Communication protocols in multi-hop radio networks

Ph.D.Mostafa H. Amma

Randomized Multi-Source Broadcast Protocols in Multi-Hop Radio Networks

In [LBA93], a suite of randomized broadcast protocols was presented for the problem of broadcasting a message in arbitrary multi-hop radio networks. These protocols improved upon that of Bar-Yehuda et al. in most of the typical cases. Unfortunately, in those protocols it is assumed that only a single broadcast by a single node is in progress at any point in time. Hence, those protocols are of limited use in practice. In this paper, we present a suite of randomized multi-source broadcast protocols in multi-hop radio networks, in which more than one broadcast can be in progress at any point in time. The protocols are compared with each other in several typical cases

An Improved Leader Election Protocol in Multi-hop Radio Networks

This paper presents two randomized protocols for the problem of electing a "leader" among a set of nodes in an arbitrary multi-hop radio network. First, a variation of the leader election protocol by Bar-Yehuda et al. [BGI87] is presented, in which the termination problem is addressed. Then, an efficient randomized leader election protocol in arbitrary multi-hop radio networks is presented. Our election protocol is shown both analytically and experimentally to be asymptotically more efficient than previously known leaden election protocols for (almost) all networks. Also the protocol is much simpler than previously known leader election protocols

Improved Randomized Broadcast Protocol in Multi-hop Radio Networks

This paper presents a suite of randomized broadcast protocols for the problem of broadcasting a message in multi-hop radio networks. The protocols are compared with the randomized broadcast protocol by Bar-Yehuda et al. The time complexity of one of the randomized broadcast protocols presented in this paper is shown, by simulation, to be much better than those of other protocols in most of the typical cases

Trading Latency for Energy in Wireless Ad Hoc Networks using Message Ferrying

Power management is a critical issue in wireless ad hoc networks where the energy supply is limited. In this paper, we investigate a routing paradigm, Message Ferrying (MF), to save energy while trading off data delivery delay. In MF, special nodes called ferries move around the deployment area to deliver messages for nodes. The reliance on the movement of ferries to deliver data increases the delivery delay. However, nodes can save energy by disabling their radios when ferries are far away. To exploit this feature, we present a power management framework, in which nodes switch their power management modes based on the knowledge of ferry location. We evaluate the performance of our scheme using ns-2 simulations and compare it with Dynamic Source Routing (DSR). Our simulation results show that MF achieves energy savings as high as 95 compared to DSR without power management and still delivers more than 98 of data. In contrast, power-managed DSR delivers much less data than MF to achieve similar energy savings. In the scenario of heavy traffic load, powermanaged DSR delivers less than 20 of data. MF also shows robust performance for highly mobile nodes, while the performance of DSR suffers significantly. Thus, delay tolerant applications should use MF rather than a multihop routing protocol to save energy efficiently when both routing approaches are available.

Automated defect inspection system for metal surfaces based on deep learning and data augmentation

Recent efforts to create a smart factory have inspired research that analyzes process data collected from Internet of Things (IOT) sensors, to predict product quality in real time. This requires an automatic defect inspection system that quantifies product quality data by detecting and classifying defects in real time. In this study, we propose a vision-based defect inspection system to inspect metal surface defects. In recent years, deep convolutional neural networks (DCNNs) have been used in many manufacturing industries and have demonstrated the excellent performance as a defect classification method. A sufficient amount of training data must be acquired, to ensure high performance using a DCNN. However, owing to the nature of the metal manufacturing industry, it is difficult to obtain enough data because some defects occur rarely. Owing to this imbalanced data problem, the generalization performance of the DCNN-based classification algorithm is lowered. In this study, we propose a new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve this problem. The CVAE-based data generation technology generates sufficient defect data to train the classification model. A conditional CVAE (CCVAE) is proposed to generate images for each defect type in a single CVAE model. We also propose a classifier based on a DCNN with high generalization performance using data generated from the CCVAE. In order to verify the performance of the proposed method, we performed experiments using defect images obtained from an actual metal production line. The results showed that the proposed method exhibited an excellent performance. © 2020 The Society of Manufacturing Engineers1

Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface

We present non-invasive means that detect unilateral hand motor brain activity from one individual and subsequently stimulate the somatosensory area of another individual, thus, enabling the remote hemispheric link between each brain hemisphere in humans. Healthy participants were paired as a sender and a receiver. A sender performed a motor imagery task of either right or left hand, and associated changes in the electroencephalogram (EEG) mu rhythm (8–10 Hz) originating from either hemisphere were programmed to move a computer cursor to a target that appeared in either left or right of the computer screen. When the cursor reaches its target, the outcome was transmitted to another computer over the internet, and actuated the focused ultrasound (FUS) devices that selectively and non-invasively stimulated either the right or left hand somatosensory area of the receiver. Small FUS transducers effectively allowed for the independent administration of stimulatory ultrasonic waves to somatosensory areas. The stimulation elicited unilateral tactile sensation of the hand from the receiver, thus establishing the hemispheric brain-to-brain interface (BBI). Although there was a degree of variability in task accuracy, six pairs of volunteers performed the BBI task in high accuracy, transferring approximately eight commands per minute. Linkage between the hemispheric brain activities among individuals suggests the possibility for expansion of the information bandwidth in the context of BBI

Trading Latency for Energy in Wireless Ad Hoc Networks using Message Ferrying

Power management is a critical issue in wireless ad hoc networks where the energy supply is limited. In this paper, we investigate a routing paradigm, Message Ferrying (MF), to save energy while trading off data delivery delay. In MF, special nodes called ferries move around the deployment area to deliver messages for nodes. The reliance on the movement of ferries to deliver data increases the delivery delay. However, nodes can save energy by disabling their radios when ferries are far away. To exploit this feature, we present a power management framework, in which nodes switch their power management modes based on the knowledge of ferry location. We evaluate the performance of our scheme using ns-2 simulations and compare it with Dynamic Source Routing (DSR). Our simulation results show that MF achieves energy savings as high as 95% compared to DSR without power management and still delivers more than 98% of data. In contrast, a power-managed DSR delivers much less data than MF to achieve similar energy savings. In the scenario of heavy traffic load, the power-managed DSR delivers less than 20% of data. MF also shows robust performance for highly mobile nodes, while the performance of DSR suffers significantly. Thus, delay tolerant applications should use MF rather than a multihop routing protocol to save energy efficiently when both routing approaches are available

Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

<div><p>The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust <i>in vivo</i> brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.</p></div