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.

Internet-of-Things (IoT) devices can only afford very small batteries due to the size, weight, power and cost constraints. On the other hand, long battery life is expected for such devices, either for improving user experience or due to charging limitations. Therefore, it is critical to reduce the energy consumption of IoT devices. A good opportunity is to save energy on the system level, by letting the system to sleep occasionally. In this paper, we introduce Image Partitioning and Incremental Loading (IPIL), an operating-system-level sleep mechanism used in Huawei LiteOS to reduce the energy consumption of an IoT device. IPIL powers off both the CPU and the main memory during sleep to save as much energy as possible. Thus, system data have to be reloaded after the system wakes up, which may take a long time and thus reduce real-time responsiveness. To solve this problem, IPIL partitions the system image into clusters and incrementally loads the clusters on-demand, reducing the amount of data loading and time overhead in the wake-up step. Experimental results show that IPIL outperforms two widely adopted sleep approaches in terms of energy consumption. At the same time, IPIL offers satisfactory responsiveness with very short wake-up delay. We also present a case study on smart watch to demonstrate how battery life, as a core value of wearable devices, can be improved with IPIL provided by LiteOS.

We designed a crossdevice testing and reporting system, called cross-device testing and reporting system (CD-TRS), to facilitate the functional validation of protocol and application design in large-scale RTWNs. CD-TRS leverages the nice property of RTWNs that all devices in the network are fully synchronized. By specifying and retrieving events and device status from multiple devices in the runtime simultaneously, CD-TRS can further assemble them into a network-wide report on the detailed system behavior by aligning the records according to their associated network timestamps (or absolute slot number (ASN) in most RTWNs). By comparing this runtime network behavior report with the required protocol and application specifications, abnormal device/network behavior can be observed and their root cause(s) can be effectively located. This thus can significantly reduce the complexity of RTWN testing and reporting. In the following, we first describe the overall architecture of CD-TRS, and then demonstrate how CD-TRS helps with the functional validation of D2-PaS, a distributed and dynamic packet scheduling framework we recently developed for handling disturbances in real-time wireless networks.

Real-time wireless networks (RTWNs) are fundamental to many Internet-of-Things (IoT) applications. Packet scheduling in an RTWN plays a critical role for achieving desired performance but is a challenging problem especially when the RTWN is large and subject to unexpected disturbances from the environment. Few solutions exist to tackle this challenge but they suffer serious limitations. This paper introduces a novel distributed dynamic packet scheduling framework, D2-PaS. D2-PaS aims to minimize the number of dropped packets while ensuring that all critical events due to disturbances are handled by their deadlines. D2-PaS builds on a number of observations that help reduce the scheduling overhead, and thus is efficient and scalable. Besides extensive simulation, D2-PaS has been implemented on an RTWN testbed to validate its applicability on real hardware. Both testbed measurements and simulation results confirm the effectiveness of D2-PaS. Compared to the best known work, D2-PaS reduces packet drop rates by 65% and 90% on average and in the best case, respectively, and also achieves 100% success for all the randomly generated task sets.

Real-time task graphs are used to describe complex real-time systems with non-cyclic timing behaviors. The workload of such systems are typically bursty, which may degrade their schedulability even with sufficient resource in the long term. In this paper, we propose to use task graph transformation to improve system schedulability. The idea is to insert artificial delays to the release times of certain vertices of a task graph to get a new graph with a smoother workload, while still meeting the timing constraints of the original task graph. Delaying the release time of a vertex may smoothen the workload of some paths of the task graph, but at the same time make the workload of other paths even more bursty. We developed efficient techniques to search for an appropriate release time delay for each vertex. Experiments with randomly generated task systems show that the proposed transformation method can make a significant number of task systems that was originally unschedulable to become schedulable, and the transformation procedure is very efficient and can easily handle large-scale task graph systems in very short computation time.

The analysis of global multiprocessor scheduling is more difficult than its uniprocessor counterpart. Due to the unknown critical instant, existing techniques use over-approximations of task interference for efficient yet pessimistic analysis. In this paper, we proposed a new technique to improve the precision of interference estimation. The key is to identify and resolve contradicting assumptions made in the analysis procedure. The resulting new analysis method improves the analysis precision at the price of a higher complexity. Then we introduce techniques to optimize the new method for better efficiency. Experiments with randomly generated task sets are conducted to evaluate both the precision and efficiency of the proposed new method.

Scheduling mixed-criticality systems that integrate multiple functionalities with different criticality levels into a shared platform appears to be a challenging problem, even on single-processor platforms. Multi-core processors are more and more widely used in embedded systems, which provide great computing capacities for such mixed-criticality systems. In this paper, we propose a partitioned scheduling algorithm MPVD to extend the state-of-the-art single-processor mixed-criticality scheduling algorithm EY to multiprocessor platforms. The key idea of MPVD is to evenly allocate tasks with different criticality levels to different processors, in order to better explore the asymmetry between different criticality levels and improve the system schedulability. Then we propose two enhancements to further improve the schedulability of MPVD. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over existing algorithms.

The response time analysis problem is intractable for most existing real-time task models, except the simplest ones. Exact solutions for this problem in general have exponential complexity, and may run into scalability problems for large-scale task systems. In this paper, we study approximate analysis for static-priority scheduling of the Digraph Real-Time task model, which is a generalization of most existing graph-based real-time task models. We present two approximate analysis methods RBF and IBF, both of which have pseudo-polynomial complexity. We quantitatively evaluate their analysis precision using the metric speedup factor. We prove that RBF has a speedup factor of 2, and this is tight even for dual-task systems. The speedup factor of IBF is an increasing function with respect to k, the number of interfering tasks. This function converges to 2 as k approaches infinity and equals 1 when k = 1, implying that the IBF analysis is exact for dual-task systems. We also conduct simulation experiments to evaluate the precision and efficiency of RBF and IBF with randomly generated task sets. Results show that the proposed approximate analysis methods have very high efficiency with low precision loss.

Scheduling mixed-criticality systems is a challenging problem. Recently a number of new techniques are developed to schedule such systems, among which an approach called OCBP has shown interesting properties and drawn considerable attentions. OCBP explores the job-level priority order in a very flexible manner to drastically improve the system schedulability. However, the job priority exploration in OCBP involves nontrivial overheads. In this work, we propose a new algorithm LPA (Lazy Priority Adjustment) based on the OCBP approach, which improves the state-of-the-art OCBP-based scheduling algorithm PLRS in both schedulability and run-time efficiency. Firstly, while the time-complexity of PLRS' online priority management is quadratic, our new algorithm LPA has linear time-complexity at run-time. Secondly, we present an approach to calculate tighter upper bounds of the busy period size, and thereby can greatly reduce the run-time space requirement. Thirdly, the tighter busy period size bounds also improve the schedulability in terms of acceptance ratio. Experiments with synthetic workloads show improvements of LPA in all the above three aspects.