Digital microfluidic biochips (DMFBs), by precisely controlling and manipulating minute fluids, have realized the integration, automation, and cost-effectiveness of biochemical experiments and are applied in various fields such as medical diagnostics, drug development, and environmental monitoring. However, due to the composition of microelectronic component arrays, DMFBs are prone to electrode faults, leading to erroneous biochemical operations and, consequently, inaccurate experimental results. In this paper, a test path optimization algorithm combining an improved grey wolf algorithm and priority strategy is proposed to solve the problem of an extended test droplet path when DMFB tests faulty electrodes. By encoding the priority of the electrodes of the DMFB and the paths between the electrodes, the test droplet routing is performed according to the priority order. The priority coefficients are dynamically adjusted using the improved grey wolf algorithm to shorten the testing path length. Experimental results demonstrate that the proposed path optimization algorithm reduces the path length by 0.45% to 2.08% compared to the Eulerian circuit method in offline testing, achieving the theoretical optimum value, and by 4.90% to 10.53% compared to the ant colony algorithm in online testing. This provides an essential foundation for accelerating the safe and reliable development of DMFBs in healthcare.
The advent of the Internet has led to a heightened awareness of network security among users. While traditional intrusion detection models exhibit high detection accuracy, they require many labeled samples for model training. Furthermore, the emergence of unsupervised deep learning models has introduced the challenge of threshold selection. A novel flow intrusion detection system has been developed to address the challenges of data labeling and threshold selection. This system employs a Variational Auto-Encoder (VAE) model and a normalized MSE threshold selection method. It has been demonstrated to effectively identify flow intrusions, thereby reducing the burden of data labeling. The experimental results on the NSL-KDD dataset demonstrate that, compared with the traditional unsupervised algorithm, the VAE model exhibits a superior performance in terms of the area under the curve (AUC).
Ethernet Passive Optical Network (EPON) is a high-speed, energy-efficient fiber optic network that has gained attention with the growth of the Industrial Internet. However, existing security protocols for EPON, based on strand space theories, have flaws such as identity inconsistencies and consensus issues. This paper expands the strand space model and authentication testing method, enhances the Fiber Channel Password Authentication Protocol (FACP) strand space model in the EPON network environment using symmetric cryptography and asymmetric cryptography, fulfills the authentication requirements, and obtains the corresponding attack examples. Experimental results demonstrate that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used EPON environments without affecting the identity authentication function.
Edge computing helps to release the tension at the center of IoT systems’ networks, thus reducing the latency, optimizing the bandwidth, and providing new privacy and security solutions, among others. Despite its benefits, edge computing faces unique challenges, including latency, security, and resource constraints. Among these challenges, energy consumption has emerged in the research community, and the global objective is to do "more with less". Researchers explore diverse strategies to enhance sustainability, from hardware optimizations to intelligent algorithms. The quest for energy efficiency and to reduce several impacts aligns with broader efforts to create an environmentally conscious technology landscape.In this paper, we present a task selection model for the edge. We focus on energy consumption and aim to maximize the value given by tasks, all the while minimizing the energy consumed. To do so, we develop a computer environment to simulate outdoor energy-harvesting edge devices, contribute to research reproducibility by recreating a photovoltaic energy harvesting prediction model, and train deep reinforcement learning models to select the best set of tasks at the edge. Our best deep reinforcement learning model, which uses Trust Region Policy Optimization, outperforms our best heuristic and is a robust task selector under varying external conditions.
Energy Harvesting (EH) from Radio Frequency (RF) energy sources has been envisioned as a promising approach to power small sensors, actuators and transceivers in the future Smart City. While several potential energy sources have been identified (mobile phones, dedicated power beacons, cellular base stations) most of them have tight power constraints or are too sparse to provide an adequate energy level. In the next decades, however, with the widespread of connected vehicles, vehicular communications will become a feasible source of RF energy. In this paper, we present the results of an experimental campaign aimed at quantifying the amount of energy that can be actually scavenged by battery-less sensors placed alongside a road from vehicular transmissions. Both a static and a dynamic scenario have been tested, in order to assess the impact of transmit power, vehicle trajectory and speed, as well as the storage capacity. In particular, our findings shed light on the peculiar features of this scenario, which may not be fully leveraged by commonly used harvesting boards tailored to different types of energy sources. An upper bound of the available energy for the dynamic scenario is derived, confirming that up to 0.45 mJ can be stored from the passage of a single vehicle with a proper tuning of the parameters setup.
