A Smart Checkpointing Scheme for Improving the Reliability of Clustering Routing Protocols

In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency

Interpreting Deep Learning-Based Networking Systems

While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators. The lack of interpretability makes DL-based networking systems prohibitive to deploy in practice. In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control. Accordingly, Metis introduces two different interpretation methods based on decision tree and hypergraph, where it converts DNN policies to interpretable rule-based controllers and highlight critical components based on analysis over hypergraph. We evaluate Metis over several state-of-the-art DL-based networking systems and show that Metis provides human-readable interpretations while preserving nearly no degradation in performance. We further present four concrete use cases of Metis, showcasing how Metis helps network operators to design, debug, deploy, and ad-hoc adjust DL-based networking systems.Comment: To appear at ACM SIGCOMM 202

Enhancing the Reliability of Head Nodes in Underwater Sensor Networks

Underwater environments are quite different from terrestrial environments in terms of the communication media and operating conditions associated with those environments. In underwater sensor networks, the probability of node failure is high because sensor nodes are deployed in harsher environments than ground-based networks. The sensor nodes are surrounded by salt water and moved around by waves and currents. Many studies have focused on underwater communication environments in an effort to improve the data transmission throughput. In this paper, we present a checkpointing scheme for the head nodes to quickly recover from a head node failure. Experimental results show that the proposed scheme enhances the reliability of the networks and makes them more efficient in terms of energy consumption and the recovery latency compared to the previous scheme without checkpointing

Energy Efficient and Real-Time Remote Sensing in AI-Powered Drone

Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones

Dynamic Computation Offloading Scheme for Drone-Based Surveillance Systems

Recently, various technologies for utilizing unmanned aerial vehicles have been studied. Drones are a kind of unmanned aerial vehicle. Drone-based mobile surveillance systems can be applied for various purposes such as object recognition or object tracking. In this paper, we propose a mobility-aware dynamic computation offloading scheme, which can be used for tracking and recognizing a moving object on the drone. The purpose of the proposed scheme is to reduce the time required for recognizing and tracking a moving target object. Reducing recognition and tracking time is a very important issue because it is a very time critical job. Our dynamic computation offloading scheme considers both the dwell time of the moving target object and the network failure rate to estimate the response time accurately. Based on the simulation results, our dynamic computation offloading scheme can reduce the response time required for tracking the moving target object efficiently

Space-efficient page-level incremental checkpointing

Incremental checkpointing, which is intended to minimize checkpointing overhead, saves only the modified pages of a process. However, the cumulative size of incremental checkpoints increases at a steady rate over time because a number of updated values may be saved for the same page. In this paper, we present a comprehensive overview of Pickpt, a page-level incremental checkpointing facility. Pickpt provides space-efficient techniques aiming to minimizing the use of disk space. For our experiments, the results showed that the use of disk space using Pickpt was significantly reduced, compared with existing incremental checkpointing

Taking Point Decision Mechanism for Page-level Incremental Checkpointing based on Cost Analysis of Process Execution Time *

Incremental checkpointing, which is intended to minimize checkpointing overhead, saves only the modified pages of a process. This means that in incremental checkpointing, the time consumed for checkpointing varies according to the amount of modified pages. Thus, efficient intervals of checkpointing have to be determined on run-time of a process. In this paper, we present an efficient and adaptive page-level incremental check-pointing facility that is based on the taking point decision mechanism for minimizing the total execution time. Our simulation results show that the expected execution time was significantly reduced compared with existing periodic page-level incremental checkpointing

Liquid-cell transmission electron microscopy for tracking self-assembly of nanoparticles

Drying a nanoparticle dispersion is a versatile way to create self-assembled structures of nanoparticles, but the mechanism of this process is not fully understood. We have traced the trajectories of individual nanoparticles using liquid-cell transmission electron microscopy (TEM) to investigate the mechanism of the assembly process. Herein, we present the protocols used for liquid-cell TEM studies of the self-assembly mechanism. First, we introduce the detailed synthetic protocols used to produce uniformly sized platinum and lead selenide nanoparticles. Next, we present the microfabrication processes used to produce liquid cells with silicon nitride or silicon windows and then describe the loading and imaging procedures of the liquid-cell TEM technique. Several notes are included to provide helpful tips for the entire process, including how to manage the fragile cell windows. The individual motions of nanoparticles tracked by liquid-cell TEM revealed that changes in the solvent boundaries caused by evaporation affected the self-assembly process of nanoparticles. The solvent boundaries drove nanoparticles to primarily form amorphous aggregates, followed by flattening of the aggregates to produce a 2-dimensional (2D) self-assembled structure. These behaviors are also observed for different nanoparticle types and different liquid-cell compositions. © 2017 Journal of Visualized Experiments101sciescopu

Expandable ELAST for super-resolution imaging of thick tissue slices using a hydrogel containing charged monomers

Abstract Hydrogels have been utilized extensively as a material for retaining position information in tissue imaging procedures, such as tissue clearing and super-resolution imaging. Immunostaining thick biological tissues, however, poses a bottleneck that restricts sample size. The recently developed technique known as entangled link-augmented stretchable tissue-hydrogel (ELAST) accelerates the immunostaining process by embedding specimens in long-chain polymers and stretching them. A more advanced version of ELAST, magnifiable entangled link-augmented stretchable tissue-hydrogel (mELAST), achieves rapid immunostaining and tissue expansion by embedding specimens in long-chain neutral polymers and subsequently hydrolyzing them. Building on these techniques, we introduce a variant of mELAST called ExELAST. This approach uses charged monomers to stretch and expand tissue slices. Using ExELAST, we first tested two hydrogel compositions that could permit uniform expansion of biological specimens. Then, we apply the tailored hydrogel to the 500-μm-thick mouse brain slices and demonstrated that they can be stained within two days and imaged with a resolution below the diffraction limit of light