display performance) introduces a pronounced ghosting effect. This effect results in noticeable color discrepancies between the displayed and source images, harming the user experience and hindering EPDs’ broader application in devices requiring dynamic content display. This article proposes a software-based solution to address the ghosting issue in EPDs. Our approach involves developing analytical models to predict the occurrence of ghosting effects and adjusting the source images to counteract the anticipated color deviations, which can reduce the perceivable ghosts on the display. Experimental evaluation conducted on real-world EPDs validates the effectiveness of our proposed approach in reducing the ghosting effect.

Firmware update is an essential service on Internet-of-Things (IoT) devices to fix vulnerabilities and add new functionalities. Firmware update is energy-consuming since it involves intensive flash erase/write operations. Nowadays, IoT devices are increasingly powered by energy harvesting. As the energy output of the harvesters on IoT devices is typically tiny and unstable, a firmware update will likely experience power failures during its progress and fail to complete. This paper presents an approach to increase the success rate of firmware update on energy-harvesting IoT devices. The main idea is to first conduct a lightweight flash write with reduced erase/write time (and thus less energy consumed) to quickly save the new firmware image to flash memory before a power failure occurs. To ensure a long data retention time, a reinforcement step follows to re-write the new firmware image on the flash with default erase/write configuration when the system is not busy and has free energy. Experiments conducted with different energy scenarios show that our approach can significantly increase the success rate and the efficiency of firmware update on energy-harvesting IoT devices.

It becomes more and more common for people to have multiple mobile devices. This opens the opportunity of multi-surface computing in which users interact with an app using multiple devices simultaneously. Recently, a system called FLUID was developed, which can distribute User Interface (UI) elements of an app to multiple devices to support multi-surface computing. FLUID enables general, flexible and transparent multi-device interaction, which cannot be achieved by previous approaches such as screen mirroring, app migration, and customized app development on multiple devices. However, the practicality of FLUID is still severely limited because it requires that (1) the app source codes must be available and (2) the same app is pre-installed on all devices. This paper presents PRUID, a UI distribution system that is free from the above-mentioned limitations of FLUID. PRUID captures and extracts relevant information about UI elements to be distributed completely at run time, without requiring the app source code. An app-independent UI agent is designed to dock and render the UI components distributed to the guest device, so pre-installation of the app on guest devices is not required. We developed representative use cases to demonstrate the usage and evaluate the performance of PRUID. The evaluation results show that the extra overhead incurred due to the UI information extraction at run time is marginal and PRUID provides a smooth user experience.

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. OpenMP is a promising framework for multi-core realtime embedded systems as well as provides spin locks to protect shared resources. In this paper, we propose a resource model for analyzing OpenMP programs with spin locks. Based on our resource model, we also develop a technique for analyzing the blocking time which impacts the total workload. Notably, the resource model provides detailed resource access behavior of the programs, making our blocking analysis more accurate. Further, we derive the schedulability analysis for real-time OpenMP tasks with spin locks protecting shared resources. Experiments with realistic OpenMP programs are conducted to evaluate the performance of our method.

DAG task model is a general parallel task model that has been widely concerned and studied by researchers. The combination of mixed-criticality and DAG task model makes it difficult to analyze system behaviors. Under federated mixed-criticality scheduling algorithm, tasks are physically isolated with regard to computation resources, which leads to lower analysis complexity and better performance. However, federated mixed-criticality scheduling algorithm suffers resource waste as in federated scheduling, and almost half of processor resources can be wasted in extreme cases. In this paper, we address the problem and propose a novel semi-federated mixed-criticality algorithm (SFMC). SFMC combines semi-federated scheduling with mixed-criticality systems, whose original architecture is changed to a dual-hierarchical one. When analyzing the combined system, we first allocate finer-grained processor resources to each MC DAG task, then we prove the correctness of the SFMC algorithm in both normal and critical states. The proposed algorithm is evaluated on randomly generated independent DAG task sets based on OpenMP benchmarks. Experiment results present that our algorithm has better performance on schedulability than the federated mixed-criticality scheduling algorithm.