230 research outputs found
Data-Driven Emulation of Mobile Access Networks
Network monitoring is fundamental to understand network evolution and behavior. However, monitoring studies have the main limitation of running new experiments when the phenomenon under analysis is over e.g., congestion. To overcome this limitation, network emulation is of vital importance for network testing and research experiments either in wired and mobile networks. When it comes to mobile networks, the variety of technical characteristics, coupled with the opaque network configurations, make realistic network emulation a challenging task. In this paper, we address this issue leveraging a large scale dataset composed of 500M network latency measurements in Mobile BroadBand networks. By using this dataset, we create 51 different network latency profiles based on the Mobile BroadBand operator, the radio access technology and signal strength. These profiles are then processed to make them compatible with the tc-netem emulation tool. Finally, we show that, despite the limitation of current tc-netem emulation tool, Generative Adversarial Networks are a promising solution used to create realistic temporal emulation. We believe that this work could be the first step toward a comprehensive data-driven network emulation. For this, we make our profiles and codes available to foster further studies in these directions
Emergency Department Performance Indexes Before and After Establishment of Emergency Medicine
Introduction: Emergency department performance index (EPI) greatly influences the function of other hospital’s units and also patient satisfaction. Recently, the Iranian Ministry of Health has defined specific national EPI containing five indexes. In the present study, the performance indexes of emergency department (ED) in one educational hospital has been assessed before and after establishment of emergency medicine. Methods: In the present cross-sectional study the ED of Shohadaye Tajrish Hospital, Tehran, Iran has been assessed during one-year period from March 2012 to February 2013. The study was divided into two six-month periods, before and after establishment of emergency medicine. Five performance indexes including: the percentage of patients were disposed during 6-hour, leaved the ED in a 12-hour, had unsuccessful cardiopulmonary resuscitations (CPR), discharged against medical advice, and the mean time of triage were calculated using data of department of medical records on daily patients’ files. Then, Mann-Whitney U test was used to make comparisons at P<0.05. Results: The average triage time decreased from 6.04 minutes in the first six months to 1.5 minutes in the second six months (P=0.06). The percentage of patients leaving the ED in a 12-hour decreased from 97.3% to 90.4% (P=0.004). However, the percentage of disposed patients during 6-hour (P=0.2), unsuccessful CPR (P=0.34) and discharged against medical advice (P=0.42) did not differ between the two periods. Conclusion: It seems that establishment of emergency medicine could be able to improve ED performance indexes such as time to triage and leave in a 12-hour period.
Towards ultra-low-cost smartphone microscopy
The outbreak of COVID-19 exposed the inadequacy of our technical tools for
home health surveillance, and recent studies have shown the potential of
smartphones as a universal optical microscopic imaging platform for such
applications. However, most of them use laboratory-grade optomechanical
components and transmitted illuminations to ensure focus tuning capability and
imaging quality, which keeps the cost of the equipment high. Here we propose an
ultra-low-cost solution for smartphone microscopy. To realize focus tunability,
we designed a seesaw-like structure capable of converting large displacements
on one side into small displacements on the other (reduced to ~9.1%), which
leverages the intrinsic flexibility of 3D printing materials. We achieved a
focus-tuning accuracy of ~5 micron, which is 40 times higher than the machining
accuracy of the 3D-printed lens holder itself. For microscopic imaging, we use
an off-the-shelf smartphone camera lens as the objective and the built-in
flashlight as the illumination. To compensate for the resulting image quality
degradation, we developed a learning-based image enhancement method. We use the
CycleGAN architecture to establish the mapping from smartphone microscope
images to benchtop microscope images without pairing. We verified the imaging
performance on different biomedical samples. Except for the smartphone, we kept
the full costs of the device under 4 USD. We think these efforts to lower the
costs of smartphone microscopes will benefit their applications in various
scenarios, such as point-of-care testing, on-site diagnosis, and home health
surveillance
Enhancing Cyber Security of LoRaWAN Gateways under Adversarial Attacks
The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Modern ML applications increasingly rely on complex deep learning models and
large datasets. There has been an exponential growth in the amount of
computation needed to train the largest models. Therefore, to scale computation
and data, these models are inevitably trained in a distributed manner in
clusters of nodes, and their updates are aggregated before being applied to the
model. However, a distributed setup is prone to Byzantine failures of
individual nodes, components, and software. With data augmentation added to
these settings, there is a critical need for robust and efficient aggregation
systems. We define the quality of workers as reconstruction ratios ,
and formulate aggregation as a Maximum Likelihood Estimation procedure using
Beta densities. We show that the Regularized form of log-likelihood wrt
subspace can be approximately solved using iterative least squares solver, and
provide convergence guarantees using recent Convex Optimization landscape
results. Our empirical findings demonstrate that our approach significantly
enhances the robustness of state-of-the-art Byzantine resilient aggregators. We
evaluate our method in a distributed setup with a parameter server, and show
simultaneous improvements in communication efficiency and accuracy across
various tasks. The code is publicly available at
https://github.com/hamidralmasi/FlagAggregato
TSN-FlexTest: Flexible TSN Measurement Testbed (Extended Version)
Robust, reliable, and deterministic networks are essential for a variety of
applications. In order to provide guaranteed communication network services,
Time-Sensitive Networking (TSN) unites a set of standards for
time-synchronization, flow control, enhanced reliability, and management. We
design the TSN-FlexTest testbed with generic commodity hardware and open-source
software components to enable flexible TSN measurements. We have conducted
extensive measurements to validate the TSN-FlexTest testbed and to examine TSN
characteristics. The measurements provide insights into the effects of TSN
configurations, such as increasing the number of synchronization messages for
the Precision Time Protocol, indicating that a measurement accuracy of 15 ns
can be achieved. The TSN measurements included extensive evaluations of the
Time-aware Shaper (TAS) for sets of Tactile Internet (TI) packet traffic
streams. The measurements elucidate the effects of different scheduling and
shaping approaches, while revealing the need for pervasive network control that
synchronizes the sending nodes with the network switches. We present the first
measurements of distributed TAS with synchronized senders on a commodity
hardware testbed, demonstrating the same Quality-of-Service as with dedicated
wires for high-priority TI streams despite a 200% over-saturation cross traffic
load. The testbed is provided as an open-source project to facilitate future
TSN research.Comment: 30 pages, 18 figures, 6 tables, IEEE TNSM, in print, 2024. Shorter
version in print in IEEE Trans. on Network and Service Management (see
related DOI below
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