Data dissemination in Internet of Things (IoT) networks provides a fundamental service for many critical applications. However, traditional dissemination schemes were investigated for an isolated IoT network, and did not fit the coexistence scenario of heterogeneous IoT networks. Motivated by the latest innovations in cross-technology communication (CTC) techniques, this article proposes CDD, a framework utilizing a WiFi-based access point (AP) to simultaneously conduct fast data diffusion in heterogeneous IoT networks (e.g., WiFi and ZigBee). We shed light on the network features in the coexistence scenario and then review the key challenges of coordinating dissemination based on CTC techniques. With these network features and challenges in mind, the CDD framework includes a series of designs to conduct efficient data dissemination in heterogeneous networks using a WiFi AP. The framework is implemented and evaluated on a real-world testbed. The results demonstrate that CDD can reduce the dissemination delay and significantly outperform the state-of-the-art approaches.

This report presents the Harespod dataset, an open dataset for high altitude hypoxia research, which includes respiration and SpO2 data. The dataset was collected from 15 college students aged 23–31 in a hypobaric oxygen chamber, during simulated altitude changes and induced hypoxia. Real-time physiological data, such as oxygen saturation waveforms, oxygen saturation, respiratory waveforms, heart rate, and pulse rate, were obtained at 100 Hz. Approximately 12 hours of valid data were collected from all participants. Researchers can easily identify the altitude corresponding to physiological signals based on their inherent patterns. Time markers were also recorded during altitude changes to facilitate realistic annotation of physiological signals and analysis of time-difference-of-arrival between various physiological signals for the same altitude change event. In high altitude scenarios, this dataset can be used to enhance the detection of human hypoxia states, predict respiratory waveforms, and develop related hardware devices. It will serve as a valuable and standardized resource for researchers in the field of high altitude hypoxia research, enabling comprehensive analysis and comparison.

Multi-hop over-the-air reprogramming is essential for remote installation of software patches and upgrades in wireless sensor networks (WSNs). Several recent coding-based reprogramming protocols have been proposed to enable efficient code dissemination in high packet loss environments. An accurate and formal analysis of the performance of these protocols, however, has not been studied sufficiently in the literature. In this paper, we present a novel high-fidelity analytical model based on the shortest path algorithm to measure the completion time by incorporating overhearing and packet coding. This model can be applied to any coding-based reprogramming protocol by substituting the coding part with protocol specific operations. We conduct extensive testbed experiments to evaluate the performance of our proposed model. Based on the analytical and numerical experiments, we find that 1) overhearing causes significant reduction of the completion time in dense wireless sensor networks, particularly, it reduces 50-70 percent of the total completion time when the packet reception rate is 0.896; 2) coding delay plays a key role in the total completion time compared to the communication delay when the packet coding parameters are selected appropriately, for example, the communication delay is about 65 percent of the coding delay when the number of packets per page is 16 for the finite field size 2 ⁸ ; 3) the total completion time can be minimized when the number of packets per page is close to 24 and the finite field size is close to 24.

New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent GPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability to the airborne embedded system. To meet timeliness requirements, it is necessary to predict timing behaviors of those applications and design a real-time scheduling policy based on priority and deadline. We therefore propose WAMP $^2$ S, a workload-aware GPU performance model based pseudo-preemptive real-time scheduling algorithm for the airborne embedded system. The workload-aware GPU performance model can accurately predict the execution time of an application, which is running concurrently with other applications on GPU. The pseudo-preemptive real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting GPU computing resources for active applications. Unlike previous work on GPU performance model and GPU real-time scheduling, WAMP $^2$ S considers the impact of co-executing workload on the execution time estimation and provides a software-only approach for preemption support. In addition, WAMP $^2$ S implements a prototype GPU scheduler without any source code analysis. We evaluate the proposed GPU performance model and real-time scheduling algorithm in both simulated and realistic application sets. Experimental results illustrate that WAMP $^2$ S can achieve low prediction error and high scheduling success ratio.